Abstract
OBJECTIVES: We aimed to establish a diagnostic system using retrospective data to predict difficult wean from mechanical ventilation. DESIGN: A multicentre retrospective study SETTING: Five tertiary hospitals from China. PARTICIPANTS: Critically ill patients received mechanical ventilation between January 2018 and December 2022. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary endpoint was success weaning from mechanical ventilation (>48 hours), reintubation or death, whichever occurred first. RESULTS: Among 1365 initially screened patients, 703 patients (median age: 69 years; 63.02% male) were included. From 42 factors, 22 (p≤0.10) were identified for multivariate analysis. Subsequently, the lung injury score, brain natriuretic peptide level at 24 hours, 24 hour fluid balance, use of dexmedetomidine, spontaneous breathing trial (continuous positive airway pressure vs other) and endotracheal tube reinsertion were included in the predictive model. The area under the curve value was 0.8888 (95% CI: 0.8382, 0.9394). The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, likelihood ratio (LR)+ and LR- were 0.7559, 0.875, 0.9746, 0.3608, 0.7721, 6.0743 and 0.279, respectively. We established a nomogram model based on the optimal model. CONCLUSIONS: A model with six factors was established to predict difficult wean from mechanical ventilation in critically ill patients. However, the model should be verified in future well-designed studies before being extended to other populations. TRIAL REGISTRATION: ChiCTR1900021432. Registered on February 21, 2019; Post-results.