Predictors for extubation failure in COVID-19 patients using a machine learning approach
Authors: Fleuren Lucas M. , Dam Tariq A. , Tonutti Michele , de Bruin Daan P. , Lalisang Robbert C. A. , Gommers Diederik , Cremer Olaf L. , Bosman Rob J. , 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. , Achterberg Sefanja , Oostdijk Evelien , Reidinga Auke C. , Festen-Spanjer Barbara , Brunnekreef Gert B. , Cornet Alexander D. , van den Tempel Walter , Boelens Age D. , Koetsier Peter , Lens Judith , Faber Harald J. , Karakus A. , Entjes Robert , de Jong Paul , Rettig Thijs C. D. , Arbous Sesmu , Vonk Sebastiaan J. J. , Fornasa Mattia , Machado Tomas , Houwert Taco , Hovenkamp Hidde , Noorduijn Londono Roberto , Quintarelli Davide , Scholtemeijer Martijn G. , de Beer Aletta A. , Cinà Giovanni , Kantorik Adam , de Ruijter Tom , Herter Willem E. , Beudel Martijn , Girbes Armand R. J. , Hoogendoorn Mark , Thoral Patrick J. , Elbers Paul W. G. ,   the Dutch ICU Data Sharing Against Covid-19 Collaborators, Koeter Julia , van Rietschote Roger , Reuland M. C. , van Manen Laura , Montenij Leon , van Bommel Jasper , van den Berg Roy , van Geest Ellen , Hana Anisa , van den Bogaard B. , Pickkers Peter , van der Heiden Pim , van Gemeren Claudia , 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. , van den Akker Remko , Rijpstra Tom A. , Boersma W. G. , Pretorius Daniel , Beukema Menno , Simons Bram , Rijkeboer A. A. , Aries Marcel , van den Oever Niels C. Gritters , van Tellingen Martijn , Dijkstra Annemieke , van Raalte Rutger 
Published: December 2021

Introduction

Determining 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.

Methods

We 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.

Results

A 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.

Conclusion

The 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.