Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort
Creators: name: Magunia, Harry
name: Lederer, Simone
name: Verbuecheln, Raphael
name: Gilot, Bryant Joseph
name: Koeppen, Michael
name: Haeberle, Helene A.
name: Mirakaj, Valbona
name: Hofmann, Pascal
name: Marx, Gernot
name: Bickenbach, Johannes
name: Nohe, Boris
name: Lay, Michael
name: Spies, Claudia
name: Edel, Andreas
name: Schiefenhövel, Fridtjof
name: Rahmel, Tim
name: Putensen, Christian
name: Sellmann, Timur
name: Koch, Thea
name: Brandenburger, Timo
name: Kindgen-Milles, Detlef
name: Brenner, Thorsten
name: Berger, Marc
name: Zacharowski, Kai
name: Adam, Elisabeth
name: Posch, Matthias
name: Moerer, Onnen
name: Scheer, Christian S.
name: Sedding, Daniel
name: Weigand, Markus A.
name: Fichtner, Falk
name: Nau, Carla
name: Prätsch, Florian
name: Wiesmann, Thomas
name: Koch, Christian
name: Schneider, Gerhard
name: Lahmer, Tobias
name: Straub, Andreas
name: Meiser, Andreas
name: Weiss, Manfred
name: Jungwirth, Bettina
name: Wappler, Frank
name: Meybohm, Patrick
name: Herrmann, Johannes
name: Malek, Nisar
name: Kohlbacher, Oliver
name: Biergans, Stephanie
name: Rosenberger, Peter
Publisher: name: Universität Ulm
Year: 2021
Subjects: subject: COVID-19
subject: Critical care
subject: ARDS
subject: Outcome
subject: Prognostic models
subject: DDC 610 / Medicine &amp; health

description: Abstract Background Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective &amp; prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.