Cardiac arrest is a complex and lethal condition that poses a substantial public health burden, with high nationwide mortality rates and the potential for profound and irreversible neurologic injury and functional disability. In addition to the number of lives lost, cardiac arrest has a considerable economic impact; measured in terms of productive years of life lost due to premature death or avoidable neurologic disability, it constitutes a societal burden equal to or greater than that of other leading causes of death in the nation (Stecker et al., 2014). Numerous factors can affect reported incidence and outcomes of out-of-hospital cardiac arrest (OHCA) and in-hospital cardiac arrest (IHCA). Understanding how to best measure and influence these factors is important to inform policy decisions that can advance the resuscitation field and improve patient outcomes following cardiac arrest through appropriate resource allocation and evidence-based service provision. This requires reliable and valid data.
Current limitations make accurate measurement of cardiac arrest incidence and outcomes challenging. A paucity of evidence is available about important non-mortality-related quality measures such as neurologic outcomes, functional status, and the long-term survival potential of cardiac arrest survivors, making it difficult to measure the burden of neurologic injury that can result from cardiac arrest. Long-standing efforts to improve nationwide survival rates and patient outcomes have resulted in limited success, although surveillance systems that combine data collection with some element of continuous quality improvement have demonstrated the ability to improve cardiac arrest outcomes in a number of communities, as described in Chapter 6.
This chapter provides an overview of the current understanding of the public health burden of OHCA and IHCA in the United States. As part of its information-gathering process, the committee commissioned data analyses from the two largest OHCA registries in the United States, the Cardiac Arrest Registry to Enhance Survival (CARES) and the Resuscitation Outcomes Consortium (ROC) Epistry, as well as the Get With The Guidelines-Resuscitation (GWTG-R) registry for IHCA (see Appendix D for selected results). This chapter describes epidemiological trends in incidence and outcomes, and discusses relevant predictors of outcomes, based on the commissioned analyses and a review of published literature. It provides an overview of the existing surveillance efforts, describes primary registries, and assesses their strengths and limitations. After discussing challenges associated with measuring the burden of cardiac arrest, this chapter makes a case for a national surveillance system for cardiac arrest, highlighting some of the shortcomings of existing systems and presenting options for overcoming these barriers.
CARDIAC ARREST INCIDENCE AND OUTCOMES IN THE UNITED STATES
Recent estimates suggest that approximately 395,000 cases of OHCA occur in the United States every year among patients of all ages, in which only 5.5 percent of all patients survive to hospital discharge (Daya et al., 2015a).1 An estimated 200,000 IHCAs of presumed cardiac origin also occur annually, with national survival rates of approximately 24 percent (Chan, 2015; Go et al., 2014; Merchant et al., 2011). Incidence also varies between adults and children. The analysis of the ROC Epistry indicates that pediatric cardiac arrests are less common and make up only about 2 to 3 percent of all OHCAs in the country (Daya et al., 2015a; Vellano et al., 2015). As this section explains, however, there is tremendous variability in these estimates resulting from different definitions of cardiac arrest used within the resuscitation field. Therefore, in the absence of a national surveillance system to capture the true incidence of cardiac arrest, any statistics should be interpreted with some degree of uncertainty, as described throughout this chapter.
1The 2013 incidence statistic includes patients of all ages and cardiac arrest events with both cardiac and noncardiac (e.g., trauma, drowning, and poisoning) etiologies. This figure is an approximation based on analysis of data from the Resuscitation Outcomes Consortium Epistry, the limitations of which are described in this chapter.
Out-of-Hospital Cardiac Arrest
Overall OHCA incidence in the United States varies widely in the literature, primarily because of differences in the underlying sources of data and populations included, and in the types of cardiac arrest incidence being described (Chugh et al., 2004; Kong et al., 2011; Zheng et al., 2001). CARES, designed to help emergency medical services (EMS) agencies evaluate and improve their performance in cardiac arrest response, monitors and reports data on patients who received EMS treatment (CPR and/or defibrillation) (CDC, 2015b). The ROC Epistry is a clinical trials research database that captures incidence of both treated and untreated cases of OHCA. As a result, total incidence reported by each database ranges from approximately 180,000 to 395,000 cases (Daya et al., 2015a; Vellano et al., 2015). Table 2-1 presents additional points of comparison for reported incidence rates based on CARES and ROC data. Still other studies identify cardiac arrest using other inclusion and exclusion criteria. For example, some studies report all-cause cardiac arrest while others only report cardiac arrests that are of presumed cardiac etiology only (excluding arrests due to traumatic causes such as drowning, poisoning, or drug overdose).
According to a 2010 systematic review of 79 studies, the overall survival rates for adults in the United States have remained stable at 7.6 percent for nearly 30 years (Sasson et al., 2010). In the committee’s commissioned analyses of 2013 data from CARES and the ROC Epistry, however, survival rates for EMS-treated patients who experienced an OHCA of presumed cardiac etiology is approximately 11 percent for adults (Daya et al., 2015a; Vellano et al., 2015) and 7.9 percent for children (Daya et al., 2015a). The committee’s commissioned analyses also found incremental increases in cardiac arrest survival rates over time (Chan, 2015; Daya et al., 2015a,b). This increase could be due to multiple factors, including a Hawthorne effect in communities that participate in registries and therefore regularly monitor and improve the quality of care (Kellum et al., 2006).
Wide regional variation in both incidence and survival across the United States has been documented, suggesting opportunities to enhance quality of care and improve outcomes. One study by Nichol and colleagues (2008) found that survival from ventricular fibrillation (VF)related OHCA ranged from 7.7 to 39.9 percent across 10 North American sites. Another publication that compared cardiac arrest outcomes across
TABLE 2-1 Types of Reported OHCA Incidence Among Adults in 2013
|Database/Patient Population||Incidence Rate per 100,000 Person-Years||Total Incidence per Yeara|
EMS “treated, all cause”
EMS “treated, all cause”
+ “untreated, all cause”b
aSee commissioned reports for calculation of total annual incidence (column 3).
bUntreated refers to cases that did not receive resuscitation treatment because patients were either dead upon EMS arrival or had existing do-not-resuscitate orders.
NOTE: The ROC Epistry incidence includes all cardiac arrests (with cardiac and noncardiac etiology), whereas the CARES incidence counts include cardiac arrest of presumed cardiac origin only.
SOURCES: Daya et al., 2015a; Vellano et al., 2015.
35 communities in the United States found survival rates ranged from less than 3.3 percent in Chicago to 40.5 percent in Rochester, Minnesota (Rea et al., 2004). Observed geographic variation in incidence and survival between and within different communities may reflect important demographic risk factors for cardiac arrest (Carr et al., 2009; Nichol et al., 2008a). EMS- and hospital-system-level factors also contribute to variation. For example, differences in the proportion of patients treated or transported by EMS, as well as differences in prehospital drug therapies provided to patients, account for some of the variation (Glover et al., 2012; Zive et al., 2011).
Incidence is defined as the number of new cases of a given disease or condition (e.g., cardiac arrest) in any given population during a specified period of time. Different studies use different definitions (i.e., numerators and denominators) to identify OHCA incidence, which results in discrepancies in the calculation of the disease burden (Berdowski et al., 2010; Kong et al., 2011). The numerator may include, for example, all patients who die suddenly, all cases with presumed cardiac etiology, all cases where EMS responded, cases where EMS responded and resuscitation treatment was provided, or all cases of EMS-treated patients with ventricular fibrillation. Possible denominators may include total population in a neighborhood, state, or country or all adults in a given geographic area. Many registries in the United States also differ in how they collect and report certain cardiac arrest variables. For example,
researchers may pull data from EMS or hospital medical records, whereas others data may be from death certificates. Furthermore, studies may use different metrics to assess specific outcomes (e.g., Glasgow Coma Scale, Cerebral Performance Category [CPC] score, and Modified Rankin Score [mRS] for neurologic status).
The State of OHCA in the United States in Comparison to Trends in Europe and Asia
Understanding the population health burden of cardiac arrest in the United States, in comparison to that in similarly developed nations in Europe and Asia, allows researchers to identify potential strategies for improvement. A large meta-analysis of 67 studies comparing the global burden of cardiac arrest found statistically significant differences in incidence of treated OHCA (Berdowski et al., 2010). This study found that North America had the highest all-rhythm OHCA incidence (54.6) per 100,000 person-years when compared to Asia (28.3), Europe (35), and Australia (44) and that rates of VF-related OHCA and the survival-to-discharge rates, respectively, for those patients varied across continents: Asia (11 percent VF-related OHCA and 2 percent survival-to-discharge rate), North America (28 percent and 6 percent), and Europe (35 percent and 9 percent) (Berdowski et al., 2010). As expected, there is also considerable variability among individual countries in each continent. The aggregate statistics in the meta-analysis should be interpreted with a degree of uncertainty, because the heterogeneity of data-collection methods and definitions in the source studies makes comparisons difficult. Consistent with recent temporal trends in U.S. registries (Chan, 2015; Daya et al., 2015b), survival rates in many European countries have also improved in recent years. For example, there was a substantial increase in nationwide 30-day and 1-year OHCA survival rates reported by the Danish Cardiac Arrest Registry between 2001 and 2010 (Wissenberg et al., 2013). The same study reports finding improvements in the rate of return of spontaneous circulation (ROSC) for OHCA in Denmark over a period of 5 years, with an increase from 21 to 61 percent. Some studies have also found decreasing incidence of VF-related cardiac arrest over time, possibly as a reflection of improving population health (Cobb et al., 2002). A recently released Australian study reported a decrease in the adjusted OHCA incidence rate from 75.7 to 65.9 per 100,000 person-years between 1997 and 2002 (Bray et al., 2014). However, the rates of survival to discharge in this study did not change over time.
In-Hospital Cardiac Arrest
Fewer studies and data sources are available to measure IHCA incidence and outcomes compared to OHCA. One of the more recent studies estimates that approximately 200,000 annual IHCA cases, ranging between 192,000 and 211,000 cases,2 occur among adults in U.S. hospitals (Merchant et al., 2011). A 2013 American Heart Association (AHA) consensus report extrapolated incidence rates and hospital admissions data from separate studies and determined that approximately 6,000 cases of IHCA occur annually among pediatric populations (Chan et al., 2010; Morrison et al., 2013; Nadkarni et al., 2006). Like OHCA, the literature on IHCA offers a large range of data points for incidence because of differences in collected metrics, study populations, and variations in the models used to extrapolate from study cohorts to the larger population. There is also substantial variability in IHCA survival rates in the United States (Merchant et al., 2014). For instance, pediatric survival rates can range from 27 to 48.7 percent (Lopez-Herce et al., 2013; Meert et al., 2009; Nadkarni et al., 2006). However, a large majority (59 to 75 percent of adults and 53 to 63 percent of children) of IHCA survivors leave the hospital with good neurologic outcomes (Nadkarni et al., 2006).
In the absence of a specific International Classification of Diseases (ICD) diagnostic code differentiating IHCA from OHCA, discussed later in this chapter, researchers have historically used multiple approaches to define and identify IHCA incidence.3 Similar to OHCA, published literature uses various proxy measures for the numerator (number of times CPR or defibrillation was initiated for patients, number of activations of an in-hospital resuscitation team, etc.). Defined denominator populations
2The reported statistics are based on a 2011 analysis, which used the most recent available data (years 2003-2007) from the Get With The Guidelines-Resuscitation registry. The study used three separate approaches to calculate the estimated range of annual IHCA events in the United States.
3Multiple diagnosis codes in the ICD-10 system can be used as a proxy measure for cardiac arrest and can lead to inaccurate and discretionary coding depending on the provider. For example, although the code for “cardiac arrest” is 427.5, a number of other ICD codes could be entered as a proxy diagnosis by the provider—including ventricular fibrillation (427.41), ventricular flutter (427.42), atrial fibrillation (427.31), and other irregular cardiac rhythms. The ICD code for cardiac arrest has been further expanded in the 10th revision and now differentiates between cardiac and noncardiac etiology of arrest (Moczygemba and Fenton, 2012).
also differ; while some studies may include all patients who experience a cardiac arrest after being admitted to a hospital, other studies may exclude patients who experience an arrest in the emergency department or in outpatient clinics or patients who have existing do-not-attempt-resuscitation (DNAR) orders. The 2013 consensus statement from the AHA proposed a standard definition for IHCA incidence, but this has not yet been uniformly applied in current research (Morrison et al., 2013).
As for OHCA, IHCA outcomes are slowly improving over time. The committee’s commissioned analysis of GWTG-R data found a steady increase in survival rates for patients with both shockable and nonshockable rhythm between 2000 (16.2 percent) to 2013 (24.4 percent) (Chan, 2015). Another study documented a decrease in overall rates of neurologic damage over 10 years, because the proportion of patients who experienced poor neurologic outcomes (clinically significant neurologic disability defined as CPC score greater than 1) dropped from 32.9 to 28.1 percent (Girotra et al., 2012). These improvements may be due to changes in the hospital environment and/or the patient population. For example, Girotra and colleagues (2014) also found a significant decrease in age, prevalence of heart failure and myocardial infarction, and cardiac arrests due to shockable rhythms, among IHCA patients, all of which are known to influence outcomes following cardiac arrest. Improvements in IHCA outcomes and best practices depend on having an accurate and representative data to measure IHCA incidence in the United States. This will require a reliable system of data collection, analysis, and reporting for use by health care systems.
FACTORS INFLUENCING INCIDENCE AND SURVIVAL
Survival and neurologic recovery following cardiac arrest are influenced by multiple interdependent factors that include individual patient characteristics, EMS or hospital system characteristics, and circumstantial factors specific to the event. Risk factors also vary in the extent to which they affect incidence and outcomes of OHCA and IHCA. According to a systematic review of OHCA literature, key predictors of survival may include whether the arrest was witnessed by a bystander or EMS, availability of bystander CPR, and having a shockable, initial cardiac rhythm (e.g., ventricular fibrillation) (Sasson et al., 2010). This review also reported an association between a patient experiencing prehospital ROSC in the field and their likelihood of survival. More likely,
the stronger predictor of neurologically favorable survival is not the location of ROSC but rather the length of time between collapse to ROSC, which would presumably be shorter for OHCA when ROSC occurs in the field versus after arrival at a hospital (Komatsu et al., 2013; Nagao et al., 2013).
Some risk factors that affect patient outcomes are modifiable and include the elements related to health care delivery and system performance. Modifiable factors, within the context of the EMS system, include factors such as whether the 911 call center dispatcher recognizes the presence of cardiac arrest and provides dispatcher-assisted CPR instructions over the telephone, EMS response times, the time interval between collapse to treatment, and quality of resuscitation care (Bobrow et al., 2014; McNally et al., 2009). Within hospitals, quality of response to IHCAs (e.g., time to defibrillation and CPR duration, activation of code teams, etc.) and availability of post-resuscitative care (e.g., availability and access to therapeutic hypothermia) affects patient outcomes. Additionally, medical leadership, ongoing quality improvement, training, cultures of excellence, and mechanisms for accountability also influence care provision. Studying the process and performance of systems of care can serve as equally important tools in understanding the state of cardiac arrest care in the nation. EMS and health care system responses to cardiac arrest are discussed in Chapters 4 and 5, respectively.
Other risk factors, such as individual patient characteristics (e.g., age, race, and ethnicity) or circumstances of an arrest (e.g., location or cardiac etiology) are nonmodifiable but can be used to help identify areas of need and target resources or specific interventions. Studies on individual patient characteristics have highlighted differences in incidence and survival across demographic factors that persist after adjusting for potential confounders, such as initial cardiac rhythm, hospital and health care system factors, and severity of illness (Moon et al., 2014; Noheria et al., 2013; Safdar et al., 2014; Shippee et al., 2011). These disparities can result from complex interactions between socioeconomic variables, health status, comorbidities, and genetics (Friedlander et al., 1998; Nehme et al., 2014a). Circumstantial factors, such as the initial cardiac rhythm and whether the arrest was witnessed and location of arrest (e.g., public versus private setting and community versus hospital settings), are associated with different patient outcomes (McNally et al., 2009, 2011; Nichol et al., 2008a). Factors associated with the public response to cardiac arrest, including provision of CPR and defibrillation, are discussed in Chapter 3.
Although successful efforts to improve cardiac arrest survival often target modifiable factors (e.g., increasing access to, or availability of, appropriate treatments and reducing EMS response time), improving research on nonmodifiable factors (e.g., patient age or race) can also influence outcomes by helping to identify high-risk populations and disparities in care. Furthermore, data and research findings on nonmodifiable factors can be used to better tailor public health interventions and allocate resources to meet identified needs and gaps throughout the health care response. The next section focuses on describing trends in cardiac arrest incidence and outcomes, depending on location of arrest, as well as patient demographic factors such as age, gender, and race and ethnicity.
Location of Arrest
Survival rates greatly depend on where the cardiac arrest occurs. This may be, in part, due to the fact that witnessed arrests tend to have shorter collapse-to-treatment times, and, as noted throughout the report, every minute counts when it comes to responding to a cardiac arrest. Thus, survival rates for IHCA are comparatively higher than for OHCA. Within hospitals, patients who arrest in nonmonitored units and the intensive care unit (ICU) are often the least likely to survive to discharge (Chan, 2015). While the nonmonitored unit results are to be expected because of a lack of possible witnesses, factors such as severity of illness could explain the ICU findings. It should be noted that location of arrest typically has a more direct impact on outcomes for OHCA patients than it does for IHCA patients.
Public Versus Private Settings
Location of arrest is an important determinant of OHCA survival with good neurologic outcomes. The committee’s commissioned analysis of 2013 CARES data determined that a majority of OHCAs (70.1 percent) occur in private homes, where fewer than 1 in 10 patients survive to discharge (see Table 2-2). International studies have similar findings. In Japan, two-thirds of OHCAs occur in private homes (Moriwaki et al., 2014). However, better survival and favorable neurologic outcomes (defined as CPC 1 or 2) are reported among cases that occur in public places when compared with those that occur in private homes. In New Zealand,
TABLE 2-2 The Influence of OHCA Location on Survival
|Location of Arrest||Proportion of Total OHCAs (%)||Survival Rate (%)|
|Nursing home/assisted living facility||10.4||4.8|
|Health care facility||4.6||16.0|
|Place of recreation||1.7||29.5|
|Public transportation center||0.3||42.1|
SOURCE: Vellano et al., 2015.
survival from arrests that occurred in public was nearly twice as common as arrests in residential locations (Fake et al., 2013). One study found that pediatric OHCAs are more likely to occur in private settings, with 88 percent of cases reported in a residential location (Brown, 2005; De Maio et al., 2004).
The lower survival rates associated with private, residential settings may be due to the relative absence of witnesses (Weisfeldt et al., 2011). Arrests that occur in public places benefit from the improved chances of a witnessed arrest and, subsequently, higher rates of CPR and earlier defibrillation by EMS or bystanders. Patients are more likely to have shockable rhythm upon EMS arrival when the time interval between the initial collapse and 911 call is shorter (McNally et al., 2011). These supportive circumstances, in turn, result in higher rates of favorable neurologic status and 30-day survival (Mitani et al., 2014; Moriwaki et al., 2014; Murakami et al., 2014). Potential confounding factors such as availability of bystander CPR and initial cardiac rhythm (also known as first recorded rhythm), rather than location of arrest, can also more directly influence outcomes. Additional data are needed to assess the relative impact of these predictors on survival.
The type of public location in which an OHCA occurs also affects survival (Brooks et al., 2013). Murakami and colleagues (2014) reported higher rates of favorable neurologic outcomes for individuals in Japan
who experienced a cardiac arrest in schools (41.9 percent) and sports facilities (51.6 percent) as compared to railway stations (28.0 percent) and public buildings (23.3 percent). There is also variation in the types of public spaces where OHCA is likely to occur. For example, 10.4 percent of OHCAs take place in nursing homes and assisted living facilities, where survival—at 4.8 percent—is even less common (Vellano et al., 2015; see Table 2-2). A demonstrated lack of overlap between public spaces where automated external defibrillators (AEDs) are placed (e.g., community pools, schools and educational facilities, and public buildings) and areas where OHCAs are most likely to occur (e.g., private homes, skilled nursing facilities, and assisted living facilities) may contribute to poor survival rates reported in some public areas (Levy et al., 2013). Differences in patient health status across settings (e.g., patients in nursing homes may have severe comorbidities compared to patients in schools), as well as availability of bystander CPR (e.g., health care facility versus public highway), may additionally account for differential survival rates between locations.
Rural Versus Urban Settings
Multiple cardiac arrest studies confirm that OHCAs that occur in rural areas are associated with relatively worse outcomes, which is likely due to a number of structural barriers. Because the majority of health care resources are located in urban centers, cardiac arrests that take place in rural areas are often subject to longer EMS response times and transport intervals (Jennings et al., 2006; Stromsoe et al., 2011). Moreover, the lower population density in rural areas decreases the likelihood of witnessed arrest, bystander CPR, and access to nearby defibrillators (Ro et al., 2013; Stromsoe et al., 2011). For example, a study of OHCA in Australia found that higher rates of ROSC, survival to hospital admission, and survival to hospital discharge were associated with more densely populated areas. The study concluded that people who experience a cardiac arrest in very-high-density areas (more than 3,000 persons/km2) were 4.32 times more likely to survive to discharge than those who arrest in very-low-density areas (≤ 10 people/km2) (Nehme et al., 2014b).
Age is an important predictor of cardiac arrest incidence and survival with positive neurologic outcomes. The committee’s commissioned
analysis of CARES data (see Table 2-3) indicates that OHCA incidence in the United States is highest among adults aged 35 years and older, with a steep age-related gradient extending from 35 to 65 years.
Infants and Children
Infants and children constitute a particularly vulnerable segment of the cardiac arrest patient population. The committee’s commissioned analyses of CARES and ROC data found an incidence of 2 to 3 percent in patients younger than 18 years of age (Daya et al., 2015a; Vellano et al., 2015). Despite this comparatively small population, the public health burden of pediatric OHCA remains high. This is due to the greater number of lost years of productivity per individual; the survival rate in children (8 percent), compared to that for adults (11 percent), is low (Daya et al., 2015a).
The estimated incidence and survival rates of pediatric cardiac arrest vary widely across studies, because of differences in definitions of variables and methods of calculation. For example, inclusion of sudden infant death syndrome–related cases or other nontraumatic cases of noncardiac origin can inflate the incidence and falsely decrease the survival rates (Atkins et al., 2009). Reported OHCA incidence rates for infants (≤ 1 year old) range from 11.5 in Denmark to 72 per 100,000 person-years in the United States (Atkins and Berger, 2012; Rajan et al., 2014). Survival rates tend to be lower for infants (3.3 percent) than for children (9.1 percent) and adolescents (8.9 percent) (Atkins and Berger, 2012).
Survival rates from pediatric cardiac arrests are comparable, or higher in European countries. In the Netherlands, among resuscitated pediatric patients (ages below 21), the survival rate was 24 percent, with the majority of survivors reporting good neurologic outcomes (Bardai et al., 2011). A study of pediatric IHCA in Spain demonstrated improvements in survival over the past 10 years (41 percent up from 25.9 percent) (Lopez-Herce et al., 2014). By comparison, the survival rate for pediatric patients in South Korea was determined to be substantially lower (Ahn et al., 2010). Compared to the data presented from European registries, incidence and survival were, respectively, higher and lower for infants as compared to children and adolescents (Park et al., 2010). As with adult cases of OHCA, survival rates are increased for children when initial rhythm is shockable, arrests are witnessed, and bystanders provide CPR (Meyer et al., 2012; Rajan et al., 2014). Despite this fact, AEDs are less
TABLE 2-3 Correlation Among Age, Incidence of OHCA, and Survival
|Age (years)||Proportion of Total OHCAs (%)||Survival Rate (%)|
SOURCE: Vellano et al., 2015.
commonly used in children between the ages of 1 and 8 years old than in adults (Johnson et al., 2014).
The committee’s commissioned analyses confirmed that elderly patients, those who are older than 70, were more likely to have lower short- and long-term survival rates than younger adults (Chan, 2015; Daya et al., 2015a; Vellano et al., 2015). One study found that among OHCA patients older than 75 years old, only one-quarter survived with favorable neurologic outcomes (Grimaldi et al., 2014). Less than half of this initial group of survivors survived another 6 years post-discharge. Death was nearly 3.5 times more likely for elderly cardiac arrest survivors when compared to elderly individuals in the reference population (Grimaldi et al., 2014). Similarly, among IHCA survivors over the age of 65, approximately 65 percent survived to 6 months post discharge (Chan, 2015). However, only 33 percent survived to the 5-years post-discharge mark. More than half of all IHCA survivors in this age group had moderate to severe neurologic impairment at hospital discharge.
In the same cohort of patients, long-term survival was highest among those who initially had little to no neurologic damage following the cardiac arrest, who had a shockable initial cardiac rhythm, and were discharged home (compared to those who were discharged to hospice care or rehabilitation facilities) (Chan, 2015). For example, 72.8 percent of patients with mild or nonexistent neurocognitive deficits at discharge survived 1 year beyond the cardiac arrest, compared to 42.2 percent of those with a severe disability and only 10.2 percent of those in comas
or vegetative states following the cardiac arrest. There were no significant differences in hospital readmission rates between age groups, but minority populations, women, and profoundly disabled patients were all more likely to be readmitted (Chan et al., 2013). Studies demonstrating correlation between age and long-term survival following cardiac arrest should be interpreted with a degree of caution and take into account potential confounding factors, such as the higher prevalence of DNAR orders among older patients, which may account for inflated mortality rates (Seder et al., 2014).
Women, compared to men, are less likely to experience and survive a cardiac arrest. The committee’s commissioned analyses of CARES and ROC data found that approximately three in five cardiac arrests occurred in men. While the survival rate following OHCA is higher among men, survival rates following IHCA appear to be similar in both groups (see Table 2-4).
Many studies corroborate the findings from these registries (Girotra, 2012; Safdar et al., 2014). However, the nature of gender’s effect on cardiac arrest survival is more contentious. Depending on the statistical adjustments made and the type of survival measured, women may be reported to be less, or equally, likely to survive cardiac arrest. Mixed results regarding the correlation between gender and survival to admission, discharge, and 30 days post discharge have been reported in many studies (Hasan, 2014; Mahapatra, 2005). Survival with positive neurologic outcomes is also more common in women, potentially because of the
TABLE 2-4 Distribution of OHCA and IHCA Incidence and Survival by Gender, 2013
|Incidence (%)||Survival (%)||Incidence (%)||Survival (%)|
NOTE: CARES = Cardiac Arrest Registry to Enhance Survival; GWTG-R = Get With The Guidelines-Resuscitation; ROC = Resuscitation Outcomes Consortium.
SOURCES: Chan, 2015; Daya et al., 2015a; Vellano et al., 2015.
neuroprotective effects of estrogen and progesterone (Akahane, 2011; Hasan et al., 2014; Kim et al., 2014; Topjian et al., 2010).
The correlation between gender and prehospital factors affecting survival is not clearly understood. Cardiac arrest risk factors such as severe comorbidity, chronic obstructive pulmonary disease, cancer, and nonshockable cardiac rhythm are more common in women (Safdar et al., 2014; Simmons et al., 2012; Wissenberg et al., 2014). Other risk factors, such as structural heart disease, are less common in women than in men (Simmons et al., 2012). Coronary artery disease (CAD) is present in four out of five men who experience cardiac arrest, but occurs in less than half of women who have a cardiac arrest (Simmons et al., 2012). Because implantable cardioverter defibrillators were designed to prevent recurrent cardiac arrest in patients with existing CAD and other forms of structural heart disease, women are less likely than men to benefit from this therapy (Chugh et al., 2009).
Access to available cardiac arrest treatments is also associated with gender. One study found that women are less likely to receive CPR, defibrillations, or advanced cardiac life support treatments from EMS personnel following a cardiac arrest (Ahn et al., 2012; Safdar et al., 2014). However, survival for men and women is equal when similar resuscitation treatments are provided (Israelsson et al., 2014). The comparatively low rate of cardiac arrest incidence among women may cause health care professionals and the general public to be less prepared to respond to women who experience a cardiac arrest.
Race and Ethnicity
Multiple studies of both OHCA and IHCA have noted disparities in cardiac arrest incidence and survival by race and ethnicity (Groeneveld et al., 2003; Moon et al., 2014; Shippee et al., 2011). The committee’s commissioned analyses of data from CARES and the GWTG-R registry also indicated higher survival-to-discharge rates among white patients when compared with patients of other races and ethnicities (Chan, 2015; Vellano et al., 2015).
Studies of OHCA conducted in Chicago and New York found that white patients were twice as likely to survive to hospital discharge as African American patients (Becker et al., 1993; Galea et al., 2007). Galea and colleagues reported that whereas the incidence of OHCA is higher among African American and Hispanic patients (10.1 per 10,000 and 6.5 per 10,000, respectively) compared with white patients (5.8 per 10,000),
age-adjusted 30-day survival after hospital discharge is much lower (Galea et al., 2007). An important contributor to this lower rate of survival is the lower frequency of a shockable initial cardiac rhythm among blacks and Hispanics compared to whites (Galea et al., 2007; Vadebonceour et al., 2008). Delays related to public recognition of cardiac arrest, limited use of the 911 emergency system, and lower rates of bystander CPR in African American and Hispanic neighborhoods are potential contributors to the lower rates of shockable rhythms and survival (McNally et al., 2011; Moon et al., 2014; Sasson et al., 2014; Skolarus et al., 2013; Watts et al., 2011). Sasson and colleagues determined that the median income and racial composition of a neighborhood both predict the likelihood of receiving bystander CPR; in particular barriers associated with the cost of CPR training and a lack of information contribute to lower CPR rates in minority communities (Sasson et al., 2012, 2013). Socioeconomic variables such as family income, education, health insurance status, and language barrier are also widely known to influence patient outcomes (Reinier, 2011; Sasson et al., 2012). However, additional research is needed to parse out the mechanism by which these factors interact with other individual patient, clinical, and health system determinants of cardiac arrest survival.
Death rates following an IHCA are significantly higher for African Americans than for individuals of other races and ethnicities. Among the 49,130 IHCA patients included in the GWTG-R registry, African Americans had a higher mortality rate than white patients (60.9 percent versus 53.4 percent) (Larkin et al., 2010). Analysis of multiyear Medicare data (years 2000-2004) for 433,985 patients who received in-hospital CPR found that the adjusted odds of survival for African American patients were 23.6 percent lower than those for white patients (Ehlenbach et al., 2009). This trend continued in 2013 when the survival-to-discharge rates remained higher in white IHCA patients compared to African American IHCA patients (25.9 percent versus 20.8 percent, respectively) (Chan, 2015). Notable disparities also have been observed in long-term survival after IHCA. One-year readmission rates are higher, and long-term survival rates are lower among African American IHCA patients.
The maldistribution of risk factors for cardiac arrest and patient health factors in vulnerable populations, such as the number of preexisting conditions and illness severity, plays a crucial role in observed cardiac arrest outcome disparities. For example, a lower proportion of shockable cardiac rhythms has been identified among African American and Hispanic IHCA patients (12 percent and 15.1 percent, respectively)
compared to white patients (16.7 percent) (Galea et al., 2007). In an analysis of IHCA patients in VF or pulseless ventricular tachycardia (pVT), African American patients compared to white patients were more likely to have delayed defibrillation (22 percent and 17 percent, respectively) and less likely to survive to discharge (25.2 percent versus 37.4 percent, respectively) (Chan et al., 2009). A similar trend has been observed for Hispanic patients, although due to small sample sizes in the GWTG-R registry the differences did not achieve statistical significance. These outcome disparities remain even after adjusting for temporal trends, patient characteristics, hospital, and arrest characteristics. Although health factors contribute to variation, the inequitable access to critical pre-arrest, preventative care is also an urgent consideration when discussing disparities in hospital cardiac arrest (Shippee et al., 2011).
Moreover, the fact that the differences in outcomes described above cannot be entirely explained by health characteristics raises concerns about potential disparities in health care system factors. Differences in access to post-arrest care, such as variation in rates of cardiac catheterization and implantation of a cardioverter defibrillator during the first hospitalization, access to follow-up outpatient care, differences in discharge destination (e.g., hospice versus home), or other practice patterns, can account for potential disparities in neurologically favorable survival following cardiac arrest. As noted throughout the report, the utility of existing cardiac arrest registries is limited because of missing data on race and ethnicity and socioeconomic factors. More research is needed to determine the precise influence of these factors on cardiac arrest survival and neurologic outcomes. A better understanding of the interaction of these factors is necessary in order to eliminate these disparities and ensure that every patient receives the care she or he needs regardless of age, gender, or race and ethnicity. Box 2-1 summarizes the key conclusions regarding disparities in cardiac arrest outcomes that require further research.
ECONOMIC BURDEN OF POST-ARREST CARE
The societal impact of cardiac arrest, in terms of both the years of productive life lost due to death and disability and the economic burden of caring for cardiac arrest patients who are resuscitated and arrive at the hospital alive, is substantial. In one study, the average cost of direct care
Disparities in Cardiac Arrest Outcomes
- Compared to white patients who experience both OHCA and IHCA, African American and Hispanic populations have higher incidence and lower survival-to-hospital discharge rates.
- Compared to white patients who experience both OHCA and IHCA, racial and ethnic minorities have a higher burden of residual neurologic deficits.
- Racial and ethnic minorities have lower access to appropriate cardiac arrest treatments and therapies.
- Evidence of significant health care disparities exists in sudden cardiac arrest and merit further research and new processes of care.
to an OHCA patient with ROSC (including cost of conventional care and continued care in rehabilitation) was estimated to be $102,0174 per person (Merchant et al., 2009). Another study estimated that the total aggregate cost of OHCA in the United States was $33 billion each year (Kida et al., 2014). Many OHCA survivors are able to return to normal functional status; approximately 40 percent of patients who present an initial cardiac rhythm of asystole and 31 percent of patients with pulseless electrical activity have poor neurologic outcomes, ranging from severe dysfunction (CPC 3) to coma (CPC 4) (Vellano et al., 2015). By contrast, 90 percent of patients with VF or pVT as the initial cardiac rhythm who survive have favorable neurologic outcomes, defined as a CPC score of 1 or 2 (Vellano et al., 2015). Costs of post-arrest care for individuals following hospital discharge can be approximately $42,000 for 30 days of rehabilitation and approximately $100,000 for 365 days of long-term facility care5 (Merchant et al., 2009).
IHCA patients discharged following cardiac arrest have frequent rehospitalization (35 readmissions per 100,000 patients) and incur additional costs of care (Chan et al., 2014). Each patient utilized resources averaging $19,000 during the first year following an arrest. The published literature on total national expenditure due to cardiac arrest is limited, although there are studies that have assessed the cost-effectiveness of
4Merchant and colleagues (2009) reported a total cost of $10,201,716 for a hypothetical cohort of 100 patients. The IOM report extrapolated the per-person cost.
5The statistics on 30-day and 365-day cost were calculated based on data presented by Merchant and colleagues (2009). The study reported rehabilitation cost of $1,390 per day and long-term nursing home care cost of $250 per day.
specific treatments (e.g., therapeutic hypothermia and percutaneous coronary intervention) and strategies (e.g., training bystanders, EMS, and public-access defibrillation programs). However, based on available data, it is conceivable that these costs could be reduced dramatically if patient outcomes could be improved, through better response systems and postresuscitation care and discovery of more effective treatments.
CARDIAC ARREST SURVEILLANCE
Determining the magnitude of the public health burden of cardiac arrest is vital for improving patient outcomes in all communities. As discussed previously, incidence and survival are influenced by a number of modifiable (e.g., health care service characteristics) and nonmodifiable (e.g., patient demographics or location of arrest) factors. Evidence indicates that creating a consistent and reliable cardiac arrest surveillance system that routinely monitors OHCA and IHCA, and allows for the precise measurement of the mortality and morbidity burden of cardiac arrest and its associated predictors, can improve outcomes in a number of ways (McNally et al., 2009; Nichol et al., 2008a,b). It can better guide the selection and implementation of public health interventions, help determine appropriate allocation of resources in any given community (e.g., placement of AEDs), identify at-risk or vulnerable populations, and eliminate potential care disparities through targeted interventions. Moreover, it can allow researchers to assess the impact of current and emerging treatments and provide an evidence base for high-quality care and best practices.
In spite of these advantages, the United States does not currently maintain a single comprehensive surveillance system or registry that captures all cases of cardiac arrest in the nation. There are multiple registries that monitor and report data on a subset of OHCA or IHCA populations from select communities in which EMS agencies and hospitals have voluntarily agreed to participate. This section first examines the strengths and limitations of existing registries in the United States, presents an overview of international and multinational databases, and then explores short- and long-term strategies for improving the surveillance system by creating a national registry for cardiac arrest.
Primary Cardiac Arrest Databases in the United States
In the United States, two primary OHCA registries (CARES and the ROC Epistry) and one primary IHCA registry (GWTG-R) have produced multiple publications based on data collected over many years. Data are collected from multiple sources, including patient medical records from EMS and hospital systems, as well as death certificates. There are also some additional local and regional databases, examples of which are presented in Box 2-2. New databases (such as the Dynamic AED Registry and the National Institutes of Health’s [NIH’s] Pediatric Cardiac Arrest database) are also being developed to supplement existing cardiac arrest surveillance efforts. The OHCA and IHCA registries described below were designed to serve different purposes and therefore have varying strengths and limitations.
Examples of Additional Cardiac Arrest Registries in the United States
HeartMap Dynamic AED Registry: Aims to assess the safety of AEDs in public locations through post-market surveillance. This is important in identifying whether any of these devices have contributed to adverse outcomes in OHCA fatalities (Nichol, 2014). AEDs are tracked using crowdsourcing methods, two-dimensional matrix codes, and data on process and outcomes from EMS agencies participating in the ROC Epistry. This registry is funded by the U.S. Food and Drug Administration, as well as all monitor-defibrillator manufacturers in the United States, including Cardiac Science Corp., Heartsine Technologies Inc., Philips Health Care Inc., Physio-Control Inc., and ZOLL Medical Corp. Before the implementation of this AED registry, there was no widely deployed method of tracking their location and use in community settings (Merchant and Asch, 2012).
International Cardiac Arrest Registry (INTCAR): In 2009, the American Neurocritical Care Society joined the European Cardiac Arrest Research Network, and the Hypothermia Network, a registry based primarily in northern Europe (containing the largest number of therapeutic hypothermia-treated cardiac arrest survivors) to form this multinational registry for post arrest care (INTCAR, 2012). The organization is governed by both American and European steering committees, and participating hospitals are from both regions (73 in Europe/Asia and 10 in North and South America). Its purpose is to understand the process and outcomes associated with OHCA, and it is now the largest registry of post-resuscitation cardiac arrest care.
Milwaukee: Since its inception in 1976, the Milwaukee County EMS System has continuously maintained a computerized database of all EMS-treated patients. Information on OHCA patients includes comprehensive EMS care and survival through hospital discharge (MCDHHS, 2014). The database provides the basis for cardiac arrest continuous quality improvement programs and implementation of best clinical practice, and it is publicly available throughout the community.
National Emergency Medical Systems Information System (NEMSIS): A voluntary registry designed to capture every EMS event in the country. According to a 2011 report, NEMSIS is able to monitor cardiac arrest EMS response in 18 states (FICEMS, 2011). Unlike CARES, NEMSIS collects data from participating EMS agencies only and therefore does not include patient outcomes and discharge data from hospitals. It is funded by the National Highway Traffic Safety Administration (NHTSA), Health Resources and Services Administration, and CDC. Its primary goals are to implement an electronic documentation system in every local- and state-level registry, as well as to create a national EMS database that allows stakeholders to assess performance and benchmark. Currently, greater than 90 percent of the states and territories have a NEMSIS-compliant data system in place (NEMSIS, 2013). However, levels of sophistication vary.
Save Hearts in Arizona Registry and Education: Collects and analyzes data from Arizona cases of adult and pediatric OHCA in which resuscitation was initiated (Vadeboncoeur et al., 2007). Information about bystander use of AEDs is also captured. Data are collected from EMS agencies that respond to OHCA and the hospitals receiving the patients. Reports with feedback on patient survival are provided to participating EMS agencies.
Seattle: Since 1970, the Seattle Fire Department has maintained a registry of OHCAs for which EMS responded. Survivors are followed through hospitalization and annually thereafter. The purpose of this registry is to assess and improve quality of care delivered, and to create a system of best practices. It also enhances community accountability, because the data are made part of the public record after review by government officials (Neumar et al., 2011).
University of Pennsylvania: The Penn Alliance for Therapeutic Hypothermia was created in 2000 as a voluntary national registry of OHCA and IHCA, for patients who received therapeutic hypothermia after initial resuscitation. The registry also allows individual institutions to evaluate its performance and benchmark against other similar institutions (Grossestreuer et al., 2011).
OHCA: The Cardiac Arrest Registry to Enhance Survival (CARES)
In 2004, the Centers for Disease Control and Prevention (CDC) established CARES in collaboration with the AHA and the Department of Emergency Medicine at the Emory University School of Medicine to help communities determine standard outcome measures for OHCA and to allow EMS systems to assess their performance and benchmark at local, regional, and national levels. Since 2012, CARES has been funded by the AHA, American Red Cross, Medtronic Foundation HeartRescue Project, and Zoll Corporation (Vellano et al., 2015).
CARES collects information on nontraumatic OHCA cases of presumed cardiac etiology by linking three sources of data across the prehospital care continuum: 911 call centers, EMS systems, and receiving hospitals. The registry only includes cases of “treated” arrests (patients received attempted CPR and/or defibrillation by EMS or bystanders); cases in which resuscitation was not attempted because of existing patient DNAR orders or patients were dead upon EMS arrival are excluded (McNally et al., 2009). The CARES data set includes a minimal number of mandatory data elements for each OHCA event and its outcome. EMS data include patient demographics, arrest-specific data (e.g., location of arrest), and resuscitation-specific data (e.g., bystander CPR or AED, ROSC achieved, etc.); supplemental information from 911 centers includes time variables (e.g., time of initial call and response times), and hospital data include patient outcomes (e.g., emergency department outcome, hypothermia use, and neurologic outcome at discharge). See the CARES group commissioned report for the complete list of CARES data elements (Vellano et al., 2015).
Providers can submit data by entering them manually through the CARES website or automatically through an EMS agency’s electronic platform. The CARES system also automatically contacts receiving hospitals with a request for patient outcomes data (e.g., survival to discharge and neurologic status at discharge) whenever a new case is entered into the system by paramedics (McNally et al., 2009). The software automates data analysis, allowing participating EMS agencies to access their own data, generate reports by date range, and to benchmark their performance against a summary national report. Hospitals also have access to a facility-specific report, allowing users to view prehospital and in-hospital characteristics of their patient populations with benchmarking capability.
CARES collects OHCA-related data from communities in 35 states, including 12 state-based registries (Alaska, Delaware, Hawaii, Idaho, Illinois, Michigan, Minnesota, North Carolina, Oregon, Pennsylvania,
Utah, and Washington), representing approximately 80 million people, or 25 percent of the U.S. population (Vellano et al., 2015). The population catchment within the states highlighted in the CARES map (see Appendix D) ranges from 50 to 100 percent. The platform is now used internationally, through collaboration with the Pan-Asian Resuscitation Outcomes Study (PAROS), which includes nine countries (Australia, Japan, Malaysia, Singapore, South Korea, Taiwan, Thailand, Turkey, and the United Arab Emirates) (Ong et al., 2011).
CARES participation is voluntary, and EMS agencies and hospitals that contribute information are not compensated. Therefore, participating sites must be willing to invest the time and resources necessary for data entry, progress review and evaluation, and implementation of changes based on feedback. CARES outcome data are limited by potential selection bias, because higher-performing EMS systems may be more likely to voluntarily report outcomes.
OHCA: Resuscitation Outcomes Consortium (ROC) Epistry
ROC, a national network of research institutions, was established in 2004 to conduct randomized clinical trials that evaluate promising treatments and therapies for patients with OHCA and life-threatening trauma. It is a collaboration of 10 regional sites in the United States and Canada managed through a single data coordinating center. ROC is funded by the National Heart, Lung, and Blood Institute (NHLBI) in partnership with the U.S. Army Medical Research and Materiel Command, the Canadian Institutes of Health Research’s Institute of Circulatory and Respiratory Health, Defence Research and Development Canada, the Heart and Stroke Foundation of Canada, and the AHA (Daya et al., 2015a). There are 120 EMS agencies enrolled in the ROC Epistry (67 from the United States and 53 from Canada), representing a population of 18 million. Case identification occurs at each ROC site through manual sorting of EMS records or automated capture from electronic records.
In order to facilitate the planning and conduct of clinical trials and to assess trends, investigators developed the ROC Epistry in 2005, a population-based prospective registry capturing incidence of all OHCA cases for which an EMS response is requested. Unlike CARES, this includes both EMS “treated” and “untreated” cardiac arrest cases. Patients who received chest compressions by EMS personnel, or any external defibrillation by either EMS personnel or lay responders, are categorized as “treated” (Morrison et al., 2008). Minimal data are also recorded for pa-
tients who did not receive any resuscitation treatment (due to DNAR order or death upon EMS arrival). An interdisciplinary ROC committee developed the original data set by using existing EMS reporting structures, OHCA templates, and mandatory and optional variables (Daya et al., 2015a). Relative to CARES, the annual cost of maintaining the ROC Epistry is considerably greater, but it contains more granular information on EMS process and outcomes per OHCA case allowing for more complex analysis. However, the research findings based on this data set may not be representative of other nonparticipating EMS and hospital systems; analyses of ROC sites should consider the potential for selection bias, because initial enrollment in ROC occurred through a competitive process, with only the most successful programs meeting participation criteria (Daya et al., 2015a; Nichol et al., 2008b). See the ROC group commissioned report for the complete list of data elements (Daya et al., 2015a).
The NIH recently announced plans for establishing a cross-collaborative clinical trials network for emergency care, titled Strategies to Innovate EmeRgeNcy Care Clinical Coordinating Center (SIREN).6 The new network will continue the work of ROC and the NIH-supported Neurological Emergencies Treatment Trials, with the goal of designing clinical trials for patients with cardiac, neurologic, pulmonary, hematologic, and traumatic medical and surgical emergencies. These multidisciplinary research networks have the potential to improve research on cardiac arrest resuscitation and post-arrest care, by lowering the overall cost of conducting clinical trials research and creating a richer data source for assessing the complex treatments needed to improve rates of neurologically intact survival following cardiac arrest. Box 2-2 provides examples of additional national- and regional-level databases for OHCA.
IHCA: Get With The Guidelines-Resuscitation (GWTG-R) Registry
The AHA’s GWTG-R registry, formerly known as the National Registry for Cardiopulmonary Resuscitation (NRCPR), was launched in 2000 to help hospitals assess their performance against national bench-
6Personal communication with J. Brown, NIH, May 26, 2015. SIREN is supported by the National Institute for Neurological Disorders and Stroke (NINDS), the National Heart, Lung, and Blood Institute (NHLBI), the National Center for Advancing Translational Sciences (NCATS), the Office of Emergency Care Research (OECR) in the National Institute of General Medical Sciences (NIGMS), and the Defense Medical Research and Development Program for Combat Casualty Care (DMRDP) (NIH, 2015).
marks, track patient outcomes, and improve the quality of in-hospital resuscitation care, with the overall goal of translating guidelines into clinical practice. The prospective registry includes cardiac arrest patients of all ages, and it reports a comprehensive range of data elements that cover patient demographics, in-hospital resuscitation and IHCA or OHCA hospital-based post-arrest care event data (e.g., hypothermia use, door-to-cath lab times, etc.), as well as facility-specific data (e.g., teaching status, bed size, and geographic region) (Chan, 2015).
GWTG-R provides unique resources for participating hospitals. In addition to Web-based patient management, clinical decision support, and related educational tools, it allows individual providers and hospitals to compare their own performance against other hospitals in real time (Ellrodt et al., 2013). Hospitals are rewarded with public recognition for improvement and for meeting defined goals and benchmarks. The information collected in the database is also used to inform the development and update of resuscitation care guidelines.
Although the GWTG-R registry is the largest IHCA registry in the United States, only 317 hospitals of the more than 6,000 nationwide are currently enrolled in the program (see Appendix D; Ellrodt et al., 2013). As shown in the GWTG-R map in Appendix D, hospitals are primarily located in urban and suburban sites, are not spread evenly across the United States, and do not completely represent the demographics of the country (Nichol et al., 2008b). Moreover, hospitals are required to pay an annual fee for participation, which means that resource-strained facilities may not be represented. The registry may therefore include a biased sample of hospitals, and it likely does not accurately capture the true incidence of IHCA in the United States. Additionally, the GWTG-R registry does not provide information related to the number of hospital admissions; as a result, incidence is difficult to calculate and researchers often rely on a combination of algorithms to provide estimate this figure. Like CARES and the ROC Epistry, the ability of the GWTG-R registry to report incidence or survival for specific patient groups (pediatrics or racial and ethnic minorities), or to examine the effects of confounding factors on outcomes, is fairly limited because of small sample sizes and missing data.
Strengths and Limitations of Current Registries for OHCA and IHCA
Cardiac arrest registries in the United States have achieved remarkable success in data collection in spite of the lack of mandatory reporting requirements for cardiac arrest, because of the voluntary participation of EMS and hospitals in select communities. Numerous publications have been produced based on the ROC, CARES, and GWTG-R registries, and each database has uniquely contributed to filling current gaps in knowledge. Relative to the ROC or GWTG-R registries, minimal data reporting requirements per cardiac arrest case have allowed CARES to create a surveillance network covering a greater portion of the U.S. population, with wider geographic reach and substantially lower cost. The ROC Epistry has created a more complex data source for cardiac arrest researchers, including more detailed data collection on event and resuscitation-specific process and outcomes per OHCA case. The GWTG-R registry, as the largest IHCA database, is a valuable source for assessing hospital-based resuscitation and post-arrest care.
However, common themes and limitations of current surveillance efforts have emerged. Existing registries capture data from a limited number of communities in the nation, from a voluntary subset of EMS and hospitals. In the absence of mandatory reporting requirements for OHCA and IHCA, many communities currently do not track any cardiac arrest outcomes at all. As a result, cardiac arrest incidence and outcomes data based on current surveillance systems may not be representative of the national state of cardiac arrest (Nichol et al., 2008b).
Second, because participation is voluntary, EMS and hospital systems that are already engaged in quality improvement, or have the resources to participate, are more likely to report cardiac arrest outcomes, thus introducing potential selection bias. For example, although geographically diverse, ROC sites may not be representative of all EMS agencies in the United States because they were initially selected through a competitive process for their ability to conduct OHCA randomized clinical trials. In fact, the baseline OHCA survival reported in the committee’s commissioned analysis of ROC data was higher than the average survival rate (7.6 percent) reported in a recent 30-year systematic review (Daya et al., 2015a; Sasson et al., 2010).
Third, current registries are limited by missing or unreliable data on important predictors of outcomes, such as patient race and ethnicity, or socioeconomic factors (e.g., income, education, primary language, and insurance status), making it difficult to identify especially vulnerable
populations and to adequately measure and rectify potential disparities in cardiac arrest treatment and outcomes. There is also a gap in evidence describing the long-term outcomes for patients who survive an arrest and are discharged from hospitals (Chan and Nallamothu, 2012). A comprehensive national database for cardiac arrest should provide reliable and valid measures of incidence and outcomes, as well as assessment of predictors and treatments on population health. Some characteristics of current national registries in the United States are highlighted in Table 2-5. Appendix D provides more information on each database.
Overview of International Cardiac Arrest Registries
There has been a proliferation of national population-based registries for cardiac arrest in Europe and Asia in recent years, as well as multinational registries such as the European Registry of Cardiac Arrest (EuReCa) and PAROS (Berg, 2014; McNally, 2014). Table 2-6 summarizes information on OHCA registries outside the United States. Registries that have published their survival rates report increasing rates of survival over time (Iwami et al., 2009; Kitamura et al., 2012). Research in Japan, Denmark, and more recently in the United States has attributed these increases to the Hawthorne effect; in other words, experts determined that communities that routinely monitor cardiac arrest responses and survival rates will improve their care over time, leading to better patient outcomes (Chan et al., 2014; Kellum et al., 2006).
TABLE 2-5 Characteristics of Primary Cardiac Arrest Registries in the United States
|Database Elements and Functionality||CARES||ROC||GWTG-R|
Captures OHCA EMS “treated” cases
Captures OHCA EMS “untreated” cases
Captures IHCA cases
Benchmarking with other participating sites
Performance feedback or reward system
Data quality audit
Free, no-cost enrollment open to all participants
a This X was omitted from the prepublication copy of this report.
Next Steps for Cardiac Arrest Surveillance
In spite of the enormous societal impact of cardiac arrest, efforts to systematically monitor incidence and routinely assess outcomes are limited to voluntary OHCA and IHCA registries that provide limited geographic coverage and data on a population subset and are, therefore, unable to capture the true incidence. Robust and reliable data are needed in order to precisely measure the disease burden, identify factors that influence neurologically intact survival, and assess treatment and care protocols supporting continuous quality improvement efforts. This requires mandatory reporting and monitoring of all cardiac arrests in the United States, to allow for collection of data on both OHCA and IHCA.
Traditionally, CDC has served as the primary federal organization responsible for creating and maintaining national surveillance systems for infectious diseases to prevent epidemics or high-priority biological events (e.g., use of anthrax or other biological weapons) that can potentially impact public health and national security. National disease surveillance systems later expanded to include acute and chronic conditions such as cancer, autism, and certain cardiovascular diseases (IOM, 2011). In 2000, a federal mandate charged CDC with creating the Paul Coverdell National Acute Stroke Registry (PCNASR), in memory of the U.S. senator who suffered a fatal stroke while in Congress (CDC, 2015a). CDC’s national disease surveillance system thus extends beyond its initial scope related to prevention of infectious diseases to registries that track treatment and care of clinical conditions (e.g., stroke) that have a substantial public health burden.
With many existing registries struggling and even competing for the scare funds required to create a robust surveillance systems, it is both logical and necessary to integrate current efforts into one cohesive national surveillance system for continuous and systematic monitoring, reporting, and analysis of cardiac arrest data. Several countries around the world have already implemented national registries for OHCA, demonstrating that such an endeavor is possible. Registries such as INTCAR (2012), described earlier, have combined aspects of prehospital and hospital care with the goal of evaluating post-arrest treatments and neurologic outcomes.
At this time, there are no successful surveillance models that have fully integrated collection of OHCA and IHCA, but there may be advantages to combining data collection. For example, it would allow detailed tracking of OHCA patients from initial arrest through hospital
discharge, thus allowing for nuanced evaluation of treatments and outcomes. It would also allow researchers to determine a more reliable incidence for cardiac arrest, because separate registries for OHCA and IHCA data can lead to over-counting of events. A single registry may also be more cost efficient and eliminate duplicate efforts (e.g., EMS agencies now contribute data to multiple prehospital care databases such as NEMSIS, ROC, and CARES). The committee recognizes that few examples exist for such a model. Moreover, public health surveillance systems that are too complex, large, or pose a great burden on EMS and hospitals are less likely to succeed. The committee evaluated capacities of U.S.-based cardiac arrest registries and concluded that while current surveillance systems (e.g., CARES) provide a strong foundation for a national database, there are important limitations that need to be addressed. Further discussion with multidisciplinary surveillance experts, including organizations such as CDC, cardiac arrest investigators, state health departments, EMS and hospitals, and other relevant stakeholders, is needed to determine the framework for a national database. The next section describes some essential components for a national cardiac arrest surveillance system.
Standardize Definitions of Incidence and Survival Outcome
As discussed in previous sections, there are wide discrepancies in reported rates of cardiac arrest incidence and survival in the published literature, primarily attributable to disparate use of nomenclature and definitions of cardiac arrest. OHCA data collection and outcomes reporting is generally based on the standardized Utstein Style template.7 The 2014 updated guidelines endorsed a summary metric for comparing system performance and recommended reporting of witnessed cardiac arrest cases that received bystander CPR and had a shockable first recorded rhythm and all EMS-treated cardiac arrests (Perkins et al., 2014). The AHA’s 2013 consensus statement proposes defining the IHCA incidence rate as all patients who receive chest compressions and/or defibrillation
7In the 1990s, many experts recognized the importance of a resuscitation-survival EMS system performance metric. Identifying this gap led to the development of a standardized template for measuring cardiac arrest survival rates (Cummins et al., 1991). The Utstein Style was named after the location of where the conference was held—the Utstein Abbey in Norway. Since 1991, multiple “Utstein conferences” have attempted to unify nomenclature used for uniform reporting in many areas of resuscitation.
TABLE 2-6 Out-of-Hospital Cardiac Arrest (OHCA) Registries Outside of the United States
|Location||Registry Name||Year Registry Established||Management and Funding||Participation||Additional Information|
|Asiaa||Pan-Asian Resuscitation Outcomes Study (PAROS)||2009||Asian Emergency Medical Services Council||Voluntary||Data elements and reporting scheme based on the Cares Arrest Registry to Enhance Survival|
|Europeb||European Registry of Cardiac Arrest (EuReCa)||2007||Managed and funded by the European Resuscitation Council||Voluntary||Includes prospective data from 25 countries|
|Denmarkc||Danish Cardiac Arrest Registry||2000||Database owned by EMS Funded by TrygFonden, a private foundation||Voluntary|
|Germanyd||German Resuscitation Registry||2007||German Resuscitation Council||Voluntary||Records data on a national level, but participating centers represent only 9 percent of total population|
|Irelande||National Out-of-Hospital Cardiac Arrest Register Project||2007||Pre-Hospital Emergency Care Council and the National Ambulance Service; administered and supported by the Discipline of General Practice at the National University of Ireland–Galway||Nonvoluntary||Monthly reporting to National Ambulance Services; national registry|
|Location||Registry Name||Year Registry Established||Management and Funding||Participation||Additional Information|
|Koreag||National Emergency Department Information System for Cardiac Arrest (NEDIS-CA) registry||2005||Supported by the National Emergency Medical Center in collaboration with the Korean Association of Cardiopulmonary Resuscitation||Voluntary||Registry structure similar to CARES|
|Norwayh||Norwegian Cardiovascular Diseases Registry||2012||Norwegian Institute of Public Health||Nonvoluntary||Does not require consent of individual|
|Swedeni||Swedish Cardiac Arrest Register||1990||Funded by Swedish National Board of Health and Welfare since 1993||Voluntary|
|Victoria, Australiaj||Victorian Ambulance Cardiac Arrest Registry||1999||Managed by Ambulance Victoria, the sole EMS in Victoria; funded by government of Victoria||Data collected from prehospital through 1-year post arrest (excluding children)|
SOURCES: aOng et al., 2011; bGrasner et al., 2014a; cWissenberg et al., 2013; dGrasner et al., 2014b; eOHCAR, 2014; fHasagewa et al., 2013; gYang et al., 2015; hNIPH, 2014; iHollenberg et al., 2008; jVACAR, 2012.
(numerator) out of the total number of patients admitted to the hospital, including those in the ICU and the operating and procedure rooms, along with their recovery areas (denominator) (Morrison et al., 2013). The consensus document also recommended that patients with DNAR status be removed from both the numerator and the denominator. Although these definitions exist, they are not uniformly applied. There is a need to apply standardized definitions for both OHCA and IHCA for use in a national surveillance system, to more precisely measure the public health burden of cardiac arrest in the United States.
Identify a Uniform Set of Patient and Quality-of-Care Outcome Metrics
Creating a national database of cardiac arrest and resuscitation care requires a core set of standardized outcome metrics for both OHCA and IHCA, and expansion of data collection beyond current efforts, to serve as a reasonable baseline for every EMS and health care system in the country. The 2014 Utstein guidelines recommended a common data collection template for OHCA reporting (see Appendix F). Although existing registries have identified some common measures of mortality outcomes (such as the rate of patient survival-to-hospital discharge), there is still a lack of consensus among experts regarding nonmortality and quality-of-life data such as neurologic outcomes following cardiac arrest (Becker et al., 2011). Large databases such as CARES, ROC, and GWTG-R collect many common data elements, yet there are differences in how the information is analyzed and reported in studies, making it difficult to evaluate outcomes across registries.
Historically, resuscitation experts have measured patient outcomes primarily in terms of survival rates, including survival to hospital discharge, survival to hospital admission, or long-term survival (30-90 days, 1 year, or 5 years following discharge). The degree of neurologic injury following cardiac arrest, by contrast, has been presented using various scales, including the CPC, mRS, or other short- and long-term cognitive outcomes across studies. The Utstein reporting guidelines have designated CPC as a core outcome element (Jacobs et al., 2004) (see Box 2-3 for details). While the CARES registry reports neurologic outcomes using the CPC scoring system, the ROC Epistry relies on the mRS scale, contributing to a lack of harmonization. Some studies have defined “good outcome” as CPC 1 or CPC 2, recognizing that within these categories there may still be significant neurologic disability involving neurocognitive
Common Measures of Neurologic Function Following Cardiac Arrest
Cerebral Performance Categories (CPC):
CPC 1: good cerebral performance
CPC 2: moderate cerebral disability
CPC 3: severe dysfunction
CPC 4: coma
CPC 5: brain death
NOTE: In the updated Utstein reporting guidelines, CPC was designated as a core outcome element. Generally, CPC 1 or 2 is considered to be an indicator of positive neurologic outcome. SOURCE: Jacobs, 2004.
Modified Rankin Scale (mRS):
0: no symptoms
1: no significant disability (able to carry out all usual activities, despite some symptoms)
2: slight disability (able to look after own affairs without assistance, but unable to carry out all previous activities)
3: moderate disability (requires some help, but able to walk unassisted)
4: moderately severe disability (unable to attend to own bodily needs without assistance, and unable to walk unassisted)
5: severe disability (requires constant nursing care and attention, bedridden, incontinent)
6: brain death
NOTE: Generally, mRS ≤ 3 is considered to be an indicator of positive neurologic outcome.
SOURCE: Banks and Marotta, 2007.
deficits. Although the CPC system is a useful outcome tool, it lacks the ability to more clearly distinguish levels of significant differences in neurologic outcomes. In a systematic review of clinical trials on post-arrest care, there was significant heterogeneity in the selection of outcome measures (Trzeciak et al., 2009). Two features were observed: (1) rather than survival alone, indices of functional survival were often used and (2) neurologic function was dichotomized into ordinal scales such as the CPC categories of “good” versus “bad” neurologic outcome. In order to improve patient outcomes following cardiac arrest, it is imperative that
future surveillance efforts accurately monitor and report both mortality and morbidity measures of patient outcomes using a standardized template of variables.
The absence of a registry that captures high-quality and complete demographic data regarding race and ethnicity and socioeconomic factors makes it challenging to gather and evaluate evidence on disparities in cardiac arrest incidence, treatment, and outcomes. Racial and ethnic variables used in many studies are classified inconsistently; for example, the ROC Epistry does not include race and ethnicity data using federally defined categories (Sugarman et al., 2009). At this time CARES has a 26 percent missing rate for race and ethnicity data (McNally et al., 2011). Socioeconomic data (e.g., income, education, and insurance status) are routinely underreported in all existing registries. This is in part because challenges in collection of accurate and unbiased data; race and ethnicity data collection relies on visual assessment by EMS providers rather than patient self-reporting because the latter is not an option for cardiac arrest victims. Assessment of income and education also is usually based on neighborhood rather than individual factors. However, a method for checking completeness and accuracy for this type of reporting is needed. Additionally, some experts have suggested collecting information on location of arrest, which can be linked to demographic data from an area census tract.
Precise measurement of patient outcome variables that account for sources of potential confounding or bias is essential for advancing the current understanding of the effectiveness of existing treatments and therapies. The availability of data derived from these variables is also needed in order to guide future resuscitation research priorities. Standardized data on factors that influence a patient’s likelihood of survival with positive neurologic outcomes (e.g., patient demographic characteristics, EMS and health system processes, location and geographic characteristics, and bystander CPR and AED use) is needed to allow for accurate statistical adjustments in measuring the outcome of interest.
EMS system– and health care system–level factors are often measured in units of time (e.g., response time between collapse and treatment). Although databases such as NEMSIS provide some oversight of national EMS data synthesis and reporting in an effort to identify high- and low-performing systems and best practices, a better understanding of these factors could be developed through the use of additional metrics (e.g., whether dispatcher-assisted CPR was available and provided). The
implementation of additional metrics could also allow for benchmarking and drive improvements in systems of care.
Several national and international registries in other fields, such as epilepsy, have done important work in expanding surveillance efforts by linking patient medical records with administrative billing data from providers and payers (IOM, 2012). This enriches the data source by allowing researchers to assess the cost-effectiveness of specific treatments and therapies, by linking cost data with patient outcomes. Existing cardiac arrest registries have sparse data on cost and thus cannot be used to assess the economic impact of cardiac arrest.
Expanding data collection beyond the current minimal data template to include important variables, such as patient demographic and socioeconomic data and availability of specific post-arrest treatments can fill existing gaps in knowledge. The minimalist data points that are currently collected by CARES can be modified to enrich available information without creating substantial burden on the system. Cardiac arrest is a complex event that requires a wide continuum of care, in which the most critical data pieces come from separate data sources, including prehospital information collected from 911 call centers and local EMS providers, additional patient health information and administrative billing data from hospitals records, and death records. Expanding the number of data variables collected from EMS or hospitals will require a secure and integrated database that is able to accept multiple sources.
Create a Secure, Integrated Data Repository for Cardiac Arrest
Currently, data systems between EMS and hospitals lack interoperability because of the proprietary nature of each system, funding limitations, and concerns regarding patient privacy laws. However, a recent study in North Carolina demonstrated the utility of linking statewide EMS data with information from a state-based stroke registry, which allows researchers to follow the patient from the initial 911 emergency call through hospital discharge and, thus, evaluate effectiveness of specific treatments as well as quality of care (Mears et al., 2010). Future surveillance and research efforts can promote state-based data integration by leveraging electronic medical records from EMS and hospitals. Moreover, a database that is able to accept relevant data from medical devices could also be a valuable addition for cardiac arrest surveillance. For example, AEDs in public locations can provide data on location of arrest and whether cardiac arrest is due to a shockable rhythm (see Chapter 6
for detailed discussion). Other emerging sources of data such as wearable medical devices or mobile technologies can provide data about time of arrest, allowing for more precise calculation of collapse-to-treatment times. As data sources and collection activities expand, additional protections may be necessary, including security of the data system, quality control measures, procedures for protection of personal health information, deidentification of data as needed, and data reporting and analytic capabilities.
Engage State Support in Mandatory Reporting of Cardiac Arrest, Improving Data Integration and Outcomes Assessment
Involving state governments and health agencies, along with CDC, in activities related to a national surveillance system for cardiac arrest is necessary, because states would be responsible for providing technical assistance and training for participating hospitals, EMS agencies, and related staff, as well as for aggregating state-level data from multiple sources. State health and related government agencies are uniquely positioned to drive the success of a national registry for cardiac arrest by (1) mandating reporting of all new cardiac arrest (OHCA and IHCA) events, (2) coordinating data collection from EMS and hospitals into a standardized registry, and (3) collaborating with relevant stakeholders to produce a publicly available “report card” of patient outcomes. At this time, 12 state-based OHCA registries contribute EMS data into CARES. This model can be used as a template to expand data collection activities to other states using an expanded standardized reporting template, which allows for multifaceted data entry.
CDC’s National Program of Cancer Registries (NPCR), for example, supports a national system of state-based cancer registries in 45 states and covers 96 percent of the U.S. population—allowing researchers to more accurately assess the burden of cancer, allowing policy makers to provide more targeted and localized solutions (CDC, 2013). The congressional act8 provides CDC with funds for improving existing state-level registries, creating new registries in states where ones did not exist previously, and setting national quality standards, but states still have the authority to mandate reporting, timeliness, and data quality and take steps to ensure compliance with patient confidentiality requirements (Izquierdo and Schoenbach, 2000). Similarly, PCNASR, aimed at im
8In 1992, Congress authorized the creation of NPCR through Public Law 102-515.
proving the quality of in-hospital stroke care (CDC, 2015a), requires that participating state health departments implement state-based registries and collaborate with local EMS systems and hospitals to collect data from the onset of the stroke through hospital discharge (George et al., 2009, 2011).
State health departments and government agencies also are a natural fit for providing surveillance oversight, because a majority of states already allocate resources to and regulate local EMS systems and hospitals. According to a recent report from the NHTSA, 39 states (78 percent) currently have formal regulations that require local EMS agencies to collect and report EMS data to a state-level EMS data system (FICEMS, 2011). A majority of these states report data using a standard template from the NEMSIS database. In the same report, 18 states noted the ability to monitor cardiac arrest data. Although only half of these state-level registries currently link EMS data with other health care systems (such as the trauma registry, emergency department and hospital discharge data, and motor vehicle crash data). State governments could enhance data consolidation from multiple sources by leveraging existing electronic health records to collect outcomes data. State-level registries could be used to produce a publicly available “report card” of patient outcomes to inform local and regional stakeholders, and allow benchmarking against a national standard.
Use Publicly Available Outcomes Data to Facilitate Accountability at Multiple Levels
The lack of accountability for EMS and health systems at the local level has likely contributed to variation in outcomes that exist between otherwise similar communities and comparably resourced health care systems. Currently, one-fifth of EMS systems and several hundred hospitals in the United States systematically collect and report data on OHCA and IHCA to a larger registry, and as a result these systems and hospitals can benchmark their performance against other communities. Although some EMS and health systems informally monitor patient and process outcomes to implement quality improvement strategies within their own system, patient outcomes data from EMS and hospitals are not harmonized across an integrated platform. As a result, the leadership of EMS systems are often unable to assess the quality of care provided by EMS personnel, because they do not have access to patient outcomes data after transporting a patient to a hospital. The inability to measure performance,
and to benchmark against other communities, accounts for the general inattention to cardiac arrest and a lack of accountability among providers and leadership.
With the exception of CARES, outcome data from OHCA and IHCA cardiac arrest registries in the United States are not available to, or easily accessible by, the public. Subsequently, members of the general public are unaware of the performance of their local health care systems compared to other communities. This creates challenges for policy makers and other stakeholders to identify high-risk populations and communities, educate the public about cardiac arrest risks, and appropriately allocate resources. Reliable data at the local level will allow community organizations, advocates, and professional organizations to better target interventions toward vulnerable populations. A data system that generates customized output for the user at federal, regional, municipal, and local levels is needed. Making deidentified data sets available to academic institutions and investigators can also reduce the cost of research. Public reporting of data will increase transparency in care delivery and promote a national dialogue on cardiac arrest, thus generating greater accountability at each level of society.
The societal and economic burden of death and disability due to cardiac arrest is a significant public health problem in the United States. Published studies based on data collected by OHCA and IHCA registries have identified a number of factors related to characteristics of the individual patient and the EMS or health care system, as well as elements unique to the cardiac arrest event, that determine survival. However, existing surveillance systems for cardiac arrest have a number of limitations that have made it challenging to accurately measure and communicate the public health burden of cardiac arrest, and to assess the effectiveness of currently available treatments and care delivery strategies.
Valid and reliable data play an essential role in driving high-quality care, and ultimately in improving patient outcomes. Communities that continuously measure their performance and benchmark against national standards have made strides in improving population rates of survival with favorable neurologic outcomes. The cardiac arrest field would be substantially enhanced through the establishment of a national surveillance system for cardiac arrest, with mandatory data reporting for both
OHCA and IHCA to allow for accurate evaluation of incidence and outcomes, as well as effectiveness of treatments and care. The data from this national registry could be publicly available (with appropriate patient privacy and confidentiality protections in place) for use by a wide range of stakeholders. This would also allow the United States to contribute to a global data network and create multinational databases similar to the work that has been completed in Europe and Asia. It has become apparent that cardiac arrest and resuscitation systems, both in the hospital and out of the hospital, must know, measure, and understand local survival rates, and identify the factors that determine those outcomes, in order to promote system improvement. The creation of a national database, and mandatory reporting of cardiac arrest, may represent one of the most effective methods for driving improvements in nationwide survival.
Ahn, K. O., S. D. Shin, G. J. Suh, W. C. Cha, K. J. Song, S. J. Kim, E. J. Lee, and M. E. H. Ong. 2010. Epidemiology and outcomes from non-traumatic out-of-hospital cardiac arrest in Korea: A nationwide observational study. Resuscitation 81(8):974-981.
Ahn, K. O., S. D. Shin, and S. S. Hwang. 2012. Sex disparity in resuscitation efforts and outcomes in out-of-hospital cardiac arrest. American Journal of Emergency Medicine 30(9):1810-1816.
Akahane, M., T. Ogawa, S. Koike, S. Tanabe, H. Horiguchi, T. Mizoguchi, H. Yasunaga, and T. Imamura. 2011. The effects of sex on out-of-hospital cardiac arrest outcomes. American Journal of Medicine 124(4):325-333.
Atkins, D. L., and S. Berger. 2012. Improving outcomes from out-of-hospital cardiac arrest in young children and adolescents. Pediatric Cardiology 33(3):474-483.
Atkins, D. L., S. Everson-Stewart, G. K. Sears, M. Daya, M. H. Osmond, C. R. Warden, and R. A. Berg. 2009. Epidemiology and outcomes from out-of-hospital cardiac arrest in children: The Resuscitation Outcomes Consortium Epistry-Cardiac Arrest. Circulation 119(11):1484-1491.
Banks, J. L., and C. A. Marotta. 2007. Outcomes validity and reliability of the modified Rankin scale: Implications for stroke clinical trials: A literature review and synthesis. Stroke 38(3):1091-1096.
Bardai, A., J. Berdowski, C. van der Werf, M. T. Blom, M. Ceelen, I. M. van Langen, J. G. P. Tijssen, A. A. M. Wilde, R. W. Koster, and H. L. Tan. 2011. Incidence, causes, and outcomes of out-of-hospital cardiac arrest in children. A comprehensive, prospective, population-based study in the Netherlands. Journal of the American College of Cardiology 57(18):1822-1828.
Becker, L. B., B. H. Han, P. M. Meyer, F. A. Wright, K. V. Rhodes, D. W. Smith, and J. Barrett. 1993. Racial differences in the incidence of cardiac arrest and subsequent survival. The CPR Chicago project. New England Journal of Medicine 329(9):600-606.
Becker, L. B., T. P. Aufderheide, R. G. Geocadin, C. W. Callaway, R. M. Lazar, M. W. Donnino, and V. M. Nadkarni. 2011. Primary outcomes for resuscitation science studies a consensus statement from the American Heart Association. Circulation 124(19):2158-2177.
Berdowski, J., R. Berg, J. Tijjssen, and R. Koster. 2010. Global incidences of out-of-hospital cardiac arrest and survival rates: Systematic review of 67 prospective studies. Resuscitation 81(11):1479-1487.
Berg, R. A. 2014. Harnessing the power of national databases: Progress in Japan. Presentation to the Committee on the Treatment of Cardiac Arrest: Current Status and Future Directions https://www.iom.edu/~/media/Files/Activity%20Files/PublicHealth/TreatmentofCardiacArrest/August%202014/Berg-Panel%204%20presentation%20Japananese%20Natldatabase%20for%20posting.pdf (accessed June 15, 2015).
Bobrow, B., M. Panczyk, U. Stolz, M. Sotelo, T. Vadeboncoeur, J. Sutter, B. Langlais, and D. Spaite. 2014. Abstract 1: Statewide implementation of a standardized prearrival telephone CPR program is associated with increased bystander CPR and survival from out-of-hospital cardiac arrest. Circulation 130(Suppl 2):A1.
Bray, J. E., S. Di Palma, I. Jacobs, L. Straney, and J. Finn. 2014. Trends in the incidence of presumed cardiac out-of-hospital cardiac arrest in Perth, Western Australia, 1997-2010. Resuscitation 85(6):757-761.
Brooks, S. C., J. H. Hsu, S. K. Tang, R. Jeyakumar and T. C. Chan 2013. Determining risk for out-of-hospital cardiac arrest by location type in a Canadian urban setting to guide future public access defibrillator placement. Annals of Emergency Medicine 61(5):530-538.
Brown, L. 2005. Pediatric out-of-hospital cardiopulmonary arrest and public access defibrillation programs for children. Pediatric Emergency Medicine Practice 2(9):5.
Carr, B. G., J. M. Kahn, R. M. Merchant, A. A. Kramer, and R. W. Neumar. 2009. Inter-hospital variability in post-cardiac arrest mortality. Resuscitation 80(1):30-34.
CDC (Centers for Disease Control and Prevention). 2013. National Program of Cancer Registries (NPCR): About the program. http://www.cdc.gov/cancer/npcr/about.htm (accessed March 20, 2015).
CDC. 2015a. Paul Coverdell National Acute Stroke Registry Program Summary Report, 2007–2012. Atlanta, GA: U.S. Department of Health and Human Services.
CDC. 2015b. CARES: Cardiac Arrest Registry to Enhance Survival. https://mycares.net (accessed March 20, 2015).
Chan, P. 2015. Public health burden of in-hospital cardiac arrest. Paper commissioned by the Committee on the Treatment of Cardiac Arrest: Current Status and Future Directions. http://www.iom.edu/~/media/Files/Report%20Files/2015/GWTG.pdf (accessed June 30, 2015).
Chan, P. S., and B. K. Nallamothu. 2012. Improving outcomes following in-hospital cardiac arrest: Life after death. Journal of the American Medical Association 307(18):1917-1918.
Chan, P. S., H. M. Krumholz, G. Nichol, and B. K. Nallamothu. 2008. Delayed time to defibrillation after in-hospital cardiac arrest. New England Journal of Medicine 358(1):9-17.
Chan, P. S., G. Nichol, H. M. Krumholz, J. A. Spertus, P. G. Jones, E. D. Peterson, S. S. Rathore, B. K. Nallamothu, and American Heart Association National Registry of Cardiopulmonary Resuscitation (NRCPR) Investigators. 2009. Racial differences in survival after in-hospital cardiac arrest. Journal of the American Medical Association 302(11):1195-1201.
Chan, P. S., R. Jain, B. K. Nallmothu, R. A. Berg, and C. Sasson. 2010. Rapid response teams: A systematic review and meta-analysis. Archives of Internal Medicine 170(1):18-26.
Chan, P. S., B. K. Nallamothu, H. M. Krumholz, J. A. Spertus, Y. Li, B. G. Hammill, and L. H. Curtis. 2013. Long-term outcomes in elderly survivors of in-hospital cardiac arrest. New England Journal of Medicine 368(11):1019-1026.
Chan, P. S., B. K. Nallamothu, H. M. Krumholz, L. H. Curtis, Y. Li, B. G. Hammill, and J. A. Spertus. 2014. Readmission rates and long-term hospital costs among survivors of an in-hospital cardiac arrest. Circulation: Cardiovascular Quality and Outcomes 7(6):889-895.
Chugh, S. S., J. Jui, K. Gunson, E. C. Stecker, B. T. John, B. Thompson, N. Ilias, C. Vickers, V. Dogra, M. Daya, J. Kron, Z.-J. Zheng, G. Mensah, and J. McAnulty. 2004. Current burden of sudden cardiac death: Multiple source surveillance versus retrospective death certificate-based review in a large U.S. community. Journal of the American College of Cardiology 44(6):1268-1275.
Chugh, S. S., A. Uy-Evanado, C. Teodorescu, K. Reinier, R. Mariani, K. Gunson, and J. Jui. 2009. Women have a lower prevalence of structural heart disease as a precursor to sudden cardiac arrest: The Ore-SUDS (Oregon Sudden Unexpected Death Study). Journal of the American College of Cardiology 54(22):2006-2011.
Cobb, L. A., C. E. Fahrenbruch, M. Olsufka, and M. K. Copass. 2002. Changing incidence of out-of-hospital ventricular fibrillation, 1980-2000. Journal of the American Medical Association 288(23):3008-3013.
Cummins, R. O., D. A. Chamberlain, N. S. Abramson, M. Allen, P. J. Baskett, L. Becker, L. Bossaert, H. H. Delooz, W. F. Dick, M. S. Eisenberg, T. R. Evans, S. Homberg, R. Kerber, A. Mullie, J. P. Ornato, E. Sandoe, A. Skulberg, H. Tunstall-Pedoe, R. Swanson, and W. H. Thies. 1991. Recommended
guidelines for uniform reporting of data from out-of-hospital cardiac arrest: The Utstein Style. A statement for health professionals from a task force of the American Heart Association, the European Resuscitation Council, the Heart and Stroke Foundation of Canada, and the Australian Resuscitation Council. Circulation 84(2):960-975.
Daya, M., R. Schmicker, S. May, and L. Morrison. 2015a. Current burden of cardiac arrest in the United States: Report from the Resuscitation Outcomes Consortium. Paper commissioned by the Committee on the Treatment of Cardiac Arrest: Current Status and Future Directions. http://www.iom.edu/~/media/Files/Report%20Files/2015/ROC.pdf (accessed June 30, 2015).
Daya, M. R., R. H. Schmicker, D. M. Zive, T. D. Rea, G. Nichol, J. E. Buick, and Resuscitation Outcomes Consortium Investigators. 2015b. Out-of-hospital cardiac arrest survival improving over time: Results from the Resuscitation Outcomes Consortium (ROC). Resuscitation 91:108-115.
De Maio, V., I. G. Stiell, C. Vaillancourt C. 2004. Locations of pediatric cardiac arrest: Implications for public access defibrillation [abstract]. Prehospital Emergency Care 8(1):80. Presented at the National Association of EMS Physicians, Tucson, AZ, January 2004.
Ehlenbach, W. J., A. E. Barnato, J. R. Curtis, W. Kreuter, T. D. Koepsell, R. A. Deyo, and R. D. Stapleton. 2009. The epidemiology of in-hospital cardiopulmonary resuscitation in older adults: 1992-2005. New England Journal of Medicine 361(1):22-31.
Ellrodt, A. G., G. C. Fonarow, L. H. Schwamm, N. Albert, D. L. Bhatt, C. P. Cannon, and E. E. Smith. 2013. Synthesizing lessons learned from Get With The Guidelines: The value of disease-based registries in improving quality and outcomes. Circulation 128(22):2447-2460.
Fake, A. L, P. H. Swain, and P. D. Larsen. 2013. Survival from out-of-hospital cardiac arrest in Wellington in relation to socioeconomic status and arrest location. New Zealand Medical Journal 126(1376):28-37.
FICEMS (Federal Interagency Committee on Emergency Medical Services). 2011. National EMS assessment. Washington, DC: U.S. Department of Transportation, National Highway Traffic Safety Administration.
Friedlander, Y., D. S. Siscovick, S. Weinmann, M. A. Austin, B. M. Psaty, R. N. Lemaitre, P. Arbogast, T. E. Raghunathan, and L. A. Cobb. 1998. Family history as a risk factor for primary cardiac arrest. Circulation 97(2):155-160.
Galea, S., S. Blaney, A. Nandi, R. Silverman, D. Vlahov, G. Foltin, M. Kusick, M. Tunik, and N. Richmond. 2007. Explaining racial disparities in incidence of and survival from out-of-hospital cardiac arrest. American Journal of Epidemiology 166(5):534-543.
George, M. G., X. Tong, H. McGruder, P. Yoon, W. Rosamond, A. Winquist, J. Hinchey, H. K. Wall, and D. K. Pandey. 2009. Paul Coverdell National Acute Stroke Registry surveillance—four states, 2005-2007. Morbidity and Mortality Weekly Report: Surveillance Summaries 58(7):1-23.
George, M. G., X. Tong, and P. W. Yoon. 2011. Use of a registry to improve acute stroke care—seven states, 2005–2009. Morbidity and Mortality Weekly Report 60(7):206-210.
Girotra, S., B. K. Nallamothu, J. A. Spertus, Y. Li, H. M. Krumholz, and P. S. Chan. 2012. Trends in survival after in-hospital cardiac arrest. New England Journal of Medicine 367(20):1912-1920.
Girotra, S., P. Cram, J. A. Spertus, B. K. Nallamothu, Y. Li, P. G. Jones, and P. S. Chan. 2014. Hospital variation in survival trends for in-hospital cardiac arrest. Journal of the American Heart Association 3(3):e000871.
Glover, B. M., S. P. Brown, L. Morrison, D. Davis, P. J. Kudenchuk, L. Van Ottingham; Resuscitation Outcomes Consortium Investigators. 2012. Wide variability in drug use in out-of-hospital cardiac arrest: A report from the Resuscitation Outcomes Consortium. Resuscitation 83(11):1324-1330.
Go, A. S., D. Mozaffarian, V. L. Roger, E. J. Benjamin, J. Berry, M. J. Blaha, S. Dai, E. S. Ford, C. S. Fox, S. Franco, H. J. Fullerton, C. Gillespie, S. M. Hailpern, J. A. Heit, V. J. Howard, M. D. Huffman, S. E. Judd, B. M. Kissela, S. J. Kittner, D. T. Lackland, J. H. Lichtman, L. D. Lisabeth, R. H. Mackey, D. J. Magid, G. M. Marcus, A. Marelli, D. B. Matchar, D. K. McGuire, E. R. Mohler, C. S. Moy, M. E. Mussolino, R. W. Neumar, G. Nichol, D. K. Pandey, N. P. Paynter, M. J. Reeves, P. D. Sorlie, J. Stein, A. Towfighi, T. N. Turan, S. S. Virani, N. D. Wong, D. Woo, M. B. Turner; on behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. 2014. Heart disease and stroke statistics—2014 update: A report from the American Heart Association. Circulation 129(3):e28.
Grasner, J. T., B. W. Böttiger, J. Herlitz, R. W. Koster, F. R Ortiz, I. Tjelmeland, J. Wnent, H. Maurer, S. Masterson, and L. Bossaert. 2014a. EuReCa ONE—25 nations, ONE Europe, ONE registry: A prospective data analysis over 1 month in 25 resuscitation registries in Europe. Circulation 130(2):A274.
Grasner, J. T., S. Seewald, A. Bohn, M. Fischer, M. Messelken, T. Jantzen, and J. Wnent. 2014b. German resuscitation registry: Science and resuscitation research. Anaesthesist 63(6):470-476.
Grimaldi, D., F. Dumas, M. C. Perier, J. Charpentier, O. Varenne, B. Zuber, B. Vivien, F. Pene, J. P. Mira, J. P. Empana, and A. Cariou. 2014. Short- and long-term outcome in elderly patients after out-of-hospital cardiac arrest: A cohort study. Critical Care Medicine 42(11):2350-2357.
Groeneveld, P. W., P. A. Heidenreich, and A. M. Garber. 2003. Racial disparity in cardiac procedures and mortality among long-term survivors of cardiac arrest. Circulation 108(3):286-291.
Grossestreuer, A. V., B. S. Abella, M. Leary, R. W. Neumar, L. B. Becker, and D. F. Gaieski. 2011. Penn alliance for therapeutic hypothermia: The first U.S. registry of post-cardiac arrest hypothermia patients-hemodynamics, mortality, and therapeutic hypothermia. Circulation 124(21 Suppl): A15463.
Hasagewa, K., Y. Tsugawa, C. A. Camargo, Jr., A. Hiraide, and D. F. M. Brown. 2013. Regional variability in survival outcomes of out-of-hospital cardiac arrest: The All-Japan Utstein Registry. Resuscitation 84(8):1099-1107.
Hasan, O. F., J. Al Suwaidi, A. A. Omer, W. Ghadban, H. Alkilani, A. Gehani, and A. M. Salam. 2014. The influence of female gender on cardiac arrest outcomes: A systematic review of the literature. Current Medical Research and Opinion 30(11):2169-2178.
Hollenberg, J., J. Herlitz, J. Lindqvist, G. Riva, K. Bohm, M. Rosenqvist, and L. Svensson. 2008. Improved survival after out-of-hospital cardiac arrest is associated with an increase in proportion of emergency crew—witnessed cases and bystander cardiopulmonary resuscitation. Circulation 118(4): 389-396.
INTCAR (International Cardiac Arrest Registry). 2012. What is INTCAR. http://www.intcar.org (accessed June 23, 2015).
IOM (Institute of Medicine). 2011. A nationwide framework for surveillance of cardiovascular and chronic lung diseases. Washington, DC: The National Academies Press.
IOM. 2012. Epilepsy across the spectrum: Promoting health and understanding. Washington, DC: The National Academies Press.
Israelsson, J., C. Persson, A. Stromberg, and K. Arestedt. 2014. Is there a difference in survival between men and women suffering in-hospital cardiac arrest? Heart & Lung: The Journal of Acute Critical Care 43(6):510-515.
Iwami, T., G. Nichol, A. Hiraide, Y. Hayashi, T. Nishiuchi, K. Kajino, H. Morita, H. Yukioka, H. Ikeuchi, H. Sugimoto, H. Nonogi, and T. Kawamura. 2009. Continuous improvements in “chain of survival” increased survival after out-of-hospital cardiac arrests: A large-scale population-based study. Circulation 119(5):728-734.
Izquierdo, J. N., and V. J. Schoenbach. 2000. The potential and limitations of data from population-based state cancer registries. American Journal of Public Health 90(5):695-698.
Jacobs, I., V. Nadkarni, J. Bahr, R. A. Berg, J. E. Billi, L. Bossaert, P. Cassan, A. Coovadia, K. D’Este, J. Finn, H. Halperin, A. Handley, J. Herlitz, R. Hickey, A. Idris, W. Kloeck, G. L. Larkin, M. E. Mancini, P. Mason, G. Mears, K. Monsieurs, W. Montgomery, P. Morley, G. Nichol, J. Nolan, K. Okada, J. Perlman, M. Shuster, P. A. Steen, F. Sterz, J. Tibballs, S. Timerman, T. Truitt, and D. Zideman. 2004. Cardiac arrest and cardiopulmonary resuscitation outcome reports: Update and simplification of the Utstein templates for resuscitation registries. A statement for health care professionals from a task force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian Resuscitation Council, New Zealand Resuscitation Council, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Councils of Southern Africa). Resuscitation 63(3):233-249.
Jennings, P. A., P. Cameron, T. Walker, S. Bernard, and K. Smith. 2006. Out-of-hospital cardiac arrest in Victoria: Rural and urban outcomes. Medical Journal of Australia 185(3):135.
Johnson, M. A., B. J. H. Grahan, J. S. Haukoos, B. McNally, R. Campbell, C. Sasson, and D. E. Slattery. 2014. Demographics, bystander CPR, and AED use in out-of-hospital pediatric arrests. Resuscitation 85(7):920-926.
Kellum, M. J., K. W. Kennedy, and G. A. Ewy. 2006. Cardiocerebral resuscitation improves survival of patients with out-of-hospital cardiac arrest. American Journal of Medicine 119(4):335-340.
Kida, K. and F. Ichinose. 2014. Preventing ischemic brain injury after sudden cardiac arrest using NO inhalation. Critical Care 18(2):212-218.
Kim, M. J., S. D. Shin, W. M. McClellan, B. McNally, Y. S. Ro, K. J. Song, E. J. Lee, Y. J. Lee, J. Y. Kim, S. O. Hong, J. A. Choi, and Y. T. Kim. 2014. Neurological prognostication by gender in out-of-hospital cardiac arrest patients receiving hypothermia treatment. Resuscitation 85(12):1732-1738.
Kitamura, T., T. Iwami, T. Kawamura, M. Nitta, K. Nagao, H. Nonogi, N. Yonemoto, and T. Kimura. 2012. Nationwide improvements in survival from out-of-hospital cardiac arrest in Japan. Circulation 126(24):2834-2843.
Komatsu, T., K. Kinoshita, A. Sakurai, T. Moriya, J. Yamaguchi, A. Sugita, R. Kogawa, and K. Tanjoh. 2013. Shorter time until return of spontaneous circulation is the only independent factor for a good neurological outcome in patients with postcardiac arrest syndrome. Emergency Medicine Journal 0:1-17.
Kong, M. H., G. C. Fonarow, E. D. Peterson, A. B. Curtis, A. F. Hernandez, G. D. Sanders, K. L. Thomas, D. L. Hayes, and S. M. Al-Khatib. 2011. Systematic review of the incidence of sudden cardiac death in the United States. Journal of the American College of Cardiology 57(7):794-801.
Larkin, G. L., W. S. Copes, B. H. Nathanson, and W. Kaye. 2010. Preresuscitation factors associated with mortality in 49,130 cases of in-hospital cardiac arrest: A report from the national registry for cardiopulmonary resuscitation. Resuscitation 81(3):302-311.
Levy, M. J., K. G. Seaman, M. G. Millin, R. A. Bissell, and J. L. Jenkins. 2013. A poor association between out-of-hospital cardiac arrest location and public automated external defibrillator placement. Prehospital and Disaster Medicine 28(4):342-347.
Lopez-Herce, J., J. Del Castillo, M. Matamoros, S. Canadas, A. Rodriguez-Calvo, C. Cecchetti, A. Rodriguez-Nunez, and A. C. Alvarez. 2013. Factors associated with mortality in pediatric in-hospital cardiac arrest: A prospective multicenter multinational observational study. Intensive Care Medicine 39(2):309-318.
Lopez-Herce, J., J. del Castillo, S. Canadas, A. Rodriguez-Nunez, and A. Carrillo. 2014. In-hospital pediatric cardiac arrest in Spain. Revista Española de Cardiología (English Edition) 67(3):189-195.
Mahapatra, S., T. J. Bunch, R. D. White, D. O. Hodge, and D. L. Packer. 2005. Sex differences in outcome after ventricular fibrillation in out-of-hospital cardiac arrest. Resuscitation 65(2):197-202.
MCDHHS (Milwaukee County Department of Health and Human Services). 2014. Annual Review. http://county.milwaukee.gov/ImageLibrary/Groups/cntyHHS/2014ReportCOVER.pdf (accessed June 23, 2015).
McNally, B. 2014. The importance of cardiac arrest registries. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 22(1):1-2.
McNally, B., A. Stokes, A. Crouch, and A. L. Kellermann. 2009. CARES: Cardiac Arrest Registry to Enhance Survival. Annals of Emergency Medicine 54(5):674-683.e672.
McNally, B., R. Robb, M. Mehta, K. Vellano, A. L. Valderrama, P. W. Yoon, C. Sasson, A. Crouch, A. B. Perez, R. Merritt, and A. Kellermann. 2011. Out-of-hospital cardiac arrest surveillance—Cardiac Arrest Registry to Enhance Survival (CARES), United States, October 1, 2005–December 31, 2010. Morbidity and Mortality Weekly Report: Surveillance Summaries 60(8):1-19.
Mears, G. D., W. D. Rosamond, C. Lohmeier, C. Murphy, E. O’Brien, A. W. Asimos, and J. H. Brice. 2010. A link to improve stroke patient care: A successful linkage between a statewide emergency medical services data system and a stroke registry. Academic Emergency Medicine 17(12):1398-1404.
Meert, K. L., A. Donaldson, V. Nadkarni, K. S. Tieves, C. L. Schleien, R. J. Brilli, R. S. B. Clark, D. H. Shaffner, F. Levy, K. Statler, H. J. Dalton, E. W. van der Jagt, R. Hackbarth, R. Pretzlaff, L. Hernan, M. Dean, and F. W. Moler. 2009. Multicenter cohort study of in-hospital pediatric cardiac arrest. Pediatric Critical Care Medicine: A Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 10(5):544.
Merchant, R. M., and D. A. Asch. 2012. Can you find an AED if a life depends on it? Circulation: Cardiovascular Quality and Outcomes 5(2): 241-243.
Merchant, R. M., L. B. Becker, B. S. Abella, D. A. Asch, and P. W. Groeneveld. 2009. Cost-effectiveness of therapeutic hypothermia after cardiac arrest. Circulation: Cardiovascular Quality and Outcomes 2(5):421-428.
Merchant, R. M., L. Yang, L. B. Becker, R. A. Berg, V. Nadkarni, G. Nichol, B. G. Carr, N. Mitra, S. M. Bradley, B. S. Abella, and P. W. Groeneveld. 2011. Incidence of treated cardiac arrest in hospitalized patients in the United States. Critical Care Medicine 39(11):2401-2406.
Merchant, R. M., R. A. Berg, L.Yang, L. B. Becker, P. W. Groeneveld, and P. S. Chan. 2014. Hospital variation in survival after in‐hospital cardiac arrest. Journal of the American Heart Association 3(1):e000400.
Meyer, L., B. Stubbs, C. Fahrenbruch, C. Maeda, K. Harmon, M. Eisenberg, and J. Drezner. 2012. Incidence, causes, and survival trends from cardiovascular-related sudden cardiac arrest in children and young adults 0 to 35 years of age: A 30-year review. Circulation 126(11):1363-1372.
Mitani, Y., K. Ohta, F. Ichida, M. Nii, Y. Arakaki, H. Ushinohama, T. Takahashi, H. Ohashi, N. Yodoya, E. Fujii, K. Ishikura, S. Tateno, S. Sato, T. Suzuki, T. Higaki, M. Iwamoto, M. Yoshinaga, M. Nagashima, and N. Sumitomo. 2014. Circumstances and outcomes of out-of-hospital cardiac arrest in elementary and middle school students in the era of public-access defibrillation. Circulation 78(3):701-707.
Moczygemba, J., and S. H. Fenton. 2012. Lessons learned from an ICD-10-CM clinical documentation pilot study. Perspectives in Health Information Management 9(Winter):1-6.
Moon, S., B. J. Bobrow, T. F. Vadeboncoeur, W. Kortuem, M. Kisakye, C. Sasson, U. Stolz, and D. W. Spaite. 2014. Disparities in bystander CPR provision and survival from out-of-hospital cardiac arrest according to neighborhood ethnicity. American Journal of Emergency Medicine 32(9):1041-1045.
Moriwaki, Y., Y. Tahara, M. Iwashita, T. Kosuge, and N. Suzuki. 2014. Risky locations for out-of-hospital cardiopulmonary arrest in a typical urban city. Journal of Emergencies, Trauma, and Shock 7(4):285-294.
Morrison, L. J., G. Nichol, T. D. Rea, J. Christenson, C. W. Callaway, S. Stephens, and R. G. Pirrallo. 2008. Rationale, development and implementation of the Resuscitation Outcomes Consortium Epistry—Cardiac Arrest. Resuscitation 78(2):161-169.
Morrison, L. J., R. W. Neumar, J. L. Zimmerman, M. S. Link, L. K. Newby, P. W. McMullan, Jr., T. V. Hoek, C. C. Halverson, L. Doering, M. A. Peberdy, and D. P. Edelson. 2013. Strategies for improving survival after in-hospital cardiac arrest in the United States: 2013 consensus recommendations: A consensus statement from the American Heart Association. Circulation 127(14):1538-1563.
Murakami, Y., T. Iwami, T. Kitamura, C. Nishiyama, T. Nishiuchi, Y. Hayashi, and T. Kawamura. 2014. Outcomes of out-of-hospital cardiac arrest by public location in the public-access defibrillation era. Journal of the American Heart Association 3(2):e000533.
Nadkarni, V. M., G. L. Larkin, M. A. Peberdy, S. M. Carey, W. Kaye, M. E. Mancini, G. Nichol, T. Lane-Truitt, J. Potts, J. P. Ornato, and R. A. Berg. 2006. First documented rhythm and clinical outcome from in-hospital cardiac arrest among children and adults. Journal of the American Medical Association 295(1):50-57.
Nagao, K., E. Tachibana, T. Yagi, N. Yonemoto, M. Takayama, H. Nonogi, S. Shirai, and T. Kimura. 2013. Relation between time interval from collapse to return of spontaneous circulation and neurologically intact survival for out-of-hospital cardiac arrest. Circulation 128(22 Suppl):A154.
Nehme, Z., E. Andrew, J. E. Bray, P. Cameron, S. Bernard, I. T. Meredith, and K. Smith. 2014a. The significance of pre-arrest factors in out-of-hospital cardiac arrests witnessed by emergency medical services: A report from the Victorian Ambulance Cardiac Arrest Registry. Resuscitation 88:35-42.
Nehme, Z., E. Andrew, P. Cameron, J. E. Bray, S. Bernard, I. T. Meredith, and K. Smith. 2014b. Population density predicts outcome from out-of-hospital cardiac arrest in Victoria, Australia. Medical Journal of Australia 200(8):471-475.
NEMSIS (National Emergency Medical Service Information System). 2013. Goals and Objectives. http://www.nemsis.org/theProject/whatIsNEMSIS/goalsAndObjectives.html (accessed June 23, 2015).
Neumar, R. W., J. M. Barnhart, R. A. Berg, P. S. Chan, R. G. Geocadin, R. V. Luepker, L. K. Newby, M. R. Sayre, and G. Nichol. 2011. Implementation strategies for improving survival after out-of-hospital cardiac arrest in the United States: Consensus recommendations from the 2009 American Heart Association Cardiac Arrest Survival Summit. Circulation 123(24):2898-2910.
Nichol, G. E. 2014. HeartMap Dynamic AED Registry. http://grantome.com/grant/NIH/U01-FD004933-02 (accessed June 10, 2015).
Nichol, G., E. Thomas, C. W. Callaway, J. Hedges, J. L. Powell, T. P. Aufderheide, T. Rea, R. Lowe, T. Brown, J. Dreyer, D. Davis, A. Idris, and I. Stiell. 2008a. Regional variation in out-of-hospital cardiac arrest incidence and outcome. Journal of the American Medical Association 300(12):1423-1431.
Nichol, G., J. Rumsfeld, B. Eigel, B. S. Abella, D. Labarthe, Y. Hong, R. E. O’Connor, V. N. Mosesso, R. A. Berg, and M. L. Weisfeldt. 2008b. Essential features of designating out-of-hospital cardiac arrest as a reportable event: A scientific statement from the American Heart Association Emergency Cardiovascular Care Committee; Council on Cardiopulmonary, Perioperative, and Critical Care; Council on Cardiovascular Nursing; Council on Clinical Cardiology; and Quality of Care and Outcomes Research Interdisciplinary Working Group. Circulation 117(17):2299-2308.
NIH (National Institutes of Health). 2015. Notice of intent to publish a funding opportunity for a clinical trials network for emergency care research: regional clinical centers (U24). http://grants.nih.gov/grants/guide/noticefiles/NOT-NS-15-021.html (accessed May 29, 2015).
NIPH (Norwegian Institute of Public Health). 2014. Norwegian Cardiovascular Disease Registry. http://www.fhi.no/eway/default.aspx?pid=240&trg=MainContent_6898&Main_6664=6898:0:25,7847:1:0:0:::0:0&MainContent_6898=6706:0:25,9011:1:0:0:::0:0 (accessed June 10, 2015).
Noheria, A., C. Teodorescu, A. Uy-Evanado, K. Reinier, R. Mariani, K. Gunson, J. Jui, and S. S. Chugh. 2013. Distinctive profile of sudden cardiac arrest in middle-aged vs. older adults: A community-based study. International Journal of Cardiology 168(4):3495-3499.
OHCAR (National Out-of-Hospital Cardiac Arrest Register). 2014. Sixth annual report. http://www.phecit.ie/Images/PHECC/Publications%20and%20Media/Other%20Publications/5.1%20OHCAR%206th%20Annual%20Report.pdf (accessed June 19, 2015).
Ong, M. E., S. D. Shin, H. Tanaka, M. H. Ma, P. Khruekarnchana, N. Hisamuddin, R. Atilla, P. Middleton, K. Kajino, B. S. Leong, and M. N. Khan. 2011. Pan-Asian Resuscitation Outcomes Study (PAROS): Rationale, methodology, and implementation. Academic Emergency Medicine 18(8):890-897.
Park, C. B., S. D. Shin, G. J. Suh, K. O. Ahn, W. C. Cha, K. J. Song, S. J. Kim, E. J. Lee, and M. E. Ong. 2010. Pediatric out-of-hospital cardiac arrest in Korea: A nationwide population-based study. Resuscitation 81(5):512-517.
Perkins, G. D., I. G. Jacobs, V. M. Nadkarni, R. A. Berg, F. Bhanji, D. Biarent, L. L. Bossaert, S. J. Brett, D. Chamberlain, A R. de Caen, C. D. Deakin, J. C. Finn, J-T. Grasner, M F. Hazinski, T. Iwami, R. W. Koster, S. H. Lim, M. H-M. Ma, B. F. McNally, P. T. Morley, L. J. Morrison, K. G. Monsieurs, W. Montgomery, G. Nichol, K. Okada, M. E. H. Ong, A. H. Travers, J. P. Nolan, for the Utstein Collaborators. 2014. ILCOR consensus statement: Cardiac arrest and cardiopulmonary resuscitation outcome reports: Update of the Utstein resuscitation registry templates for out-of-hospital cardiac arrest. A Statement for healthcare professionals from a task force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian and New Zealand Council on Resuscitation, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Council of Southern Africa, Resuscitation Council of Asia); and the American Heart Association Emergency Cardiovascular Care Committee and the Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation. Circulation. http://www.ncbi.nlm.nih.gov/pubmed/25391522 (accessed June 19, 2015).
Rajan, S., M. Wissenberg, F. Folke, C. M. Hansen, F. K. Lippert, P. Weeke, L. Karlsson, K. B. Sondergaard, K. Kragholm, E. F. Christensen, S. L. Nielsen, L. Kober, G. H. Gislason, and C. Torp-Pedersen. 2014. Out-of-hospital cardiac arrests in children and adolescents: Incidences, outcomes, and household socioeconomic status. Resuscitation 88C:12-19.
Rea, T. D., M. S. Eisenberg, G. Sinibaldi, and R. D. White. 2004. Incidence of EMS-treated out-of-hospital cardiac arrest in the United States. Resuscitation 63(1):17-24.
Reinier, K., E. Thomas, D. L. Andrusiek, T. P. Aufderheide, S. C. Brooks, C. W. Callaway, P. E. Pepe, T. D. Rea, R. H. Schmicker, C. Vaillancourt, S. S. Chugh; Resuscitation Outcomes Consortium Investigators. 2011. Socioeconomic status and incidence of sudden cardiac arrest. Canadian Medical Association Journal 183(15):1705-1712.
Ro, Y. S., S. D. Shin, K. J. Song, E. J. Lee, J. Y. Kim, K. O. Ahn, S. P. Chung, Y. T. Kim, S. O. Hong, J.-A. Choi, S. O. Hwang, D. J. Oh, C. B. Park, G. J. Suh, S.-I. Cho, and S. S. Hwang. 2013. A trend in epidemiology and outcomes of out-of-hospital cardiac arrest by urbanization level: A nationwide observational study from 2006 to 2010 in South Korea. Resuscitation 84(5):547-557.
Safdar, B., U. Stolz, I. G. Stiell, D. C. Cone, B. J. Bobrow, M. deBoehr, J. Dreyer, J. Maloney, and D. W. Spaite. 2014. Differential survival for men and women from out-of-hospital cardiac arrest varies by age: Results from the OPALS study. Academic Emergency Medicine 21(12):1503-1511.
Sasson, C., M. A. Rogers, J. Dahl, and A. L. Kellermann. 2010. Predictors of survival from out-of-hospital cardiac arrest: A systematic review and metaanalysis. Circulation: Cardiovascular Quality and Outcomes 3(1):63-81.
Sasson, C., D. J. Magid, P. Chan, E. D. Root, B. F. McNally, A. L. Kellermann, and J. S. Haukoos. 2012. Association of neighborhood characteristics with bystander-initiated CPR. New England Journal of Medicine 367(17):1607-1615.
Sasson, C., J. S. Haukoos, C. Bond, M. Rabe, S. H. Colbert, R. King, M. Sayre, and M. Heisler. 2013. Barriers and facilitators to learning and performing cardiopulmonary resuscitation in neighborhoods with low bystander cardiopulmonary resuscitation prevalence and high rates of cardiac arrest in Columbus, OH. Circulation: Cardiovascular Quality and Outcomes 6(5):550-558.
Sasson, C., J. S. Haukoos, L. Ben-Youssef, L. Ramirez, S. Bull, B. Eigel, D. J. Maqid, and R. Padilla. 2014. Barriers to calling 911 and learning and performing cardiopulmonary resuscitation for residents of primarily Latino, high-risk neighborhoods in Denver, Colorado. Annals of Emergency Medicine 65(5):545-552.
Seder, D. B., N. Patel, J. McPherson, P. McMullan, K. B. Kern, B. Unger, S. Nanda, M. Hacobian, M. B. Kelley, N. Nielsen, J. Dziodzio, and M. Mooney. 2014. Geriatric experience following cardiac arrest at six interventional cardiology centers in the United States 2006-2011: Interplay of age, do-not-resuscitate order, and outcomes. Critical Care Medicine 42(2):289-295.
Shippee, T. P., K. F. Ferraro, and R. J. Thorpe. 2011. Racial disparity in access to cardiac intensive care over 20 years. Ethnicity and Health 16(2):145-165.
Simmons, A., R. Pimentel, and D. Lakkireddy. 2012. Sudden cardiac death in women. Reviews in Cardiovascular Medicine 13(1):e37-e42.
Skolarus, L. E., J. B. Murphy, M. A. Zimmerman, S. Bailey, S. Fowlkes, D. L. Brown, L. D. Lisabeth, E. Greenberg, and L. B. Morgenstern. 2013. Individual and community determinants of calling 911 for stroke among African Americans in an urban community. Circulation: Cardiovascular Quality and Outcomes 6:278-283.
Stecker, E. C., K. Reinier, E. Marijon, K. Narayanan, C. Teodorescu, A. Uy-Evanado, K. Gunson, J. Jui, and S. S. Chugh. 2014. Public health burden of sudden cardiac death in the United States. Circulation: Arrhythmia and Electrophysiology 7(2):212-217.
Stromsoe, A., L. Svensson, A. Claesson, J. Lindkvist, A. Lundstrom, and J. Herlitz. 2011. Association between population density and reported incidence, characteristics and outcome after out-of-hospital cardiac arrest in Sweden. Resuscitation 82(10):1307-1313.
Sugarman, J., C. Sitlani, D. Andrusiek, T. Aufderheide, E. M. Bulger, D. P. Davis, D. B. Hoyt, A. Idris, J. D. Kerby, J. Powell, T. Schmidt, A. S. Slutsky, G. Sopko, S. Stephens, C. Williams, G. Nichol, Resuscitation Outcomes Consortium Investigators. 2009. Is the enrollment of racial and ethnic minorities in research in the emergency setting equitable? Resuscitation 80(6):644-649.
Topjian, A. A., A. R. Localio, R. A. Berg, E. A. Alessandrini, P. A. Meaney, P. E. Pepe, G. L. Larkin, M. A. Peberdy, L. B. Becker, and V. M. Nadkarni. 2010. Women of child-bearing age have better inhospital cardiac arrest survival outcomes than do equal-aged men. Critical Care Medicine 38(5):1254-1260.
Trzeciak, S., A. E. Jones, J. H. Kilgannon, B. M. Fuller, B. W. Roberts, J. E. Parrillo, and J. T. Farrar. 2009. Outcome measures utilized in clinical trials of interventions for post-cardiac arrest syndrome: A systematic review. Resuscitation 80(6):617-623.
VACAR (Victorian Ambulance Cardiac Arrest Registry). 2012. VACAR 2011-2012 Annual Report. http://www.ambulance.vic.gov.au/Media/docs/vacar-annualreport2011-12-910ad17b-3f51-4418-8bf7-351beb8f9f84-0.pdf (accessed June 11, 2015).
Vadeboncoeur, T., B. J. Bobrow, L. Clark, K. B. Kern, A. B. Sanders, R. A. Berg, and G. A. Ewy. 2007. The Save Hearts in Arizona Registry and Education (SHARE) program: Who is performing CPR and where are they doing it? Resuscitation 75(1):68-75.
Vadeboncoeur, T. F., P. B. Richman, M. Darkoh, V. Chikani, L. Clark, and B. J. Bobrow. 2008. Bystander cardiopulmonary resuscitation for out-of-hospital cardiac arrest in the Hispanic vs. the non-Hispanic populations. American Journal of Emergency Medicine 26:655-660.
Vellano, K., A. Crouch, M. Rajdev, and B. McNally. 2015. Cardiac Arrest Registry to Enhance Survival (CARES) report on the public health burden of out-of-hospital cardiac arrest. Paper commissioned by the Committee on Treatment of Cardiac Arrest: Current Status and Future Directions http://www.iom.edu/~/media/Files/Report%20Files/2015/CARES.pdf (accessed June 30, 2015).
Watts, J., J. D. Cowden, A. P. Cupertino, M. D. Dowd, and C. Kennedy. 2011. 911 (nueve once): Spanish-speaking parents’ perspectives on prehospital emergency care for children. Journal of Immigrant and Minority Health 13:526-532.
Weisfeldt, M. L., S. Everson-Stewart, C. Sitlani, T. Rea, T. P. Aufderheide, D. L. Atkins, B. Bigham, S. C. Brooks, C. Foerster, R. Gray, J. P. Ornato, J. Powell, P. J. Kudenchuk, and L. J. Morrison. 2011. Ventricular tachyarrhythmias after cardiac arrest in public versus at home. New England Journal of Medicine 364(4):313-321.
Wissenberg, M., F. K. Lippert, F. Folke, P. Weeke, C. M. Hansen, E. F. Christensen, and C. Torp-Pedersen. 2013. Association of national initiatives
to improve cardiac arrest management with rates of bystander intervention and patient survival after out-of-hospital cardiac arrest. Journal of the American Medical Association 310(13):1377-1384.
Wissenberg, M., C. M. Hansen, F. Folke, F. K. Lippert, P. Weeke, L. Karlsson, S. Rajan, K. B. Sondergaard, K. Kragholm, E. F. Christensen, S. L. Nielsen, L. Kober, G. H. Gislason, and C. Torp-Pedersen. 2014. Survival after out-of-hospital cardiac arrest in relation to sex: A nationwide registry-based study. Resuscitation 85(9):1212-1218.
Yang, H. J., Kim, G. W., Kim, H., Cho, J. S., Rho, T. H., Yoon, H. D., and NEDIS-CA Consortium. 2015. Epidemiology and outcomes in out-of-hospital cardiac arrest: A report from the NEDIS-based cardiac arrest registry in Korea. Journal of Korean Medical Science 30(1):95-103.
Zheng, Z. J., J. B. Croft, W. H. Giles, and G. A. Mensah. 2001. Sudden cardiac death in the United States, 1989 to 1998. Circulation 104(18):2158-2163.
Zive, D., K. Koprowicz, T. Schmidt, I. Stiell, G. Sears, L. Van Ottingham, A. Idris, S. Stephens, M. Daya, and ROC Investigators. 2011. Variation in out-of-hospital cardiac arrest resuscitation and transport practices in the Resuscitation Outcomes Consortium: ROC Epistry–Cardiac Arrest. Resuscitation 82(3):277-284.