Development and validation of early prediction for neurological outcome at 90 days after return of spontaneous circulation in out-of-hospital cardiac arrest

Abstract

Aim: To develop and validate a model for the early prediction of long-term neurological outcome in patients with non-traumatic out-of-hospital cardiac arrest (OHCA).

Methods: We analysed multicentre OHCA registry data of adult patients with non-traumatic OHCA who experienced return of spontaneous circulation (ROSC) and had been admitted to the intensive care unit between 2013 and 2017. We allocated 1329 (2013-2015) and 1025 patients (2016-2017) to the derivation and validation sets, respectively. The primary outcome was the dichotomized cerebral performance category (CPC) at 90 days, defined as good (CPC 1-2) or poor (CPC 3-5). We developed 2 models: model 1 included variables without laboratory data, and model 2 included variables with laboratory data available immediately after ROSC. Logistic regression with least absolute shrinkage and selection operator regularization was employed for model development. Measures of discrimination, accuracy, and calibration (C-statistics, Brier score, calibration plot, and net benefit) were assessed in the validation set.

Results: The C-statistic (95% confidence intervals) of models 1 and 2 in the validation set was 0.947 (0.930-0.964) and 0.950 (0.934-0.966), respectively. The Brier score of models 1 and 2 in the validation set was 0.0622 and 0.0606, respectively. The calibration plot showed that both models were well-calibrated to the observed outcome. Decision curve analysis indicated that model 2 was similar to model 1.

Conclusion: The prediction tool containing detailed in-hospital information showed good performance for predicting neurological outcome at 90 days immediately after ROSC in patients with OHCA.

Keywords: Cerebral performance category; Least absolute shrinkage and selection operator; Out-of-hospital cardiac arrest; Prediction; Prognostication.