biostudies-literatureUnknown11(9)Tanaka ALiberation 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), <i>p</i> = 0.004 and &lt;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.Journal of clinical medicine2520https://www.ebi.ac.uk/biostudies/studies/S-EPMC9102390biostudies-literaturePrediction Model of Extubation Outcomes in Critically Ill Patients: A Multicenter Prospective Cohort Study.PMC9102390Kosaka JFujino YShintani AKotake YMaki YMizobuchi SEgi MTanaka AUchiyama AMorimatsu HHirao OKabata DFurushima NfalsePrediction Model of Extubation Outcomes in Critically Ill Patients: A Multicenter Prospective Cohort Study.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), <i>p</i> = 0.004 and &lt;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.2022-01-01T00:00:00Z2022 Apr2025-04-19T16:19:26.228Z2025-04-19T16:19:26.228ZS-EPMC91023903556664610.3390/jcm11092520