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
Authors: Fleuren Lucas M. , Tonutti Michele , de Bruin Daan P. , Lalisang Robbert C. A. , Dam Tariq A. , Gommers Diederik , Cremer Olaf L. , Bosman Rob J. , Vonk Sebastiaan J. J. , Fornasa Mattia , Machado Tomas , van der Meer Nardo J. M. , Rigter Sander , Wils Evert-Jan , Frenzel Tim , Dongelmans Dave A. , de Jong Remko , Peters Marco , Kamps Marlijn J. A. , Ramnarain Dharmanand , Nowitzky Ralph , Nooteboom Fleur G. C. A. , de Ruijter Wouter , Urlings-Strop Louise C. , Smit Ellen G. M. , Mehagnoul-Schipper D. Jannet , Dormans Tom , de Jager Cornelis P. C. , Hendriks Stefaan H. A. , Oostdijk Evelien , Reidinga Auke C. , Festen-Spanjer Barbara , Brunnekreef Gert , Cornet Alexander D. , van den Tempel Walter , Boelens Age D. , Koetsier Peter , Lens Judith , Achterberg Sefanja , Faber Harald J. , Karakus A. , Beukema Menno , Entjes Robert , de Jong Paul , Houwert Taco , Hovenkamp Hidde , Noorduijn Londono Roberto , Quintarelli Davide , Scholtemeijer Martijn G. , de Beer Aletta A. , Cinà Giovanni , Beudel Martijn , de Keizer Nicolet F. , Hoogendoorn Mark , Girbes Armand R. J. , Herter Willem E. , Elbers Paul W. G. , Thoral Patrick J. ,   Dutch ICU Data Sharing Against COVID-19 Collaborators, Rettig Thijs C. D. , Reuland M. C. , van Manen Laura , Montenij Leon , van Bommel Jasper , van den Berg Roy , van Geest Ellen , Hana Anisa , Boersma W. G. , van den Bogaard B. , Pickkers Peter , van der Heiden Pim , van Gemeren Claudia C. W. , Meinders Arend Jan , de Bruin Martha , Rademaker Emma , van Osch Frits H. M. , de Kruif Martijn , Schroten Nicolas , Arnold Klaas Sierk , Fijen J. W. , van Koesveld Jacomar J. M. , Simons Koen S. , Labout Joost , van de Gaauw Bart , Kuiper Michael , Beishuizen Albertus , Geutjes Dennis , Lutisan Johan , Grady Bart P. X. , van den Akker Remko , Simons Bram , Rijkeboer A. A. , Arbous Sesmu , Aries Marcel , van den Oever Niels C. Gritters , van Tellingen Martijn , Dijkstra Annemieke , van Raalte Rutger , Roggeveen Luca , van Diggelen Fuda , Hassouni Ali el , Guzman David Romero , Bhulai Sandjai , Ouweneel Dagmar , Driessen Ronald , Peppink Jan , de Grooth H. J. , Zijlstra G. J. , van Tienhoven A. J. , van der Heiden Evelien , Spijkstra Jan Jaap , van der Spoel Hans , de Man Angelique , Klausch Thomas , de Vries Heder , de Neree tot Babberich Michael , Thijssens Olivier , Wagemakers Lot , van der Pol Hilde G. A. , Hendriks Tom , Berend Julie , Silva Virginia Ceni , Kullberg Bob , Heunks Leo , Juffermans Nicole , Slooter Arjan 
Published: June 2021

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, lowP/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 cmH2O.

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/Fratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.