Curr Cardiol Rep (2014) 16:457 DOI 10.1007/s11886-013-0457-0
ISCHEMIC HEART DISEASE (D MUKHERJEE, SECTION EDITOR)
Using Risk Prediction Tools in Survivors of In-hospital Cardiac Arrest Saket Girotra & Brahmajee K. Nallamothu & Paul S. Chan
# Springer Science+Business Media New York 2014
Abstract In-hospital cardiac arrests are common and associated with poor outcomes. Predicting the likelihood of favorable neurological survival following resuscitation from an in-hospital cardiac arrest could provide important information for physicians and families. In this article, we review the literature regarding predictors of survival following inhospital cardiac arrest. Specifically, we describe the Cardiac Arrest Survival Postresuscitation In-hospital (CASPRI) score that was recently developed and validated using data from the Get With the Guidelines-Resuscitation registry. The CASPRI score includes 11 predictor variables: age, initial cardiac arrest rhythm, defibrillation time, baseline neurological status, duration of resuscitation, mechanical ventilation, renal insufficiency, hepatic insufficiency, sepsis, malignancy, and hypotension. The score is simple to use at the bedside, has excellent discrimination and calibration, and provides robust estimates
This article is part of the Topical Collection on Ischemic Heart Disease S. Girotra (*) Department of Internal Medicine, Division of Cardiovascular Diseases, University of Iowa Carver College of Medicine, 200 Hawkins Drive, Suite 4427 RCP, Iowa City, IA 52246, USA e-mail: [email protected] B. K. Nallamothu Department of Internal Medicine, Division of Cardiovascular Diseases, University of Michigan Medical School, 1500 E Medical Center Drive, SPC 5869, Ann Arbor, MI 48109, USA e-mail: [email protected] P. S. Chan Department of Medicine, Division of Cardiovascular Diseases, University of Missouri-Kansas City, Kansas City, MO 64110, USA e-mail: [email protected] P. S. Chan St. Luke’s Mid America Heart Institute, 4401 Wornall Rd, Fifth Floor, Kansas City, MO 64111, USA
of the probability of favorable neurological survival after an in-hospital cardiac arrest. Thus, CASPRI may be valuable in establishing expectations by physicians and families in the critical period after these high-risk events. Keywords In-hospital cardiac arrest . Resuscitation . Survival . Risk prediction tools
Introduction In-hospital cardiac arrests are common and associated with significant morbidity and mortality. Nearly 200,000 patients experience an in-hospital cardiac arrest every year in the United States [1]. The annual incidence of in-hospital cardiac arrest is estimated at 2.73 events per 1000 admissions, and according to some estimates, the incidence may be increasing [1–3]. Although survival and neurological outcomes among cardiac arrest victims have improved in the past decade, mortality remains high [4••]. Nearly 80 % of in-hospital cardiac arrest victims do not survive to hospital discharge and more than 25 % of survivors suffer permanent neurological disability at discharge. Significant hospital resources and personnel are required to provide care to in-hospital cardiac arrest victims many of whom may require nursing home, and long-term care facilities following discharge [3]. Thus, inhospital cardiac arrest constitutes a significant burden on the society. Importantly, deaths due to in-hospital cardiac arrest occur in two distinct phases. Approximately 50 % of in-hospital cardiac arrest victims die during the initial event, as they do not achieve return of spontaneous circulation (ROSC) [5]. Among those who survive the initial event (i.e., achieve ROSC), many remain at an elevated risk of dying or neurological disability due to whole body ischemia from the event, reperfusion-mediated damage or the underlying pathological
457, Page 2 of 8
processes that led to cardiac arrest [6]. Such patients usually remain critically ill requiring prolonged intensive care, ventilator and circulatory support, and may need to undergo diagnostic and therapeutic procedures (e.g., cardiac catheterization). Despite the high intensity of care, a significant proportion of patients who achieve ROSC may not survive to discharge. Among those who do survive these events, many experience permanent neurological disability. Predicting the likelihood of survival with good neurological outcome during the latter phase would be of particular value to clinicians while discussing overall prognosis with patients’ families, and managing their expectations regarding the need for additional therapies and possible outcomes. An effective prediction tool would empower patients and their families as they navigate difficult decisions regarding treatment intensity. In this paper, we review factors associated with survival following in-hospital cardiac arrest and then describe the development of the Cardiac Arrest Survival Postresuscitation In-hospital (CASPRI) risk score that was recently developed and validated using data from the Get With the Guidelines-Resuscitation registry.
Predictors of In-Hospital Cardiac Arrest Outcomes Survival from an in-hospital cardiac arrest depends on a number of factors related to both the patient and the cardiac arrest. Patient Variables Age. Age-related decline in physiological reserve in multiple organ systems increases the susceptibility to whole body ischemia that accompanies a cardiac arrest. While increasing age has been associated with worse survival following an inhospital cardiac arrest in some studies [7–11], others have shown no association between age and in-hospital cardiac arrest survival [12–14]. Some of the conflicting results may in part be due to differences between studies with regards to definition of cardiac arrest, inclusion criteria, and study methodology. Moreover, the effect of age on in-hospital cardiac arrest survival may be confounded by selection bias - older patients are less likely to receive CPR, and therefore those who receive CPR may not be representative of older patients in general. Gender & Race. A recent study showed that black patients with ventricular fibrillation and pulseless ventricular tachycardia were 27 % less likely to survive an in-hospital CPR compared to white patients [15]. However, black-white differences in in-hospital cardiac arrest survival were largely due to black patients receiving care at poor quality hospitals compared to white patients, rather than an inherent susceptibility
Curr Cardiol Rep (2014) 16:457
to worse outcomes among blacks. Women of childbearing age (15-44 years) are more likely than comparably aged men to survive in-hospital cardiac arrest, whereas no gender-based differences in outcomes have been observed for older patients [16]. Co-morbidities. Pre-existing and concurrent medical conditions greatly influence patient survival and neurological outcome following a cardiac arrest. Presence of hypotension, sepsis, renal failure, malignancy, stroke, and homebound lifestyle have been associated with worse survival following inhospital cardiac arrest in multiple studies [12, 14, 17••, 18, 19]. Similarly, interventions in place at the time of cardiac arrest such as use of mechanical ventilation, and vasopressors identify patients who are critically ill prior to the arrest and therefore have a lower likelihood of survival [12, 17••, 18]. Acute myocardial infarction, on the other hand, is associated with a favorable prognosis because revascularization can potentially remove the underlying substrate for cardiac arrest in this setting. Moreover, cardiac arrests in the setting are often due to ventricular fibrillation (VF) or pulseless ventricular tachycardia (VT), rhythms, which can be more effectively treated with defibrillation compared to non-shockable rhythms [19]. Cardiac Arrest Characteristics Initial Rhythm. Survival after in-hospital cardiac arrest is 4-5 fold higher in patients with VF or pulseless VT compared to patients with asystole or pulseless electrical activity (PEA) [4••, 5]. This is because VF and pulseless VT can be promptly treated with defibrillation. Moreover, VF and VT usually occur in the setting of cardiac ischemia, which may be amenable to revascularization as compared to asystole and PEA, which usually occur in patients with severe underlying medical illness (e.g., hypoxia, sepsis, etc.) that may not be readily reversible. Studies suggest that 20-25 % of all in-hospital cardiac arrests are due to VF or pulseless VT [4••]. Prompt Resuscitation. The importance of early cardiopulmonary resuscitation (CPR) and timely defibrillation in patients with VF and pulseless VT has been well recognized. Survival is markedly higher in patients who receive CPR within 1 minute of collapse [20], and VF and pulseless VT patients who receive defibrillation within 2 minutes [21]. Delays in initiation of CPR and defibrillation (when appropriate) may also account for worse outcomes observed with in-hospital cardiac arrest events during nights and weekends [22]. Duration of Resuscitation. Studies have repeatedly shown that patients who require longer resuscitation have a lower likelihood of survival following an in-hospital cardiac arrest [7, 11, 12, 18, 23, 24•]. Resuscitation duration of > 10-
Curr Cardiol Rep (2014) 16:457
15 minutes has been found to be an independent predictor of mortality in patients with in-hospital cardiac arrests [11, 12, 18]. This is due to increased risk of neurological and end organ injury with whole body ischemia and hypoperfusion that accompanies a prolonged resuscitation effort. Event Location. Patients admitted to an intensive care unit or a monitored unit are usually sicker compared to patients admitted to a general hospital ward and therefore expected to have worse outcomes following an in-hospital cardiac arrest. However, closer monitoring, greater availability of skilled personnel, immediate availability of code carts, etc. increase the likelihood of survival from an in-hospital cardiac arrest in these locations due to prompt initiation of CPR, as has been borne out in multiple studies [9, 13, 25].
Earlier Risk Models to Predict In-Hospital Cardiac Arrest Survival In 1983, Bedell et al. conducted a prospective study of 294 inhospital cardiac arrest patients at a single university hospital and identified several independent predictors of survival. These included pre-arrest variables (hypotension [blood pressure 15 minutes, intubation) and post-cardiac arrest variables (coma and need for pressors). Similar findings have been noted in multiple other studies [7–11, 13, 14, 18, 19]. However, very few studies have translated their models into risk prediction tools for use at the bedside. George et al. developed the prearrest morbidity index (PAM), and Ebell developed the Prognosis After Resuscitation (PAR) score based on information on pre-arrest variables only [18, 19]. Both the scores were developed in order to identify patients who would be unlikely to survive should they have a cardiac arrest, so that resuscitation efforts could be avoided in such patients The PAM score is based on 15 pre-arrest variables that can be summed together to yield a composite score (range 0 to 25), while the PAR score is based on seven variables (range -2 to 28). Higher scores indicate worse likelihood of survival for both scores. Using a cutoff of nine or higher to identify non-survivors, both scores had a high specificity (~100 %) but very poor sensitivity (40,000 initial survivors of in-hospital cardiac arrest at>500 hospitals in the large, multi-center Get With The Guidelines-Resuscitation (GWTG-Resuscitation) registry. A brief description of GWTG-Resuscitation follows. Get With the Guidelines-Resuscitation Registry Formerly known as the National Registry of Cardiopulmonary Resuscitation, GWTG-Resuscitation is the largest registry of in-hospital cardiac arrests in the world [28]. Since its inception in 2000, the registry has enrolled nearly 200,000 patients at approximately 600 hospitals. Data collection in the registry is based on strict definitions according to the Utstein template [29]. Moreover, data collection practices are standardized across participating sites, and there is careful oversight to ensure completeness and accuracy of the submitted data. The development of the GWTG-Resuscitation registry represents a major advance in promoting research in the field of in-
457, Page 4 of 8
hospital resuscitation and in facilitating the development and validation of robust models for risk prediction. Study Design The study used data from 42,957 patients from 551 GWTGResuscitation hospitals who were at least 18 years or older, had a cardiac arrest in an inpatient ward or an intensive care unit, and survived the initial cardiac arrest with achievement of ROSC for at least 20 minutes. The primary outcome of the study was favorable neurological survival to discharge. Neurological status among survivors was assessed at the time of discharge using previously developed CPC score [30]. A CPC score of 1 was used to describe patients with mild to no neurological disability; 2, moderate neurological disability; 3, severe disability; 4, coma or vegetative state and 5, brain death. Favorable neurological survival at discharge was classified as CPC score of 1 or 2. Model Development The authors screened 37 variables as candidate predictors for the study outcome. These included demographics (age and sex), location of cardiac arrest (intensive care unit [ICU], telemetry unit, non-monitored unit), initial cardiac arrest rhythm (VF, pulseless VT, asystole, PEA), calendar year, time (work hours: 7:00 AM to 10:59 PM vs. after hours: 11:00 PM to 6:59 AM) and day (weekday vs. weekend) of cardiac arrest, neurological status before cardiac arrest (pre-arrest CPC score), comorbidities or medical conditions present prior to cardiac arrest (heart failure; myocardial infarction; diabetes mellitus; renal, hepatic, or respiratory insufficiency; baseline evidence of motor, cognitive, or functional deficits [central nervous system depression]; acute stroke; acute non-stroke neurologic disorder; pneumonia; hypotension; sepsis; major trauma; metabolic or electrolyte abnormality; metastatic or hematologic malignant disease), and interventions already in place at the time of cardiac arrest (mechanical ventilation, intravenous vasopressor medications, pulmonary artery catheter, intra-aortic balloon pump, hemodialysis). In addition, information on key cardiac resuscitation variables that would be known following successful resuscitation was also included. These were (1) duration of resuscitation to achieve return of spontaneous circulation and (2) time to defibrillation. Race was not included because prior studies have found that racial differences in survival after in-hospital cardiac arrest are largely mediated by differences in comorbidity burden and hospital care quality for blacks and whites [15]. The authors divided the study sample randomly into a derivation cohort (two-thirds of the sample, n=28,629) and a validation cohort (one-third of the sample, n=14,328). Within the derivation cohort, they used multivariate logistic regression analyses to identify significant predictors of favorable
Curr Cardiol Rep (2014) 16:457
neurological survival to discharge. They used generalized estimation equation with an exchangeable correlation matrix to account for clustering of patients at a hospital. After identifying all variables that were significant in a multivariable model (full model), the authors developed a parsimonious model by sequentially eliminating variables that did not provide incremental prognostic information to the model. Model discrimination was assessed using the c-statistic and calibration was evaluated using the Hosmer-Lemeshow goodness of fit test, and observed vs. predicted plots. Finally, coefficients for individual predictor variables from the parsimonious model were rounded to derive the CASPRI risk score, which was validated in the validation cohort. Study Results Baseline characteristics were similar in the derivation and the validation cohort (Tables 1 and 2). The mean age was 66 years, 56 % were male, and 19 % were black. In general, the prevalence of co-morbidities was high – 45 % had respiratory insufficiency, 37 % had renal failure, 34 % had diabetes and 18 % had sepsis. Pre-arrest CPC score of 3 or higher was present in 18 % of patients. Nearly 85 % of all study patients were located in a monitored setting at the time of cardiac arrest (intensive care unit [60 %], telemetry unit [25 %]), and onethird of patients were on mechanical ventilators or receiving vasopressors prior to the onset of cardiac arrest. The initial cardiac arrest rhythm was VF or pulseless VT in 26 % of the patients. Patients were resuscitated for an average of 11.0 minutes before ROSC was achieved. Rate of favorable neurological survival was also similar in the derivation cohort and the validation cohort. Using multivariable logistic regression, the authors identified 20 independent predictors in the derivation cohort, from which they eliminated nine variables that did not incrementally improve model discrimination. The final model, which included 11 variables – age, initial cardiac arrest rhythm, pre-arrest CPC score, hospital location, duration of resuscitation, mechanical ventilation, renal insufficiency, hepatic insufficiency, sepsis, malignancy, and hypotension had a c-statistic of 0.802, which was similar to the c-statistic of the full model with all 20 predictor variables (0.808). The excellent discrimination of the model was also seen when it was tested in the independent validation cohort (c-statistic 0.80). The model also showed good calibration based on the Hosmer-Lemeshow test (P= 0.29) as well as close agreement between the observed and predicted rates of favorable neurological survival as shown in Fig. 1. The coefficients of the predictor variables were combined together to develop the CASPRI score, with weights assigned to each variable according to the strength of association with the outcome (Fig. 2). The score ranges from 0-100 with higher scores indicating worse outcome. The CASPRI score
Curr Cardiol Rep (2014) 16:457
Page 5 of 8, 457
Table 1 Patient and cardiac arrest characteristics for the derivation and validation cohortsa
Table 1 (continued) Characteristic
Characteristic
Derivation cohort Validation cohort (n=28,629) (n=14,328)
Derivation cohort Validation cohort (n=28,629) (n=14,328) Demographics Age, mean (SD), y Male sex Black race Preexisting conditions CPC score prior to arrest
66.0 (15.2) 16,046 (56.0) 5531 (19.3)
66.2 (15.4) 8081 (56.4) 2813 (19.6)
1 2 3 4 or 5 Missing Respiratory insufficiency Renal insufficiency Arrhythmia Diabetes mellitus Hypotension Heart failure prior to admission Heart failure this admission Myocardial infarction prior to admission Myocardial infarction this admission Metabolic or electrolyte abnormality Sepsis Pneumonia Metastatic or hematological malignancy Baseline depression in CNS function Hepatic insufficiency Acute CN nonstroke event Acute stroke Major trauma Characteristics of arrest Initial cardiac arrest rhythm Pulseless electrical activity Asystole VF Pulseless VT Duration of resuscitation, min Mean (SD) Median (IQR) Defibrillation time, mina
13,404 (52.8) 7346 (28.9) 3136 (12.4) 1498 (5.9) 3245 (11.3) 12,739 (44.5) 10,625 (37.1) 10,601 (37.0) 9606 (33.6) 8172 (28.5) 6792 (23.7) 5250 (18.3) 5250 (18.3)
6745 (52.9) 3704 (29.0) 1585 (12.4) 725 (5.7) 1569 (11.0) 6380 (44.5) 5319 (37.1) 5348 (37.3) 4892 (34.1) 4033 (28.1) 3387 (23.6) 2712 (18.9) 2712 (18.9)
5399 (18.9)
2668 (18.6)
5351 (18.7)
2708 (18.9)
5087 (17.8) 4408 (15.4) 3444 (12.0)
2552 (17.8) 2110 (14.7) 1721 (12.0)
3810 (13.3)
1867 (13.0)
2489 (8.7) 2388 (8.3) 1212 (4.2) 1002 (3.5)
1221 (8.5) 1172 (8.2) 622 (4.3) 486 (3.4)
11,853 (41.4) 9256 (32.3) 4497 (15.7) 3023 (10.6)
6001 (41.9) 4705 (32.8) 2130 (14.9) 1492 (10.4)
16.1 (17.1) 11.0 (6.0-21.0)
16.1 (18.5) 11.0 (6.0-21.0)
Mean (SD) Median (IQR) Hospital location
1.9 (3.2) 1.0 (0.0-2.0)
1.8 (3.0) 1.0 (0.0-2.0)
17,240 (60.2)
8617 (60.1)
Intensive care unit
No (%)
No (%)
Telemetry unit Non-monitored unit Time and day of arrest Night Weekend
7042 (24.6) 4347 (15.2)
3530 (24.6) 2181 (15.2)
9376 (32.8) 8940 (31.2)
4603 (32.1) 4337 (30.3)
Hospital-wide response 24,361 (85.1) activated Use of automated external 3478 (12.1) defibrillators Interventions in place prior to arrest Mechanical ventilation Intravenous vasopressors Intravenous antiarrhythmics Pulmonary artery catheter Hemodialysis Intra-aortic balloon pump Implantable cardioverter defibrillator
8955 (31.3) 8227 (28.7) 2242 (7.8) 1381 (4.8) 1207 (4.2) 556 (1.9) 492 (1.7)
12192 (85.1) 1743 (12.2)
4492 (31.4) 4116 (28.7) 1111 (7.8) 749 (5.2) 644 (4.5) 330 (2.3) 235 (1.6)
Abbreviations: CNS, central nervous system; CPC, cerebral performance category; IQR, interquartile range; SD, standard deviation; VF, ventricular fibrillation; VT, ventricular tachycardiaa Reproduced with permission from: Chan PS, Spertus JA, Krumholz HM, Berg RA, Li Y, Sasson C, Nallamothu BK. A validated prediction tool for initial survivors of inhospital cardiac arrest. Arch Intern Med. 2012;172:947-953 [17••]. b
for arrests due to VF or pulseless VT
accurately predicts the likelihood of favorable neurological survival over a wide range of probabilities; patients in the top decile (CASPRI score 0-9) had a 70.7 % mean probability of favorable neurological survival compared to patients in the bottom decile (CASPRI score≥28) who had a 2.8 % mean probability of favorable neurological survival (Fig. 1). Figure 2 also shows the mean probabilities of survival for each 5-point increase in the CASPRI score. The CASPRI score represents a major advance over previous risk models as it has been developed exclusively for initial survivors of in-hospital cardiac arrest patients and provides prognostication when it is most likely to be useful—after a patient has been successfully resuscitated from the cardiac arrest. Second, the model includes pertinent pre-arrest and intra-arrest variables that strongly correlate with patient outcomes, and has excellent discrimination and calibration properties. Third, the model is unusual in that it accurately predicts outcomes over a wide range of survival probabilities. Fourth, the translation of the model into an easy to use risk score makes it attractive for use at the bedside. While the CASPRI score should not be used as the sole criterion to recommend or withhold specific treatments from patients, we believe that by
457, Page 6 of 8
Curr Cardiol Rep (2014) 16:457
Table 2 Multivariate predictors of favorable neurological survival to dischargea,b OR (95 % CI) Predictors
Total t statistic
Age group, years ≤ 49 50-59 60-69 70-79 ≥ 80 Initial arrest rhythm VF or pulseless VT Time to defibrillation 1 or 2 min Time to defibrillation 3 min Time to defibrillation 4 or 5 min Time to defibrillation≥6 min Asystole Pulseless electrical activity
24.0
Derivation cohort (n=28,629)
Validation cohort (n=14,328)
1.22 (1.11-1.35) 1.11 (1.01-1.23) 1 (reference) 0.76 (0.70-0.82) 0.57 (0.52-0.63)
1.16 (1.01-1.33) 1.13 (0.98-1.30) 1 (reference) 0.73 (0.64-0.83) 0.60 (0.52-0.69)
1 (reference) 1.19 (0.97-1.45) 0.81 (0.66-0.98) 0.64 (0.53-0.78) 0.31 (0.28-0.35) 0.32 (0.30-0.35)
1 (reference) 0.95 (0.71-1.30) 0.80 (0.60-1.06) 0.80 (0.60-1.06) 0.31 (0.27-0.35) 0.31 (0.28-0.35)
1 (reference) 0.70 (0.65-0.75) 0.17 (0.14-0.20) 0.15 (0.12-0.21)
1 (reference) 0.72 (0.64-0.81) 0.18 (0.14-0.22) 0.20 (0.15-0.26)
1 (reference) 1.70 (1.53-1.89) 1.37 (1.23-1.54)
1 (reference) 1.76 (1.53-2.02) 1.47 (1.27-1.71)
1 (reference) 0.84 (0.70-1.01) 0.52 (0.44-0.63) 0.37 (0.31-0.45) 0.28 (0.23-0.34) 0.33 (0.27-0.40) 0.31 (0.25-0.38) 0.22 (0.18-0.27)
1 (reference) 0.92 (0.75-1.12) 0.63 (0.52-0.77) 0.48 (0.39-0.59) 0.34 (0.27-0.43) 0.40 (0.31-0.52) 0.32 (0.24-0.42) 0.25 (0.20-0.32)
0.50 (0.46-0.55) 0.65 (0.61-0.69) 0.48 (0.41-0.56) 0.50 (0.45-0.56) 0.46 (0.41-0.52) 0.60 (0.54-0.66) 0.80
0.46 (0.40-0.52) 0.62 (0.57-0.70) 0.36 (0.28-0.45) 0.60 (0.53-0.69) 0.44 (0.37-0.52) 0.66 (0.59-0.75) 0.80
57.1
Pre-arrest CPC score 1 2 3 4 Hospital location Non-monitored unit Telemetry unit Intensive care unit Duration of resuscitation, min 1 2-4 5-9 10-14 15-19 20-24 25-29 ≥ 30
51.2
Mechanical ventilation Renal insufficiency Hepatic insufficiency Sepsis Malignant disease Hypotension Model C statistic
15.7 13.1 9.0 12.7 13.1 10.7
15.2
67.6
Abbreviations: CPC, cerebral performance category; deviation; VF, ventricular fibrillation; VT, ventricular tachycardiaa Reproduced with permission from: Chan PS, Spertus JA, Krumholz HM, Berg RA, Li Y, Sasson C, Nallamothu BK. A validated prediction tool for initial survivors of in-hospital cardiac arrest. Arch Intern Med. 2012;172:947-953 [17••]. b
Adjusted estimates for model predictors of favorable neurological survival to discharge in the derivation and the validation cohorts. The t statistic provides the contribution of each variable to the overall model for the derivation cohort
Curr Cardiol Rep (2014) 16:457
Page 7 of 8, 457
Fig. 1 The Cardiac Arrest Survival Postresuscitation In-hospital (CASPRI) scorecard and nomogram for favorable neurological survival. For this in-hospital cardiac arrest risk score, points for each variable are determined, and a summary score is obtained. The corresponding likelihood of surviving to hospital discharge without severe neurological disability is determined from the risk table or plot. CPC indicates cerebral performance category, VF/VT ventricular fibrillation or ventricular tachycardia. (Reproduced with permission from: Chan PS, Spertus JA, Krumholz HM, Berg RA, Li Y, Sasson C, Nallamothu BK. A validated prediction tool for initial survivors of in-hospital cardiac arrest. Arch Intern Med. 2012;172:947-953) [17••]
providing concrete probabilities of favorable neurological survival, the CASPRI score can enhance communication between patients’ families and physicians. Providing critical information regarding overall prognosis, the CASPRI score can inform discussions between physicians and family discussions regarding intensity of treatment. In that regard, the CASPRI score has the potential to empower patients and their families and provide them with greater autonomy as they evaluate difficult decisions regarding treatment intensity in light of patient’s overall wishes and advance directives.
Fig. 2 Comparison of predicted vs. observed outcome rate for the validation cohort. Each data point represents a decile of risk for the outcome of favorable neurological survival to discharge. CASPRI indicates Cardiac Arrest Survival Postresuscitation In-hospital. (Reproduced with permission from: Chan PS, Spertus JA, Krumholz HM, Berg RA, Li Y, Sasson C, Nallamothu BK. A validated prediction tool for initial survivors of in-hospital cardiac arrest. Arch Intern Med. 2012;172:947953) [17••]
communicate the CASPRI score to patients and their families and the real-world impact of implementing this tool on outcomes. Acknowledgments NHLBI.
Paul S. Chan has received grant support from
Conclusion
Compliance with Ethics Guidelines
The CASPRI score is a valid and robust tool for predicting favorable neurological survival in successfully resuscitated patients with an in-hospital cardiac arrest. By providing accurate prognostic information, the CASPRI score can enhance communication between patients’ families and physicians, and facilitate their discussions regarding intensity of care. Further work is required to understand how best to
Conflict of Interest Saket Girotra declares that he has no conflict of interest. Brahmajee K. Nallamothu declares that he has no conflict of interest. Paul S. Chan has been a consultant for Optum Rx and American Heart Association. Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.
457, Page 8 of 8
References Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance 1.
Merchant RM, Yang L, Becker LB, et al. Incidence of treated cardiac arrest in hospitalized patients in the United States. Crit Care Med. 2011;39:2401–6. 2. Ehlenbach WJ, Barnato AE, Curtis JR, et al. Epidemiologic study of in-hospital cardiopulmonary resuscitation in the elderly. N Engl J Med. 2009;361:22–31. 3. Kazaure HS, Roman SA, Sosa JA. Epidemiology and Outcomes of In-Hospital Cardiopulmonary Resuscitation in the United States, 2000-2009. Resuscitation. 2013;84:1255–60. 4.•• Girotra S, Nallamothu BK, Spertus JA, Li Y, Krumholz HM, Chan PS. Trends in survival after in-hospital cardiac arrest. N Engl J Med. 2012;367:1912–20. This study describes temporal trends in survival and neurological outcomes among in-hospital cardiac arrest victims during the past decade.. 5. Nadkarni VM, Larkin GL, Peberdy MA, et al. First documented rhythm and clinical outcome from in-hospital cardiac arrest among children and adults. JAMA. 2006;295:50–7. 6. Peberdy MA, Callaway CW, Neumar RW, et al. Part 9: post-cardiac arrest care: 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2010;122:S768–86. Scientific statement from the American Heart Association that summarizes the evidence regarding post-resuscitation care.. 7. Cooper S, Janghorbani M, Cooper G. A decade of in-hospital resuscitation: outcomes and prediction of survival? Resuscitation. 2006;68:231–7. 8. Cooper S, Cade J. Predicting survival, in-hospital cardiac arrests: resuscitation survival variables and training effectiveness. Resuscitation. 1997;35:17–22. 9. Tunstall-Pedoe H, Bailey L, Chamberlain DA, Marsden AK, Ward ME, Zideman DA. Survey of 3765 cardiopulmonary resuscitations in British hospitals (the BRESUS Study): methods and overall results. BMJ. 1992;304:1347–51. 10. Skrifvars MB, Castren M, Nurmi J, Thoren AB, Aune S, Herlitz J. Do patient characteristics or factors at resuscitation influence longterm outcome in patients surviving to be discharged following inhospital cardiac arrest? J Intern Med. 2007;262:488–95. 11. Schultz SC, Cullinane DC, Pasquale MD, Magnant C, Evans SR. Predicting in-hospital mortality during cardiopulmonary resuscitation. Resuscitation. 1996;33:13–7. 12. Bedell SE, Delbanco TL, Cook EF, Epstein FH. Survival after cardiopulmonary resuscitation in the hospital. N Engl J Med. 1983;309:569–76. 13. Sandroni C, Ferro G, Santangelo S, et al. In-hospital cardiac arrest: survival depends mainly on the effectiveness of the emergency response. Resuscitation. 2004;62:291–7. 14. Ballew KA, Philbrick JT, Caven DE, Schorling JB. Predictors of survival following in-hospital cardiopulmonary resuscitation. A moving target. Arch Intern Med. 1994;154:2426–32. 15. Chan PS, Nichol G, Krumholz HM, et al. Racial differences in survival after in-hospital cardiac arrest. JAMA. 2009;302:1195– 201. This paper examines disparities in outcomes between black and white patients with in-hospital cardiac arrest due to ventricular
Curr Cardiol Rep (2014) 16:457 fibrillation or ventricular tachycardia, including the potential source of the disparties.. 16. Topjian AA, Localio AR, Berg RA, et al. Women of child-bearing age have better inhospital cardiac arrest survival outcomes than do equal-aged men. Crit Care Med. 2010;38:1254–60. 17.•• Chan PS, Spertus JA, Krumholz HM, et al. A validated prediction tool for initial survivors of in-hospital cardiac arrest. Arch Intern Med. 2012;172:947–53. This paper describes the development and validation of the Cardiac Arrest Survival Postresuscitation In-hospital (CASPRI) prediction tool - which was designed to predict the likelihood of survival without severe neurological disability in initial survivors of in-hospital cardiac arrest.. 18. George Jr AL, Folk 3rd BP, Crecelius PL, Campbell WB. Pre-arrest morbidity and other correlates of survival after in-hospital cardiopulmonary arrest. Am J Med. 1989;87:28–34. 19. Ebell MH. Prearrest predictors of survival following in-hospital cardiopulmonary resuscitation: a meta-analysis. J Fam Prac. 1992;34:551–8. 20. Herlitz J, Bang A, Alsen B, Aune S. Characteristics and outcome among patients suffering from in hospital cardiac arrest in relation to whether the arrest took place during office hours. Resuscitation. 2002;53:127–33. 21. Chan PS, Krumholz HM, Nichol G, Nallamothu BK. Delayed time to defibrillation after in-hospital cardiac arrest. N Engl J Med. 2008;358:9–17. 22. Peberdy MA, Ornato JP, Larkin GL, et al. Survival from in-hospital cardiac arrest during nights and weekends. JAMA. 2008;299:785– 92. This paper showed that outcomes in patients with in-hospital cardiac arrest were between daytime hours and night, as well as weekdays and weekend. 23. Stemmler EJ. Cardiac resuscitation. A 1-year study of patients resuscitated within a university hospital. Ann Intern Med. 1965;63:613–8. 24.• Goldberger ZD, Chan PS, Berg RA, et al. Duration of resuscitation efforts and survival after in-hospital cardiac arrest: an observational study. Lancet. 2012. doi:10.1016/S0140-6736(12)60862-9. This paper showed that hospitals that have a tendency to continue resuscitation efforts for a longer period (as measured by the duration of resuscitation among non-survivors) had better overall in-hospital cardiac arrest survival.. 25. Herlitz J, Bang A, Aune S, Ekstrom L, Lundstrom G, Holmberg S. Characteristics and outcome among patients suffering in-hospital cardiac arrest in monitored and non-monitored areas. Resuscitation. 2001;48:125–35. 26. Ebell MH, Kruse JA, Smith M, Novak J, Drader-Wilcox J. Failure of three decision rules to predict the outcome of in-hospital cardiopulmonary resuscitation. Med Dec Making: Intern J Soc Med Dec Making. 1997;17:171–7. 27. O'Keeffe S, Ebell MH. Prediction of failure to survive following inhospital cardiopulmonary resuscitation: comparison of two predictive instruments. Resuscitation. 1994;28:21–5. 28. Peberdy MA, Kaye W, Ornato JP, et al. Cardiopulmonary resuscitation of adults in the hospital: a report of 14720 cardiac arrests from the National Registry of Cardiopulmonary Resuscitation. Resuscitation. 2003;58:297–308. 29. Cummins RO, Chamberlain D, Hazinski MF, et al. Recommended guidelines for reviewing, reporting, and conducting research on inhospital resuscitation: the in-hospital 'Utstein style'. American Heart Association. Circulation. 1997;95:2213–39. 30. Jennett B, Bond M. Assessment of outcome after severe brain damage. Lancet. 1975;1:480–4.
ISCHEMIC HEART DISEASE (D MUKHERJEE, SECTION EDITOR)
Using Risk Prediction Tools in Survivors of In-hospital Cardiac Arrest Saket Girotra & Brahmajee K. Nallamothu & Paul S. Chan
# Springer Science+Business Media New York 2014
Abstract In-hospital cardiac arrests are common and associated with poor outcomes. Predicting the likelihood of favorable neurological survival following resuscitation from an in-hospital cardiac arrest could provide important information for physicians and families. In this article, we review the literature regarding predictors of survival following inhospital cardiac arrest. Specifically, we describe the Cardiac Arrest Survival Postresuscitation In-hospital (CASPRI) score that was recently developed and validated using data from the Get With the Guidelines-Resuscitation registry. The CASPRI score includes 11 predictor variables: age, initial cardiac arrest rhythm, defibrillation time, baseline neurological status, duration of resuscitation, mechanical ventilation, renal insufficiency, hepatic insufficiency, sepsis, malignancy, and hypotension. The score is simple to use at the bedside, has excellent discrimination and calibration, and provides robust estimates
This article is part of the Topical Collection on Ischemic Heart Disease S. Girotra (*) Department of Internal Medicine, Division of Cardiovascular Diseases, University of Iowa Carver College of Medicine, 200 Hawkins Drive, Suite 4427 RCP, Iowa City, IA 52246, USA e-mail: [email protected] B. K. Nallamothu Department of Internal Medicine, Division of Cardiovascular Diseases, University of Michigan Medical School, 1500 E Medical Center Drive, SPC 5869, Ann Arbor, MI 48109, USA e-mail: [email protected] P. S. Chan Department of Medicine, Division of Cardiovascular Diseases, University of Missouri-Kansas City, Kansas City, MO 64110, USA e-mail: [email protected] P. S. Chan St. Luke’s Mid America Heart Institute, 4401 Wornall Rd, Fifth Floor, Kansas City, MO 64111, USA
of the probability of favorable neurological survival after an in-hospital cardiac arrest. Thus, CASPRI may be valuable in establishing expectations by physicians and families in the critical period after these high-risk events. Keywords In-hospital cardiac arrest . Resuscitation . Survival . Risk prediction tools
Introduction In-hospital cardiac arrests are common and associated with significant morbidity and mortality. Nearly 200,000 patients experience an in-hospital cardiac arrest every year in the United States [1]. The annual incidence of in-hospital cardiac arrest is estimated at 2.73 events per 1000 admissions, and according to some estimates, the incidence may be increasing [1–3]. Although survival and neurological outcomes among cardiac arrest victims have improved in the past decade, mortality remains high [4••]. Nearly 80 % of in-hospital cardiac arrest victims do not survive to hospital discharge and more than 25 % of survivors suffer permanent neurological disability at discharge. Significant hospital resources and personnel are required to provide care to in-hospital cardiac arrest victims many of whom may require nursing home, and long-term care facilities following discharge [3]. Thus, inhospital cardiac arrest constitutes a significant burden on the society. Importantly, deaths due to in-hospital cardiac arrest occur in two distinct phases. Approximately 50 % of in-hospital cardiac arrest victims die during the initial event, as they do not achieve return of spontaneous circulation (ROSC) [5]. Among those who survive the initial event (i.e., achieve ROSC), many remain at an elevated risk of dying or neurological disability due to whole body ischemia from the event, reperfusion-mediated damage or the underlying pathological
457, Page 2 of 8
processes that led to cardiac arrest [6]. Such patients usually remain critically ill requiring prolonged intensive care, ventilator and circulatory support, and may need to undergo diagnostic and therapeutic procedures (e.g., cardiac catheterization). Despite the high intensity of care, a significant proportion of patients who achieve ROSC may not survive to discharge. Among those who do survive these events, many experience permanent neurological disability. Predicting the likelihood of survival with good neurological outcome during the latter phase would be of particular value to clinicians while discussing overall prognosis with patients’ families, and managing their expectations regarding the need for additional therapies and possible outcomes. An effective prediction tool would empower patients and their families as they navigate difficult decisions regarding treatment intensity. In this paper, we review factors associated with survival following in-hospital cardiac arrest and then describe the development of the Cardiac Arrest Survival Postresuscitation In-hospital (CASPRI) risk score that was recently developed and validated using data from the Get With the Guidelines-Resuscitation registry.
Predictors of In-Hospital Cardiac Arrest Outcomes Survival from an in-hospital cardiac arrest depends on a number of factors related to both the patient and the cardiac arrest. Patient Variables Age. Age-related decline in physiological reserve in multiple organ systems increases the susceptibility to whole body ischemia that accompanies a cardiac arrest. While increasing age has been associated with worse survival following an inhospital cardiac arrest in some studies [7–11], others have shown no association between age and in-hospital cardiac arrest survival [12–14]. Some of the conflicting results may in part be due to differences between studies with regards to definition of cardiac arrest, inclusion criteria, and study methodology. Moreover, the effect of age on in-hospital cardiac arrest survival may be confounded by selection bias - older patients are less likely to receive CPR, and therefore those who receive CPR may not be representative of older patients in general. Gender & Race. A recent study showed that black patients with ventricular fibrillation and pulseless ventricular tachycardia were 27 % less likely to survive an in-hospital CPR compared to white patients [15]. However, black-white differences in in-hospital cardiac arrest survival were largely due to black patients receiving care at poor quality hospitals compared to white patients, rather than an inherent susceptibility
Curr Cardiol Rep (2014) 16:457
to worse outcomes among blacks. Women of childbearing age (15-44 years) are more likely than comparably aged men to survive in-hospital cardiac arrest, whereas no gender-based differences in outcomes have been observed for older patients [16]. Co-morbidities. Pre-existing and concurrent medical conditions greatly influence patient survival and neurological outcome following a cardiac arrest. Presence of hypotension, sepsis, renal failure, malignancy, stroke, and homebound lifestyle have been associated with worse survival following inhospital cardiac arrest in multiple studies [12, 14, 17••, 18, 19]. Similarly, interventions in place at the time of cardiac arrest such as use of mechanical ventilation, and vasopressors identify patients who are critically ill prior to the arrest and therefore have a lower likelihood of survival [12, 17••, 18]. Acute myocardial infarction, on the other hand, is associated with a favorable prognosis because revascularization can potentially remove the underlying substrate for cardiac arrest in this setting. Moreover, cardiac arrests in the setting are often due to ventricular fibrillation (VF) or pulseless ventricular tachycardia (VT), rhythms, which can be more effectively treated with defibrillation compared to non-shockable rhythms [19]. Cardiac Arrest Characteristics Initial Rhythm. Survival after in-hospital cardiac arrest is 4-5 fold higher in patients with VF or pulseless VT compared to patients with asystole or pulseless electrical activity (PEA) [4••, 5]. This is because VF and pulseless VT can be promptly treated with defibrillation. Moreover, VF and VT usually occur in the setting of cardiac ischemia, which may be amenable to revascularization as compared to asystole and PEA, which usually occur in patients with severe underlying medical illness (e.g., hypoxia, sepsis, etc.) that may not be readily reversible. Studies suggest that 20-25 % of all in-hospital cardiac arrests are due to VF or pulseless VT [4••]. Prompt Resuscitation. The importance of early cardiopulmonary resuscitation (CPR) and timely defibrillation in patients with VF and pulseless VT has been well recognized. Survival is markedly higher in patients who receive CPR within 1 minute of collapse [20], and VF and pulseless VT patients who receive defibrillation within 2 minutes [21]. Delays in initiation of CPR and defibrillation (when appropriate) may also account for worse outcomes observed with in-hospital cardiac arrest events during nights and weekends [22]. Duration of Resuscitation. Studies have repeatedly shown that patients who require longer resuscitation have a lower likelihood of survival following an in-hospital cardiac arrest [7, 11, 12, 18, 23, 24•]. Resuscitation duration of > 10-
Curr Cardiol Rep (2014) 16:457
15 minutes has been found to be an independent predictor of mortality in patients with in-hospital cardiac arrests [11, 12, 18]. This is due to increased risk of neurological and end organ injury with whole body ischemia and hypoperfusion that accompanies a prolonged resuscitation effort. Event Location. Patients admitted to an intensive care unit or a monitored unit are usually sicker compared to patients admitted to a general hospital ward and therefore expected to have worse outcomes following an in-hospital cardiac arrest. However, closer monitoring, greater availability of skilled personnel, immediate availability of code carts, etc. increase the likelihood of survival from an in-hospital cardiac arrest in these locations due to prompt initiation of CPR, as has been borne out in multiple studies [9, 13, 25].
Earlier Risk Models to Predict In-Hospital Cardiac Arrest Survival In 1983, Bedell et al. conducted a prospective study of 294 inhospital cardiac arrest patients at a single university hospital and identified several independent predictors of survival. These included pre-arrest variables (hypotension [blood pressure 15 minutes, intubation) and post-cardiac arrest variables (coma and need for pressors). Similar findings have been noted in multiple other studies [7–11, 13, 14, 18, 19]. However, very few studies have translated their models into risk prediction tools for use at the bedside. George et al. developed the prearrest morbidity index (PAM), and Ebell developed the Prognosis After Resuscitation (PAR) score based on information on pre-arrest variables only [18, 19]. Both the scores were developed in order to identify patients who would be unlikely to survive should they have a cardiac arrest, so that resuscitation efforts could be avoided in such patients The PAM score is based on 15 pre-arrest variables that can be summed together to yield a composite score (range 0 to 25), while the PAR score is based on seven variables (range -2 to 28). Higher scores indicate worse likelihood of survival for both scores. Using a cutoff of nine or higher to identify non-survivors, both scores had a high specificity (~100 %) but very poor sensitivity (40,000 initial survivors of in-hospital cardiac arrest at>500 hospitals in the large, multi-center Get With The Guidelines-Resuscitation (GWTG-Resuscitation) registry. A brief description of GWTG-Resuscitation follows. Get With the Guidelines-Resuscitation Registry Formerly known as the National Registry of Cardiopulmonary Resuscitation, GWTG-Resuscitation is the largest registry of in-hospital cardiac arrests in the world [28]. Since its inception in 2000, the registry has enrolled nearly 200,000 patients at approximately 600 hospitals. Data collection in the registry is based on strict definitions according to the Utstein template [29]. Moreover, data collection practices are standardized across participating sites, and there is careful oversight to ensure completeness and accuracy of the submitted data. The development of the GWTG-Resuscitation registry represents a major advance in promoting research in the field of in-
457, Page 4 of 8
hospital resuscitation and in facilitating the development and validation of robust models for risk prediction. Study Design The study used data from 42,957 patients from 551 GWTGResuscitation hospitals who were at least 18 years or older, had a cardiac arrest in an inpatient ward or an intensive care unit, and survived the initial cardiac arrest with achievement of ROSC for at least 20 minutes. The primary outcome of the study was favorable neurological survival to discharge. Neurological status among survivors was assessed at the time of discharge using previously developed CPC score [30]. A CPC score of 1 was used to describe patients with mild to no neurological disability; 2, moderate neurological disability; 3, severe disability; 4, coma or vegetative state and 5, brain death. Favorable neurological survival at discharge was classified as CPC score of 1 or 2. Model Development The authors screened 37 variables as candidate predictors for the study outcome. These included demographics (age and sex), location of cardiac arrest (intensive care unit [ICU], telemetry unit, non-monitored unit), initial cardiac arrest rhythm (VF, pulseless VT, asystole, PEA), calendar year, time (work hours: 7:00 AM to 10:59 PM vs. after hours: 11:00 PM to 6:59 AM) and day (weekday vs. weekend) of cardiac arrest, neurological status before cardiac arrest (pre-arrest CPC score), comorbidities or medical conditions present prior to cardiac arrest (heart failure; myocardial infarction; diabetes mellitus; renal, hepatic, or respiratory insufficiency; baseline evidence of motor, cognitive, or functional deficits [central nervous system depression]; acute stroke; acute non-stroke neurologic disorder; pneumonia; hypotension; sepsis; major trauma; metabolic or electrolyte abnormality; metastatic or hematologic malignant disease), and interventions already in place at the time of cardiac arrest (mechanical ventilation, intravenous vasopressor medications, pulmonary artery catheter, intra-aortic balloon pump, hemodialysis). In addition, information on key cardiac resuscitation variables that would be known following successful resuscitation was also included. These were (1) duration of resuscitation to achieve return of spontaneous circulation and (2) time to defibrillation. Race was not included because prior studies have found that racial differences in survival after in-hospital cardiac arrest are largely mediated by differences in comorbidity burden and hospital care quality for blacks and whites [15]. The authors divided the study sample randomly into a derivation cohort (two-thirds of the sample, n=28,629) and a validation cohort (one-third of the sample, n=14,328). Within the derivation cohort, they used multivariate logistic regression analyses to identify significant predictors of favorable
Curr Cardiol Rep (2014) 16:457
neurological survival to discharge. They used generalized estimation equation with an exchangeable correlation matrix to account for clustering of patients at a hospital. After identifying all variables that were significant in a multivariable model (full model), the authors developed a parsimonious model by sequentially eliminating variables that did not provide incremental prognostic information to the model. Model discrimination was assessed using the c-statistic and calibration was evaluated using the Hosmer-Lemeshow goodness of fit test, and observed vs. predicted plots. Finally, coefficients for individual predictor variables from the parsimonious model were rounded to derive the CASPRI risk score, which was validated in the validation cohort. Study Results Baseline characteristics were similar in the derivation and the validation cohort (Tables 1 and 2). The mean age was 66 years, 56 % were male, and 19 % were black. In general, the prevalence of co-morbidities was high – 45 % had respiratory insufficiency, 37 % had renal failure, 34 % had diabetes and 18 % had sepsis. Pre-arrest CPC score of 3 or higher was present in 18 % of patients. Nearly 85 % of all study patients were located in a monitored setting at the time of cardiac arrest (intensive care unit [60 %], telemetry unit [25 %]), and onethird of patients were on mechanical ventilators or receiving vasopressors prior to the onset of cardiac arrest. The initial cardiac arrest rhythm was VF or pulseless VT in 26 % of the patients. Patients were resuscitated for an average of 11.0 minutes before ROSC was achieved. Rate of favorable neurological survival was also similar in the derivation cohort and the validation cohort. Using multivariable logistic regression, the authors identified 20 independent predictors in the derivation cohort, from which they eliminated nine variables that did not incrementally improve model discrimination. The final model, which included 11 variables – age, initial cardiac arrest rhythm, pre-arrest CPC score, hospital location, duration of resuscitation, mechanical ventilation, renal insufficiency, hepatic insufficiency, sepsis, malignancy, and hypotension had a c-statistic of 0.802, which was similar to the c-statistic of the full model with all 20 predictor variables (0.808). The excellent discrimination of the model was also seen when it was tested in the independent validation cohort (c-statistic 0.80). The model also showed good calibration based on the Hosmer-Lemeshow test (P= 0.29) as well as close agreement between the observed and predicted rates of favorable neurological survival as shown in Fig. 1. The coefficients of the predictor variables were combined together to develop the CASPRI score, with weights assigned to each variable according to the strength of association with the outcome (Fig. 2). The score ranges from 0-100 with higher scores indicating worse outcome. The CASPRI score
Curr Cardiol Rep (2014) 16:457
Page 5 of 8, 457
Table 1 Patient and cardiac arrest characteristics for the derivation and validation cohortsa
Table 1 (continued) Characteristic
Characteristic
Derivation cohort Validation cohort (n=28,629) (n=14,328)
Derivation cohort Validation cohort (n=28,629) (n=14,328) Demographics Age, mean (SD), y Male sex Black race Preexisting conditions CPC score prior to arrest
66.0 (15.2) 16,046 (56.0) 5531 (19.3)
66.2 (15.4) 8081 (56.4) 2813 (19.6)
1 2 3 4 or 5 Missing Respiratory insufficiency Renal insufficiency Arrhythmia Diabetes mellitus Hypotension Heart failure prior to admission Heart failure this admission Myocardial infarction prior to admission Myocardial infarction this admission Metabolic or electrolyte abnormality Sepsis Pneumonia Metastatic or hematological malignancy Baseline depression in CNS function Hepatic insufficiency Acute CN nonstroke event Acute stroke Major trauma Characteristics of arrest Initial cardiac arrest rhythm Pulseless electrical activity Asystole VF Pulseless VT Duration of resuscitation, min Mean (SD) Median (IQR) Defibrillation time, mina
13,404 (52.8) 7346 (28.9) 3136 (12.4) 1498 (5.9) 3245 (11.3) 12,739 (44.5) 10,625 (37.1) 10,601 (37.0) 9606 (33.6) 8172 (28.5) 6792 (23.7) 5250 (18.3) 5250 (18.3)
6745 (52.9) 3704 (29.0) 1585 (12.4) 725 (5.7) 1569 (11.0) 6380 (44.5) 5319 (37.1) 5348 (37.3) 4892 (34.1) 4033 (28.1) 3387 (23.6) 2712 (18.9) 2712 (18.9)
5399 (18.9)
2668 (18.6)
5351 (18.7)
2708 (18.9)
5087 (17.8) 4408 (15.4) 3444 (12.0)
2552 (17.8) 2110 (14.7) 1721 (12.0)
3810 (13.3)
1867 (13.0)
2489 (8.7) 2388 (8.3) 1212 (4.2) 1002 (3.5)
1221 (8.5) 1172 (8.2) 622 (4.3) 486 (3.4)
11,853 (41.4) 9256 (32.3) 4497 (15.7) 3023 (10.6)
6001 (41.9) 4705 (32.8) 2130 (14.9) 1492 (10.4)
16.1 (17.1) 11.0 (6.0-21.0)
16.1 (18.5) 11.0 (6.0-21.0)
Mean (SD) Median (IQR) Hospital location
1.9 (3.2) 1.0 (0.0-2.0)
1.8 (3.0) 1.0 (0.0-2.0)
17,240 (60.2)
8617 (60.1)
Intensive care unit
No (%)
No (%)
Telemetry unit Non-monitored unit Time and day of arrest Night Weekend
7042 (24.6) 4347 (15.2)
3530 (24.6) 2181 (15.2)
9376 (32.8) 8940 (31.2)
4603 (32.1) 4337 (30.3)
Hospital-wide response 24,361 (85.1) activated Use of automated external 3478 (12.1) defibrillators Interventions in place prior to arrest Mechanical ventilation Intravenous vasopressors Intravenous antiarrhythmics Pulmonary artery catheter Hemodialysis Intra-aortic balloon pump Implantable cardioverter defibrillator
8955 (31.3) 8227 (28.7) 2242 (7.8) 1381 (4.8) 1207 (4.2) 556 (1.9) 492 (1.7)
12192 (85.1) 1743 (12.2)
4492 (31.4) 4116 (28.7) 1111 (7.8) 749 (5.2) 644 (4.5) 330 (2.3) 235 (1.6)
Abbreviations: CNS, central nervous system; CPC, cerebral performance category; IQR, interquartile range; SD, standard deviation; VF, ventricular fibrillation; VT, ventricular tachycardiaa Reproduced with permission from: Chan PS, Spertus JA, Krumholz HM, Berg RA, Li Y, Sasson C, Nallamothu BK. A validated prediction tool for initial survivors of inhospital cardiac arrest. Arch Intern Med. 2012;172:947-953 [17••]. b
for arrests due to VF or pulseless VT
accurately predicts the likelihood of favorable neurological survival over a wide range of probabilities; patients in the top decile (CASPRI score 0-9) had a 70.7 % mean probability of favorable neurological survival compared to patients in the bottom decile (CASPRI score≥28) who had a 2.8 % mean probability of favorable neurological survival (Fig. 1). Figure 2 also shows the mean probabilities of survival for each 5-point increase in the CASPRI score. The CASPRI score represents a major advance over previous risk models as it has been developed exclusively for initial survivors of in-hospital cardiac arrest patients and provides prognostication when it is most likely to be useful—after a patient has been successfully resuscitated from the cardiac arrest. Second, the model includes pertinent pre-arrest and intra-arrest variables that strongly correlate with patient outcomes, and has excellent discrimination and calibration properties. Third, the model is unusual in that it accurately predicts outcomes over a wide range of survival probabilities. Fourth, the translation of the model into an easy to use risk score makes it attractive for use at the bedside. While the CASPRI score should not be used as the sole criterion to recommend or withhold specific treatments from patients, we believe that by
457, Page 6 of 8
Curr Cardiol Rep (2014) 16:457
Table 2 Multivariate predictors of favorable neurological survival to dischargea,b OR (95 % CI) Predictors
Total t statistic
Age group, years ≤ 49 50-59 60-69 70-79 ≥ 80 Initial arrest rhythm VF or pulseless VT Time to defibrillation 1 or 2 min Time to defibrillation 3 min Time to defibrillation 4 or 5 min Time to defibrillation≥6 min Asystole Pulseless electrical activity
24.0
Derivation cohort (n=28,629)
Validation cohort (n=14,328)
1.22 (1.11-1.35) 1.11 (1.01-1.23) 1 (reference) 0.76 (0.70-0.82) 0.57 (0.52-0.63)
1.16 (1.01-1.33) 1.13 (0.98-1.30) 1 (reference) 0.73 (0.64-0.83) 0.60 (0.52-0.69)
1 (reference) 1.19 (0.97-1.45) 0.81 (0.66-0.98) 0.64 (0.53-0.78) 0.31 (0.28-0.35) 0.32 (0.30-0.35)
1 (reference) 0.95 (0.71-1.30) 0.80 (0.60-1.06) 0.80 (0.60-1.06) 0.31 (0.27-0.35) 0.31 (0.28-0.35)
1 (reference) 0.70 (0.65-0.75) 0.17 (0.14-0.20) 0.15 (0.12-0.21)
1 (reference) 0.72 (0.64-0.81) 0.18 (0.14-0.22) 0.20 (0.15-0.26)
1 (reference) 1.70 (1.53-1.89) 1.37 (1.23-1.54)
1 (reference) 1.76 (1.53-2.02) 1.47 (1.27-1.71)
1 (reference) 0.84 (0.70-1.01) 0.52 (0.44-0.63) 0.37 (0.31-0.45) 0.28 (0.23-0.34) 0.33 (0.27-0.40) 0.31 (0.25-0.38) 0.22 (0.18-0.27)
1 (reference) 0.92 (0.75-1.12) 0.63 (0.52-0.77) 0.48 (0.39-0.59) 0.34 (0.27-0.43) 0.40 (0.31-0.52) 0.32 (0.24-0.42) 0.25 (0.20-0.32)
0.50 (0.46-0.55) 0.65 (0.61-0.69) 0.48 (0.41-0.56) 0.50 (0.45-0.56) 0.46 (0.41-0.52) 0.60 (0.54-0.66) 0.80
0.46 (0.40-0.52) 0.62 (0.57-0.70) 0.36 (0.28-0.45) 0.60 (0.53-0.69) 0.44 (0.37-0.52) 0.66 (0.59-0.75) 0.80
57.1
Pre-arrest CPC score 1 2 3 4 Hospital location Non-monitored unit Telemetry unit Intensive care unit Duration of resuscitation, min 1 2-4 5-9 10-14 15-19 20-24 25-29 ≥ 30
51.2
Mechanical ventilation Renal insufficiency Hepatic insufficiency Sepsis Malignant disease Hypotension Model C statistic
15.7 13.1 9.0 12.7 13.1 10.7
15.2
67.6
Abbreviations: CPC, cerebral performance category; deviation; VF, ventricular fibrillation; VT, ventricular tachycardiaa Reproduced with permission from: Chan PS, Spertus JA, Krumholz HM, Berg RA, Li Y, Sasson C, Nallamothu BK. A validated prediction tool for initial survivors of in-hospital cardiac arrest. Arch Intern Med. 2012;172:947-953 [17••]. b
Adjusted estimates for model predictors of favorable neurological survival to discharge in the derivation and the validation cohorts. The t statistic provides the contribution of each variable to the overall model for the derivation cohort
Curr Cardiol Rep (2014) 16:457
Page 7 of 8, 457
Fig. 1 The Cardiac Arrest Survival Postresuscitation In-hospital (CASPRI) scorecard and nomogram for favorable neurological survival. For this in-hospital cardiac arrest risk score, points for each variable are determined, and a summary score is obtained. The corresponding likelihood of surviving to hospital discharge without severe neurological disability is determined from the risk table or plot. CPC indicates cerebral performance category, VF/VT ventricular fibrillation or ventricular tachycardia. (Reproduced with permission from: Chan PS, Spertus JA, Krumholz HM, Berg RA, Li Y, Sasson C, Nallamothu BK. A validated prediction tool for initial survivors of in-hospital cardiac arrest. Arch Intern Med. 2012;172:947-953) [17••]
providing concrete probabilities of favorable neurological survival, the CASPRI score can enhance communication between patients’ families and physicians. Providing critical information regarding overall prognosis, the CASPRI score can inform discussions between physicians and family discussions regarding intensity of treatment. In that regard, the CASPRI score has the potential to empower patients and their families and provide them with greater autonomy as they evaluate difficult decisions regarding treatment intensity in light of patient’s overall wishes and advance directives.
Fig. 2 Comparison of predicted vs. observed outcome rate for the validation cohort. Each data point represents a decile of risk for the outcome of favorable neurological survival to discharge. CASPRI indicates Cardiac Arrest Survival Postresuscitation In-hospital. (Reproduced with permission from: Chan PS, Spertus JA, Krumholz HM, Berg RA, Li Y, Sasson C, Nallamothu BK. A validated prediction tool for initial survivors of in-hospital cardiac arrest. Arch Intern Med. 2012;172:947953) [17••]
communicate the CASPRI score to patients and their families and the real-world impact of implementing this tool on outcomes. Acknowledgments NHLBI.
Paul S. Chan has received grant support from
Conclusion
Compliance with Ethics Guidelines
The CASPRI score is a valid and robust tool for predicting favorable neurological survival in successfully resuscitated patients with an in-hospital cardiac arrest. By providing accurate prognostic information, the CASPRI score can enhance communication between patients’ families and physicians, and facilitate their discussions regarding intensity of care. Further work is required to understand how best to
Conflict of Interest Saket Girotra declares that he has no conflict of interest. Brahmajee K. Nallamothu declares that he has no conflict of interest. Paul S. Chan has been a consultant for Optum Rx and American Heart Association. Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.
457, Page 8 of 8
References Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance 1.
Merchant RM, Yang L, Becker LB, et al. Incidence of treated cardiac arrest in hospitalized patients in the United States. Crit Care Med. 2011;39:2401–6. 2. Ehlenbach WJ, Barnato AE, Curtis JR, et al. Epidemiologic study of in-hospital cardiopulmonary resuscitation in the elderly. N Engl J Med. 2009;361:22–31. 3. Kazaure HS, Roman SA, Sosa JA. Epidemiology and Outcomes of In-Hospital Cardiopulmonary Resuscitation in the United States, 2000-2009. Resuscitation. 2013;84:1255–60. 4.•• Girotra S, Nallamothu BK, Spertus JA, Li Y, Krumholz HM, Chan PS. Trends in survival after in-hospital cardiac arrest. N Engl J Med. 2012;367:1912–20. This study describes temporal trends in survival and neurological outcomes among in-hospital cardiac arrest victims during the past decade.. 5. Nadkarni VM, Larkin GL, Peberdy MA, et al. First documented rhythm and clinical outcome from in-hospital cardiac arrest among children and adults. JAMA. 2006;295:50–7. 6. Peberdy MA, Callaway CW, Neumar RW, et al. Part 9: post-cardiac arrest care: 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2010;122:S768–86. Scientific statement from the American Heart Association that summarizes the evidence regarding post-resuscitation care.. 7. Cooper S, Janghorbani M, Cooper G. A decade of in-hospital resuscitation: outcomes and prediction of survival? Resuscitation. 2006;68:231–7. 8. Cooper S, Cade J. Predicting survival, in-hospital cardiac arrests: resuscitation survival variables and training effectiveness. Resuscitation. 1997;35:17–22. 9. Tunstall-Pedoe H, Bailey L, Chamberlain DA, Marsden AK, Ward ME, Zideman DA. Survey of 3765 cardiopulmonary resuscitations in British hospitals (the BRESUS Study): methods and overall results. BMJ. 1992;304:1347–51. 10. Skrifvars MB, Castren M, Nurmi J, Thoren AB, Aune S, Herlitz J. Do patient characteristics or factors at resuscitation influence longterm outcome in patients surviving to be discharged following inhospital cardiac arrest? J Intern Med. 2007;262:488–95. 11. Schultz SC, Cullinane DC, Pasquale MD, Magnant C, Evans SR. Predicting in-hospital mortality during cardiopulmonary resuscitation. Resuscitation. 1996;33:13–7. 12. Bedell SE, Delbanco TL, Cook EF, Epstein FH. Survival after cardiopulmonary resuscitation in the hospital. N Engl J Med. 1983;309:569–76. 13. Sandroni C, Ferro G, Santangelo S, et al. In-hospital cardiac arrest: survival depends mainly on the effectiveness of the emergency response. Resuscitation. 2004;62:291–7. 14. Ballew KA, Philbrick JT, Caven DE, Schorling JB. Predictors of survival following in-hospital cardiopulmonary resuscitation. A moving target. Arch Intern Med. 1994;154:2426–32. 15. Chan PS, Nichol G, Krumholz HM, et al. Racial differences in survival after in-hospital cardiac arrest. JAMA. 2009;302:1195– 201. This paper examines disparities in outcomes between black and white patients with in-hospital cardiac arrest due to ventricular
Curr Cardiol Rep (2014) 16:457 fibrillation or ventricular tachycardia, including the potential source of the disparties.. 16. Topjian AA, Localio AR, Berg RA, et al. Women of child-bearing age have better inhospital cardiac arrest survival outcomes than do equal-aged men. Crit Care Med. 2010;38:1254–60. 17.•• Chan PS, Spertus JA, Krumholz HM, et al. A validated prediction tool for initial survivors of in-hospital cardiac arrest. Arch Intern Med. 2012;172:947–53. This paper describes the development and validation of the Cardiac Arrest Survival Postresuscitation In-hospital (CASPRI) prediction tool - which was designed to predict the likelihood of survival without severe neurological disability in initial survivors of in-hospital cardiac arrest.. 18. George Jr AL, Folk 3rd BP, Crecelius PL, Campbell WB. Pre-arrest morbidity and other correlates of survival after in-hospital cardiopulmonary arrest. Am J Med. 1989;87:28–34. 19. Ebell MH. Prearrest predictors of survival following in-hospital cardiopulmonary resuscitation: a meta-analysis. J Fam Prac. 1992;34:551–8. 20. Herlitz J, Bang A, Alsen B, Aune S. Characteristics and outcome among patients suffering from in hospital cardiac arrest in relation to whether the arrest took place during office hours. Resuscitation. 2002;53:127–33. 21. Chan PS, Krumholz HM, Nichol G, Nallamothu BK. Delayed time to defibrillation after in-hospital cardiac arrest. N Engl J Med. 2008;358:9–17. 22. Peberdy MA, Ornato JP, Larkin GL, et al. Survival from in-hospital cardiac arrest during nights and weekends. JAMA. 2008;299:785– 92. This paper showed that outcomes in patients with in-hospital cardiac arrest were between daytime hours and night, as well as weekdays and weekend. 23. Stemmler EJ. Cardiac resuscitation. A 1-year study of patients resuscitated within a university hospital. Ann Intern Med. 1965;63:613–8. 24.• Goldberger ZD, Chan PS, Berg RA, et al. Duration of resuscitation efforts and survival after in-hospital cardiac arrest: an observational study. Lancet. 2012. doi:10.1016/S0140-6736(12)60862-9. This paper showed that hospitals that have a tendency to continue resuscitation efforts for a longer period (as measured by the duration of resuscitation among non-survivors) had better overall in-hospital cardiac arrest survival.. 25. Herlitz J, Bang A, Aune S, Ekstrom L, Lundstrom G, Holmberg S. Characteristics and outcome among patients suffering in-hospital cardiac arrest in monitored and non-monitored areas. Resuscitation. 2001;48:125–35. 26. Ebell MH, Kruse JA, Smith M, Novak J, Drader-Wilcox J. Failure of three decision rules to predict the outcome of in-hospital cardiopulmonary resuscitation. Med Dec Making: Intern J Soc Med Dec Making. 1997;17:171–7. 27. O'Keeffe S, Ebell MH. Prediction of failure to survive following inhospital cardiopulmonary resuscitation: comparison of two predictive instruments. Resuscitation. 1994;28:21–5. 28. Peberdy MA, Kaye W, Ornato JP, et al. Cardiopulmonary resuscitation of adults in the hospital: a report of 14720 cardiac arrests from the National Registry of Cardiopulmonary Resuscitation. Resuscitation. 2003;58:297–308. 29. Cummins RO, Chamberlain D, Hazinski MF, et al. Recommended guidelines for reviewing, reporting, and conducting research on inhospital resuscitation: the in-hospital 'Utstein style'. American Heart Association. Circulation. 1997;95:2213–39. 30. Jennett B, Bond M. Assessment of outcome after severe brain damage. Lancet. 1975;1:480–4.
