Search

Objectives: Prognostication of neurologic status among survivors of in-hospital cardiac arrests remains a challenging task for physicians. Although models such as the Cardiac Arrest Survival Post-Resuscitation In-hospital score are useful for predicting neurologic outcomes, they were developed using traditional statistical techniques. In this study, we derive and compare the performance of several machine learning models with each other and with the Cardiac Arrest Survival Post-Resuscitation In-hospital score for predicting the likelihood of favorable neurologic outcomes among survivors of resuscitation.Design: Analysis of the Get With The Guidelines-Resuscitation registry.Setting: Seven-hundred fifty-five hospitals participating in Get With The Guidelines-Resuscitation from January 1, 2001, to January 28, 2017.Patients: Adult in-hospital cardiac arrest survivors.Interventions: None.Measurements and Main Results: Of 117,674 patients in our cohort, 28,409 (24%) had a favorable neurologic outcome, as defined as survival with a Cerebral Performance Category score of less than or equal to 2 at discharge. Using patient characteristics, pre-existing conditions, prearrest interventions, and periarrest variables, we constructed logistic regression, support vector machines, random forests, gradient boosted machines, and neural network machine learning models to predict favorable neurologic outcome. Events prior to October 20, 2009, were used for model derivation, and all subsequent events were used for validation. The gradient boosted machine predicted favorable neurologic status at discharge significantly better than the Cardiac Arrest Survival Post-Resuscitation In-hospital score (C-statistic: 0.81 vs 0.73; p < 0.001) and outperformed all other machine learning models in terms of discrimination, calibration, and accuracy measures. Variables that were consistently most important for prediction across all models were duration of arrest, initial cardiac arrest rhythm, admission Cerebral Performance Category score, and age.Conclusions: The gradient boosted machine algorithm was the most accurate for predicting favorable neurologic outcomes in in-hospital cardiac arrest survivors. Our results highlight the utility of machine learning for predicting neurologic outcomes in resuscitated patients. [ABSTRACT FROM AUTHOR]

Here, we investigate if adding longitudinal trends for pCART variables improves model performance. We recently developed pCART, a gradient-boosted machine (GBM) learning model to predict the risk of deterioration using vital signs and laboratory results from the electronic health record. The trend--based GBM model slightly outperformed pCART in predicting clinical deterioration events within the next 12 hours (AUC 0.85 vs. 0.84, P< 0.001). [Extracted from the article]

B Learning Objectives: b Mortality has decreased in the pediatric intensive care unit (PICU) setting with a concomitant rise in morbidity. Studies that have attempted to characterize these morbidities characteristically report a single outcome for a subset of PICU survivors with variable durations of follow-up often limited to one follow-up timepoint. [Extracted from the article]