Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Lucas M. Fleuren*, Michele Tonutti, Daan P. de Bruin, Robbert C. A. Lalisang, Tariq A. Dam, Diederik Gommers, Olaf L. Cremer, Rob J. Bosman, Sebastiaan J. J. Vonk, Mattia Fornasa, Tomas Machado, Nardo J. M. van der Meer, Sander Rigter, Evert-Jan Wils, Tim Frenzel, Dave A. Dongelmans, Remko de Jong, Marco Peters, Marlijn J. A. Kamps, Dharmanand RamnarainRalph Nowitzky, Fleur G. C. A. Nooteboom, Wouter de Ruijter, Louise C. Urlings-Strop, Ellen G. M. Smit, D. Jannet Mehagnoul-Schipper, Tom Dormans, Cornelis P. C. de Jager, Stefaan H. A. Hendriks, Evelien Oostdijk, Auke C. Reidinga, Barbara Festen-Spanjer, Gert Brunnekreef, Alexander D. Cornet, Walter van den Tempel, Age D. Boelens, Peter Koetsier, Judith Lens, Sefanja Achterberg, Harald J. Faber, A. Karakus, Menno Beukema, Robert Entjes, Paul de Jong, Taco Houwert, Hidde Hovenkamp, Roberto Noorduijn Londono, Davide Quintarelli, Martijn G. Scholtemeijer, Aletta A. de Beer, Dutch ICU Data Sharing Against Covid-19 Collaborators, Frits van Osch, Marcel Aries

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Background: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients.

Methods: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split.

Results: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH(2)O.

Conclusion: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.

Original languageEnglish
Article number32
Number of pages15
JournalIntensive Care Medicine Experimental
Issue number1
Publication statusPublished - 28 Jun 2021


  • COVID-19
  • Mortality prediction
  • Risk factors
  • Machine learning


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