Clinical predictors of extubation failure in postoperative critically ill patients: a post-hoc analysis of a multicenter prospective observational study
Year: 2025
Type: article
Abstract: BACKGROUND: Postoperative patients constitute majority of critically ill patients, although factors predicting extubation failure in this group of patients remain unidentified. Aiming to propose clinical predictors of reintubation in postoperative patients, we conducted a post-hoc analysis of a multicenter prospective observational study. METHODS: This study included postoperative critically ill patients who underwent mechanical ventilation for > 24 h and were extubated after a successful 30-min spontaneous breathing trial. The primary outcome was reintubation within 48 h after extubation, and clinical predictors for reintubation were investigated using logistic regression analyses. RESULTS: Among the 355 included patients, 10.7% required reintubation. Multivariable logistic regression identified that the number of endotracheal suctioning episodes during the 24 h before extubation and underlying respiratory disease or pneumonia occurrence were significantly associated with reintubation (adjusted odds ratio [OR] 1.11, 95% confidence interval [CI] 1.05-1.18, p < 0.001; adjusted OR 2.58, 95%CI 1.30-5.13, p = 0.007). The probability of reintubation was increased significantly with the higher frequency of endotracheal suctioning, as indicated by restricted cubic splines. Subgroup analysis showed that these predictors were consistently associated with reintubation regardless of the use of noninvasive respiratory support after extubation. CONCLUSIONS: Endotracheal suctioning frequency and respiratory complications were identified as independent predictors of reintubation. These readily obtainable predictors may aid in decision-making regarding the extubation of postoperative patients.
Source: BMC Anesthesiology
Institutions: The University of Osaka, University of Fukui Hospital, Okayama University Hospital, Osaka Prefectural Medical Center, Kobe University Hospital, Toho University Ohashi Medical Center, Center for Mathematical and Data Sciences, Kobe University, Kobe University, Kyoto University Hospital, Osaka Metropolitan University
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,190
OpenAlex ID: W4408474339
