TY - JOUR
T1 - Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients
T2 - a multicenter machine learning study with highly granular data from the Dutch Data Warehouse
AU - Fleuren, Lucas M.
AU - Tonutti, Michele
AU - de Bruin, Daan P.
AU - Lalisang, Robbert C. A.
AU - Dam, Tariq A.
AU - Gommers, Diederik
AU - Cremer, Olaf L.
AU - Bosman, Rob J.
AU - Vonk, Sebastiaan J. J.
AU - Fornasa, Mattia
AU - Machado, Tomas
AU - van der Meer, Nardo J. M.
AU - Rigter, Sander
AU - Wils, Evert-Jan
AU - Frenzel, Tim
AU - Dongelmans, Dave A.
AU - de Jong, Remko
AU - Peters, Marco
AU - Kamps, Marlijn J. A.
AU - Ramnarain, Dharmanand
AU - Nowitzky, Ralph
AU - Nooteboom, Fleur G. C. A.
AU - de Ruijter, Wouter
AU - Urlings-Strop, Louise C.
AU - Smit, Ellen G. M.
AU - Mehagnoul-Schipper, D. Jannet
AU - Dormans, Tom
AU - de Jager, Cornelis P. C.
AU - Hendriks, Stefaan H. A.
AU - Oostdijk, Evelien
AU - Reidinga, Auke C.
AU - Festen-Spanjer, Barbara
AU - Brunnekreef, Gert
AU - Cornet, Alexander D.
AU - van den Tempel, Walter
AU - Boelens, Age D.
AU - Koetsier, Peter
AU - Lens, Judith
AU - Achterberg, Sefanja
AU - Faber, Harald J.
AU - Karakus, A.
AU - Beukema, Menno
AU - Entjes, Robert
AU - de Jong, Paul
AU - Houwert, Taco
AU - Hovenkamp, Hidde
AU - Noorduijn Londono, Roberto
AU - Quintarelli, Davide
AU - Scholtemeijer, Martijn G.
AU - de Beer, Aletta A.
AU - Dutch ICU Data Sharing Against Covid-19 Collaborators
AU - van Osch, Frits
AU - Aries, Marcel
N1 - Funding Information:
Partially funded by grants from ZonMw (project 10430012010003, file 50-55700-98-908), Zorgverzekeraars Nederland and the Corona Research Fund. The sponsors had no role in any part of the study.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/6/28
Y1 - 2021/6/28
N2 - 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.
AB - 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.
KW - COVID-19
KW - Mortality prediction
KW - Risk factors
KW - Machine learning
KW - SCORE
U2 - 10.1186/s40635-021-00397-5
DO - 10.1186/s40635-021-00397-5
M3 - Article
C2 - 34180025
SN - 2197-425X
VL - 9
JO - Intensive Care Medicine Experimental
JF - Intensive Care Medicine Experimental
IS - 1
M1 - 32
ER -