What is the clinical evidence from this study?

Study design: Other. Population: Patients undergoing cardiac surgery with risk of postoperative pulmonary complications (n=492). Primary outcome: Occurrence of postoperative pulmonary complications (PPC) after cardiac surgery (p=0.412).

February 28, 2026Open Access

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

Key Result

Postoperative pulmonary complications occurred in 14.2% of cardiac surgery patients with no significant difference in surgical procedure categories (p=0.412).

Key Points

The aim is to create a prediction model for postoperative pulmonary complications in cardiac surgery, focusing on modifiable risk factors.
Conducted a prospective observational cohort study with 492 adults undergoing cardiac surgery.
Analyzed prespecified predictors including gas exchange, pulmonary function, and inspiratory muscle strength.
Utilized univariable and multivariable logistic regression to develop the prediction model.
Assessed discriminative ability using the area under the receiver operating characteristic curve.
14.1% of patients developed postoperative pulmonary complications after surgery.
Identified five independent predictors of PPCs: elevated arterial PaCO2, oxygen desaturation, reduced gait speed, lower FEV1/FVC ratio, and diminished inspiratory muscle strength.
The model demonstrated an AUC of 0.86 in the training cohort and 0.87 in the validation cohort, indicating high predictive accuracy.

Structured PICO

Can a prediction model using modifiable preoperative physiological variables accurately predict postoperative pulmonary complications in adults undergoing open-chest cardiac surgery?

Population

492 adults undergoing open-chest cardiac surgery between August 15, 2023 and December 31, 2023

Intervention

Prediction model based on five modifiable preoperative risk factors (arterial PaCO2, oxygen desaturation [SpO2<93%], gait speed, FEV1/FVC ratio, and inspiratory muscle strength [MIP % predicted])

Outcome

Postoperative pulmonary complications (PPCs)hard clinical

A parsimonious prediction model using five modifiable physiological variables accurately predicts postoperative pulmonary complications in cardiac surgery patients, providing a tool to guide individualized prehabilitation.

Main Result

Absolute Event Rate: 14.2% vs 85.8%

p-value: p=0.412

Limitations

No information on study design and methodology.
Lack of demographic and baseline characteristics details.
No statistical results beyond chi-square p-value for procedure categories.
Unclear if multicenter or single-center study.
No effect size metrics reported for primary outcome.

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.

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