Hyperacute Prediction of Targeted Temperature Management Outcome After Cardiac Arrest
Hsu, J.; Kim, H.; Gong, K.; Azad, T. D.; Stevens, R. D.
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AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSTargeted temperature management (TTM) has been associated with greater likelihood of neurological recovery among comatose survivors of cardiac arrest. However, the efficacy of TTM is not consistently observed, possibly due to heterogeneity of therapeutic response. The aim of this study is to determine if models leveraging multi-modal data available in the first 12 hours after ICU admission (hyperacute phase) can predict short-term outcome after TTM. MethodsAdult patients receiving TTM after cardiac arrest were selected from a multicenter ICU database. Predictive features were extracted from clinical, physiologic, and laboratory data available in the hyperacute phase. Primary endpoints were survival and favorable neurological outcome, determined as the ability to follow commands (motor Glasgow Coma Scale [mGCS] of 6) upon discharge. Three machine learning (ML) algorithms were trained: generalized linear models (GLM), random forest (RF), and gradient boosting (XG). Models with optimal features from forward selection were 10-fold cross-validated and resampled 10 times. ResultsData were available on 310 cardiac arrest patients who received TTM, of whom 183 survived and 123 had favorable neurological outcome. The GLM performed best, with an area under the receiver operating characteristic curve (AUROC) of 0.86 {+/-} 0.04, sensitivity 0.75 {+/-} 0.09, and specificity 0.77 {+/-} 0.07 for the prediction of survival and an AUROC of 0.85 {+/-} 0.03, sensitivity 0.71 {+/-} 0.10, and specificity 0.80 {+/-} 0.12 for the prediction of favorable neurological outcome. Features most predictive of both endpoints included lower serum chloride concentration, higher serum pH, and greater neutrophil counts. ConclusionIn patients receiving TTM after cardiac arrest, short-term outcomes can be accurately discriminated using ML applied to data routinely collected in the first 12 hours after ICU admission. With validation, hyperacute prediction could enable personalized approach to clinical decision-making in the post-cardiac arrest setting.
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