Machine Learning Prediction of Death in Critically Ill Patients With Coronavirus Disease 2019.

Link: https://doi.org/10.1097/CCE.0000000000000515
Authors: Churpek, Matthew M; Gupta, Shruti; Spicer, Alexandra B; Hayek, Salim S; Srivastava, Anand; Chan, Lili; Melamed, Michal L; Brenner, Samantha K; Radbel, Jared; Madhani-Lovely, Farah; Bhatraju, Pavan K; Bansal, Anip; Green, Adam; Goyal, Nitender; Shaefi, Shahzad; Parikh, Chirag R; Semler, Matthew W; Leaf, David E; ,

Abstract: Critically ill patients with coronavirus disease 2019 have variable mortality. Risk scores could improve care and be used for prognostic enrichment in trials. We aimed to compare machine learning algorithms and develop a simple tool for predicting 28-day mortality in ICU patients with coronavirus disease 2019. This was an observational study of adult patients with coronavirus disease 2019. The primary outcome was 28-day inhospital mortality. Machine learning models and a simple tool were derived using variables from the first 48 hours of ICU admission and validated externally in independent sites and temporally with more recent admissions. Models were compared with a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 using the area under the receiver operating characteristic curve and calibration. Sixty-eight U.S. ICUs. Adults with coronavirus disease 2019 admitted to 68 ICUs in the United States between March 4, 2020, and June 29, 2020. None. eXtreme Gradient Boosting had the highest discrimination overall, and our simple tool had higher discrimination than a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 on external validation. These models could be used to improve triage decisions and clinical trial enrichment.