Development and Validation of a Clinically Actionable Prediction Model for Postoperative Pulmonary Complications in Cardiac Surgery: A Focus on Modifiable Risk Factors.

Abstract

Objective: To develop and validate a clinically actionable prediction model for postoperative pulmonary complications (PPCs) in cardiac surgery patients, focusing on modifiable preoperative risk factors amenable to targeted optimization.

Methods: In this prospective observational cohort study, 492 adults undergoing open-chest cardiac surgery between August 15, 2023 and December 31, 2023 were analyzed. Prespecified predictors included gas exchange variables, pulmonary function, inspiratory muscle strength, and physical performance. Univariable and multivariable logistic regression analyses were used to develop the prediction model. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC).

Results: A total of 90 patients (14.1%) developed PPCs after surgery. Five independent predictors were identified: elevated arterial PaCO2 (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.00-1.26), oxygen desaturation (SpO2<93%) (OR 12.47, 95% CI 3.51-48.13), reduced gait speed (OR 0.17, 95% CI 0.04-0.71), lower FEV1/FVC ratio (OR 0.96, 95% CI 0.92-1.00), and diminished inspiratory muscle strength (MIP % predicted) (OR 0.96, 95% CI 0.92-0.99). The model demonstrated good discriminative ability with an AUC of 0.86 (95% CI 0.80-0.93) in the training cohort and 0.87 (95% CI 0.74-0.93) in the validation cohort.

Conclusion: This parsimonious model achieved high predictive accuracy using five modifiable physiological variables. By targeting abnormalities in gas exchange, pulmonary mechanics, muscle strength, and functional reserve, the model offers a practical tool to guide individualized prehabilitation strategies for reducing PPC risk in cardiac surgery patients.