Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-08-17

AUTHORS

Harry Magunia, Simone Lederer, Raphael Verbuecheln, Bryant Joseph Gilot, Michael Koeppen, Helene A. Haeberle, Valbona Mirakaj, Pascal Hofmann, Gernot Marx, Johannes Bickenbach, Boris Nohe, Michael Lay, Claudia Spies, Andreas Edel, Fridtjof Schiefenhövel, Tim Rahmel, Christian Putensen, Timur Sellmann, Thea Koch, Timo Brandenburger, Detlef Kindgen-Milles, Thorsten Brenner, Marc Berger, Kai Zacharowski, Elisabeth Adam, Matthias Posch, Onnen Moerer, Christian S. Scheer, Daniel Sedding, Markus A. Weigand, Falk Fichtner, Carla Nau, Florian Prätsch, Thomas Wiesmann, Christian Koch, Gerhard Schneider, Tobias Lahmer, Andreas Straub, Andreas Meiser, Manfred Weiss, Bettina Jungwirth, Frank Wappler, Patrick Meybohm, Johannes Herrmann, Nisar Malek, Oliver Kohlbacher, Stephanie Biergans, Peter Rosenberger

ABSTRACT

BackgroundIntensive 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 & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.MethodsA 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.Results1039 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.ConclusionsUsing 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. More... »

PAGES

295

Journal

TITLE

Critical Care

ISSUE

1

VOLUME

25

Author Affiliations

  • Department of Anesthesiology and Intensive Care Medicine, University Hospital Tübingen, Eberhard-Karls-University Tübingen, Hoppe Seyler Str. 3, 72076, Tübingen, Germany
  • Institute for Translational Bioinformatics and Medical Data Integration Center, University Hospital Tübingen, Eberhard-Karls-University Tübingen, Tübingen, Germany
  • Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
  • Center for Anaesthesia, Intensive Care and Emergency Medicine, Zollernalb Klinikum, Balingen, Germany
  • Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, Berlin, Germany
  • Institute of Medical Informatics, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität Zu Berlin, Berlin, Germany
  • Department of Anesthesiology, Intensive Care Medicine/Pain Therapy, Knappschaftskrankenhaus Bochum, Bochum, Germany
  • Department of Anaesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany
  • Chair of Anesthesiology 1, Witten/Herdecke University, Wuppertal, Germany
  • Department of Anesthesiology and Intensive Care Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
  • Department of Anaesthesiology, University Hospital Düsseldorf, Düsseldorf, Germany
  • Department of Anesthesiology and Intensive Care Medicine, University Hospital Essen, University Duisburg-Essen, Essen, Germany
  • Department of Anaesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
  • Department of Anesthesiology and Critical Care, Medical Center - University of Freiburg, Freiburg, Germany
  • Center for Anesthesiology, Emergency and Intensive Care Medicine, University of Göttingen, Göttingen, Germany
  • Department of Anesthesiology, University Medicine Greifswald, Greifswald, Germany
  • Department Cardiology, Angiology and Intensive Care Medicine, University Hospital Halle (Saale), Halle (Saale), Germany
  • Department of Anesthesiology, Heidelberg University Hospital, Heidelberg, Germany
  • Department of Anesthesiology and Intensive Care, Leipzig University Hospital, Leipzig, Germany
  • Department of Anesthesiology and Intensive Care, University Medical Center Schleswig-Holstein, Campus Lübeck, University of Lübeck, Lübeck, Germany
  • Department of Anaesthesiology and Intensive Care Therapy, Otto-Von-Guericke-University Magdeburg, Magdeburg, Germany
  • University Hospital Marburg, UKGM, Philipps University Marburg, Marburg, Germany
  • Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Giessen and Marburg, Justus-Liebig University Giessen, Giessen, Germany
  • Department of Anesthesiology and Intensive Care, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Munich, Germany
  • Klinik Und Poliklinik Für Innere Medizin II, Klinikum Rechts Der Isar der, Technischen Universität München, Munich, Germany
  • Department for Anesthesiology, Intensive Care Medicine, Emergency Medicine and Pain Medicine, St. Elisabethen Klinikum, Ravensburg, Germany
  • Department of Anesthesiology, Intensive Care Medicine and Pain Medicine, Saarland University Hospital Medical Center, Homburg/Saar, Germany
  • Department of Anesthesiology and Intensive Care Medicine, Ulm University, Ulm, Germany
  • Department of Anaesthesiology and Intensive Care Medicine, Cologne-Merheim Medical Centre, Witten/Herdecke University, Cologne-Merheim, Germany
  • Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wuerzburg, University Wuerzburg, Wuerzburg, Germany
  • Department of Internal Medicine 1, University Hospital Tübingen, Tübingen, Germany
  • Department of Computer Science, Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13054-021-03720-4

    DOI

    http://dx.doi.org/10.1186/s13054-021-03720-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1140475897

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34404458


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        "description": "BackgroundIntensive 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 & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.MethodsA 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.Results1039 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 \u201csurvival\u201d. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients\u2019 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.ConclusionsUsing 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 \u201cClinicalTrials\u201d (clinicaltrials.gov) under NCT04455451.", 
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