Objective: Investigators in France have developed a risk score to predict death or poor neurologic outcome after out-of-hospital cardiac arrest. The aim of this study is to externally validate this score in an independent patient population in the United States.
Design: Retrospective, observational, cohort study.
Patients: Patients being admitted to the intensive care unit after out-of-hospital cardiac arrest.
Setting: Two geographically distinct tertiary care hospitals in the United States.
Measurements and Main Results: The primary end point was poor outcome, defined as either death or a Cerebral Performance Category score of 3–5. The secondary end point was all-cause mortality. Calibration was assessed by comparing the number of expected outcomes based on the logistic model of the French study with observed outcomes within this study using Hosmer-Lemeshow C test (goodness-of-fit). Discrimination was assessed by calculation of the area under the receiver operating characteristic curve. Of a total of 128 patients, 99 (77%) had a poor outcome, including 91 nonsurvivors (71%). The probability of poor neurologic outcome and mortality increased stepwise with increasing out-of-hospital cardiac arrest score. Graphic display of observed against predicted outcomes and goodness-of-fit test indicated good calibration of the score (p = .4). The score showed good discrimination for poor outcome (area under the receiving operating characteristic curve, 0.85; 95% confidence interval, 0.79–0.92) and for mortality (area under the receiving operating characteristic curve, 0.85; 95% confidence interval, 0.78–0.91). In patients with an out-of-hospital cardiac arrest score >40 points and >60 points, the positive predictive value for poor outcome was 97% and 100%, respectively.
Conclusions: This study found good calibration and high discrimination of the out-of-hospital cardiac arrest score in two geographically distinct patient populations in the United States. Particularly, this score had a high positive predictive value and performed well in identifying high-risk patients for poor outcomes.