Prediction Model of Extubation Outcomes in Critically Ill Patients: A Multicenter Prospective Cohort Study

Aiko Tanaka, Daijiro Kabata, Osamu Hirao, Junko Kosaka, Nana Furushima, Yuichi Maki, Akinori Uchiyama, Moritoki Egi, Ayumi Shintani, Hiroshi Morimatsu, Satoshi Mizobuchi, Yoshifumi Kotake, Yuji Fujino

Research output: Contribution to journalArticlepeer-review

Abstract

Liberation from mechanical ventilation is of great importance owing to related complications from extended ventilation time. In this prospective multicenter study, we aimed to construct a versatile model for predicting extubation outcomes in critical care settings using obtainable physiological predictors. The study included patients who had been extubated after a successful 30 min spontaneous breathing trial (SBT). A multivariable logistic regression model was constructed to predict extubation outcomes (successful extubation without reintubation and uneventful extubation without reintubation or noninvasive respiratory support) using eight parameters: age, heart failure, respiratory disease, rapid shallow breathing index (RSBI), PaO2/FIO2, Glasgow Coma Scale score, fluid balance, and endotracheal suctioning episodes. Of 499 patients, 453 (90.8%) and 328 (65.7%) achieved successful and uneventful extubation, respectively. The areas under the curve for successful and uneventful extubation in the novel prediction model were 0.69 (95% confidence interval (CI), 0.62–0.77) and 0.70 (95% CI, 0.65–0.74), respectively, which were significantly higher than those in the conventional model solely using RSBI (0.58 (95% CI, 0.50–0.66) and 0.54 (95% CI, 0.49–0.60), p = 0.004 and <0.001, respectively). The model was validated using a bootstrap method, and an online application was developed for automatic calculation. Our model, which is based on a combination of generally obtainable parameters, established an accessible method for predicting extubation outcomes after a successful SBT.

Original languageEnglish
Article number2520
JournalJournal of Clinical Medicine
Volume11
Issue number9
DOIs
Publication statusPublished - May 1 2022

Keywords

  • extubation
  • intensive care
  • mechanical ventilation
  • noninvasive respiratory support
  • prediction model
  • ventilator liberation

ASJC Scopus subject areas

  • Medicine(all)

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