A prediction model for good neurological outcome in successfully resuscitated out-of-hospital cardiac arrest patients

Abstract

Background: In the initial hours after out-of-hospital cardiac arrest (OHCA), it remains difficult to estimate whether the degree of post-ischemic brain damage will be compatible with long-term good neurological outcome. We aimed to construct prognostic models able to predict good neurological outcome of OHCA patients within 48h after CCU admission using variables that are bedside available. Methods: Based on prospectively gathered data, a retrospective data analysis was performed on 107 successfully resuscitated OHCA patients with a presumed cardiac cause of arrest. Targeted temperature management at 33°C was initiated at CCU admission. Prediction models for good neurological outcome (CPC1-2) at 180days post-CA were constructed at hour 1, 12, 24 and 48 after CCU admission. Following multiple imputation, variables were selected using the elastic-net method. Each imputed dataset was divided into training and validation sets (80% and 20% of patients, respectively). Logistic regression was fitted on training sets and prediction performance was evaluated on validation sets using misclassification rates. Results: The prediction model at hour 24 predicted good neurological outcome with the lowest misclassification rate (21.5%), using a cut-off probability of 0.55 (sensitivity=75%; specificity=82%). This model contained sex, age, diabetes status, initial rhythm, percutaneous coronary intervention, presence of a BIS 0 value, mean BIS value and lactate as predictive variables for good neurological outcome. Discussion: This study shows that good neurological outcome after OHCA can be reasonably predicted as early as 24h following ICU admission using parameters that are bedside available. These prediction models could identify patients who would benefit the most from intensive care.