• Lucas M Fleuren, Tariq A Dam, Michele Tonutti, Daan P de Bruin, Robbert C A Lalisang, Diederik Gommers, Olaf L Cremer, Rob J Bosman, Sander Rigter, Evert-Jan Wils, Tim Frenzel, Dave A Dongelmans, Remko de Jong, Marco Peters, Marlijn J A Kamps, Dharmanand Ramnarain, Ralph Nowitzky, Fleur G C A Nooteboom, Wouter de Ruijter, Louise C Urlings-Strop, SmitEllen G MEGMIntensive Care, Spaarne Gasthuis, Haarlem en Hoofddorp, The Netherlands., D Jannet Mehagnoul-Schipper, Tom Dormans, de JagerCornelis P CCPCDepartment of Intensive Care, Jeroen Bosch Ziekenhuis, Den Bosch, The Netherlands., Stefaan H A Hendriks, Sefanja Achterberg, Evelien Oostdijk, Auke C Reidinga, Barbara Festen-Spanjer, Gert B Brunnekreef, Alexander D Cornet, Walter van den Tempel, Age D Boelens, Peter Koetsier, Judith Lens, Harald J Faber, A Karakus, Robert Entjes, Paul de Jong, RettigThijs C DTCDDepartment of Anesthesiology, Intensive Care and Pain Medicine, Amphia Ziekenhuis, Breda, The Netherlands., Sesmu Arbous, Sebastiaan J J Vonk, Mattia Fornasa, Tomas Machado, Taco Houwert, Hidde Hovenkamp, Roberto Noorduijn Londono, Davide Quintarelli, Martijn G Scholtemeijer, Aletta A de Beer, Giovanni Cinà, Adam Kantorik, Tom de Ruijter, Willem E Herter, Martijn Beudel, GirbesArmand R JARJDepartment of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands., Mark Hoogendoorn, Patrick J Thoral, ElbersPaul W GPWGDepartment of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands., and Dutch ICU Data Sharing Against Covid-19 Collaborators.
• Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands. l.fleuren@amsterdamumc.nl.
• Crit Care. 2021 Dec 27; 25 (1): 448448.

IntroductionDetermining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19.MethodsWe used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots.ResultsA total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure.ConclusionThe most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.© 2021. The Author(s).

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