  Patients resuscitated from cardiac arrest (CA) face a high risk of
neurological disability and death, however pragmatic methods are lacking for
accurate and reliable prognostication. The aim of this study was to build
computational models to predict post-CA outcome by leveraging high-dimensional
patient data available early after admission to the intensive care unit (ICU).
We hypothesized that model performance could be enhanced by integrating
physiological time series (PTS) data and by training machine learning (ML)
classifiers. We compared three models integrating features extracted from the
electronic health records (EHR) alone, features derived from PTS collected in
the first 24hrs after ICU admission (PTS24), and models integrating PTS24 and
EHR. Outcomes of interest were survival and neurological outcome at ICU
discharge. Combined EHR-PTS24 models had higher discrimination (area under the
receiver operating characteristic curve [AUC]) than models which used either
EHR or PTS24 alone, for the prediction of survival (AUC 0.85, 0.80 and 0.68
respectively) and neurological outcome (0.87, 0.83 and 0.78). The best ML
classifier achieved higher discrimination than the reference logistic
regression model (APACHE III) for survival (AUC 0.85 vs 0.70) and neurological
outcome prediction (AUC 0.87 vs 0.75). Feature analysis revealed previously
unknown factors to be associated with post-CA recovery. Results attest to the
effectiveness of ML models for post-CA predictive modeling and suggest that PTS
recorded in very early phase after resuscitation encode short-term outcome
probabilities.
