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... Show moreIntroduction 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. Show less
H2: Hensgens MP, Goorhuis A, Notermans DW, van Benthem BH, Kuijper EJ. Decrease of hypervirulent Clostridium difficile PCR ribotype 027 in the Netherlands. Euro Surveill. 2009 H3: Hensgens MP,... Show moreH2: Hensgens MP, Goorhuis A, Notermans DW, van Benthem BH, Kuijper EJ. Decrease of hypervirulent Clostridium difficile PCR ribotype 027 in the Netherlands. Euro Surveill. 2009 H3: Hensgens MP, Keessen EC, Squire M, Riley TV, Koene MG, de Boer E, Lipman LJ, Kuijper EJ. Clostridium difficile infection in the community: a zoonotic disease? Clin Microbiol Infect. 2012 H4: Hensgens MP / Goorhuis A, van Kinschot CM, Crobach MJ, Harmanus C, Kuijper EJ. Clostridium difficile infection in an endemic setting in the Netherlands. Eur J Clin Microbiol Infect Dis. 2011 H5: Hensgens MP, Goorhuis A, Dekkers OM, Kuijper EJ. Time-interval of increased risk for Clostridium difficile infection after exposure to antibiotics. J Antimicrob Chemother. 2012 H7: Hensgens MP, Goorhuis A, Dekkers OM, van Benthem BH, Kuijper EJ. Outcome of nosocomial Clostridium difficile infections; results of a multicenter cohort study. Clin Infect Dis. 2013 H8: Hensgens MP / Bauer MP, Miller M, Gerding DN, Wilcox MH, Dale AP, Fawley WN, Kuijper EJ, Gorbach SL. Renal failure and leukocytosis are predictors of a complicated course of Clostridium difficile infection (CDI) if measured on day of diagnosis. Clin Infect Dis. 2012 H9: Hensgens MP, Kuijper EJ. Clostridium difficile infection due to binary toxin positive strains. Emerg Infect Dis. 2013 H10: Hensgens MP, Dekkers OM, Goorhuis A, Le Cessie S, Kuijper EJ. Predicting a severe course of Clostridium difficile infection at the bedside. Clin Microbiol Infect. 2012 Show less
