A Predictive Model of Reintubation After Cardiac Surgery Using the Electronic Health Record.

Abstract

Reintubation and prolonged intubation after cardiac surgery are associated with significant complications. Despite these competing risks, providers frequently extubate patients with limited insight into the risk of reintubation at the time of extubation. Achieving timely, successful extubation remains a significant clinical challenge. Based on an analysis of 2835 patients undergoing cardiac surgery at our institution between November 2017 and July 2020, we developed a model for an individual's risk of reintubation at the time of extubation. Predictors were screened for inclusion in the model based on clinical plausibility and availability at the time of extubation. Rigorous data reduction methods were used to create a model that could be easily integrated into clinical workflow at the time of extubation. In total, 90 patients (3.2%) were reintubated within 48 hours of initial extubation. Number of inotropes (1 [adjusted odds ratio (OR), 15.4; 95% confidence interval (CI) 6.5-47.6; P < .001], ≥2 [OR, 62.7; 95% CI 14.3-279.5; P < .001]); dexmedetomidine dose (OR, 3.0 [per μg/kg/h]; 95% CI 1.9-4.7; P < .001), time to extubation (OR, 1.04 [per 6-hour increase]; 95% CI 1.02-1.05; P < .001), and respiratory rate (OR, 1.04 [per breath/min]; 95% CI 1.01-1.07; P < .001) were the best predictors for the model, which displayed excellent discriminative capacity (area under the receiver operating characteristic, 0.86; 95% CI 0.84-0.89). An improved understanding of reintubation risk may lead to improved decision-making at extubation and targeted interventions to decrease reintubation in high-risk patients. Future studies are needed to optimize timing of extubation.