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

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Authors: Ruoxi Li, Meice Tian, C. Edward Wang, Yujia Huang, Weinan Chen, Song Ya, Bomiao Liu, Li Du, Xue Feng
Cited by: 1
References: 37

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

Fields of Study & Subjects

Respiratory Support and Mechanisms Chronic Obstructive Pulmonary Disease (COPD) Research Cardiac Anesthesia and Surgical Outcomes

Publication details

Journal / source
Annals of Rehabilitation Medicine
Publisher
Korean Academy of Rehabilitation Medicine
Volume
50
Issue
1
Pages
50–61
Publication date
26 February 2026
Language
EN