Critical Care (Dec 2021)

Predictors for extubation failure in COVID-19 patients using a machine learning approach

  • 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,
  • Ellen G. M. Smit,
  • D. Jannet Mehagnoul-Schipper,
  • Tom Dormans,
  • Cornelis P. C. de Jager,
  • 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,
  • Thijs C. D. Rettig,
  • 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,
  • Armand R. J. Girbes,
  • Mark Hoogendoorn,
  • Patrick J. Thoral,
  • Paul W. G. Elbers,
  • the Dutch ICU Data Sharing Against Covid-19 Collaborators

DOI
https://doi.org/10.1186/s13054-021-03864-3
Journal volume & issue
Vol. 25, no. 1
pp. 1 – 10

Abstract

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Abstract 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.

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