Best clinical model predicting extubation failure: a diagnostic accuracy post hoc analysis

Predicting extubation failure remains a clinical challenge. This study aimed to determine diagnostic accuracy of models used at the bed side. Post hoc analysis of 2341 patients at all risk included in five multicenter randomized trials. Diagnostic accuracy of three clinical prediction models was compared: 3-factors model including age > 65y, chronic heart or pulmonary disease; 4-factors model adding prolonged mechanical ventilation; and 11-factors model including age > 65 years, ≥ 2 comorbidities, prolonged mechanical ventilation, acute heart failure as the primary indication for mechanical ventilation, moderate-to-severe chronic obstructive pulmonary disease, APACHE II score > 12 on extubation day, airway patency problems, inability to deal with respiratory secretions, not simple weaning, obesity, or hypercapnia at the end of the spontaneous breathing trial. Crude and adjusted for spontaneous breathing trial (SBT) models were compared for all-cause reintubation at 7 days using Youden and Kappa indexes. The 3-factors model had a very low global prediction capability (Youden index 0.08 and Kappa index 0.04); the 4-factors and 11-factors models had low global prediction capability (Youden index 0.12 and 0.16, and Kappa index 0.06 and 0.07, respectively). Aggressive SBT strategies (pressure support ≥ 7 cm H2O with or without positive end-expiratory pressure) were associated with extubation failure risk (p < 0.001). All adjusted models had low diagnostic capability (0.08/0.03, 0.07/0.03, and 0.06/0.02 respectively). Based on these results, the 3-factors model reported a very low diagnostic accuracy, and the 4 or 11-factors models showed similar low accuracy. No improvement was observed after adjusting for other aspects of weaning.