Predicting respiratory decompensation in mechanically ventilated adult ICU patients

Yvette Tan; Michael Young; Akanksha Girish; Beini Hu; Zina Kurian; Joseph L Greenstein; Han Kim; Raimond L Winslow; James Fackler; Jules Bergmann

doi:10.3389/fphys.2023.1125991

Predicting respiratory decompensation in mechanically ventilated adult ICU patients

Front Physiol. 2023 Apr 14:14:1125991. doi: 10.3389/fphys.2023.1125991. eCollection 2023.

Authors

Affiliations

¹ Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States.
² Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.
³ Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.

Abstract

Introduction: Mechanical ventilation is a life-saving treatment in the Intensive Care Unit (ICU), but often causes patients to be at risk of further respiratory complication. We created a statistical model utilizing electronic health record and physiologic vitals data to predict the Center for Disease Control and Prevention (CDC) defined Ventilator Associated Complications (VACs). Further, we evaluated the effect of data temporal resolution and feature generation method choice on the accuracy of such a constructed model. Methods: We constructed a random forest model to predict occurrence of VACs using health records and chart events from adult patients in the Medical Information Mart for Intensive Care III (MIMIC-III) database. We trained the machine learning models on two patient populations of 1921 and 464 based on low and high frequency data availability. Model features were generated using both basic statistical summaries and tsfresh, a python library that generates a large number of derived time-series features. Classification to determine whether a patient will experience VAC one hour after 35 h of ventilation was performed using a random forest classifier. Two different sample spaces conditioned on five varying feature extraction techniques were evaluated to identify the most optimal selection of features resulting in the best VAC discrimination. Each dataset was assessed using K-folds cross-validation (k = 10), giving average area under the receiver operating characteristic curves (AUROCs) and accuracies. Results: After feature selection, hyperparameter tuning, and feature extraction, the best performing model used automatically generated features on high frequency data and achieved an average AUROC of 0.83 ± 0.11 and an average accuracy of 0.69 ± 0.10. Discussion: Results show the potential viability of predicting VACs using machine learning, and indicate that higher-resolution data and the larger feature set generated by tsfresh yield better AUROCs compared to lower-resolution data and manual statistical features.

Keywords: machine learning—ML; mechanical ventilalion; random forest; respiratory decompensation; ventilator-associated complications; ventilator-associated event (VAE).