Evidence map›Paper›PMID 41755471›Full record

ArticleAnnals of rehabilitation medicine2026

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

Ruoxi Li, Meice Tian, Chuangshi Wang, Yujia Huang, Weinan Chen, Ya Song, Bomiao Liu, Liu Du, Xue Feng

Abstract read
In one paragraph

Article in Annals of rehabilitation medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ruoxi LiCardiopulmonary Rehabilitation Center, National Center for Cardiovascular Disease, China &Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Meice TianDepartment of Surgery, National Center for Cardiovascular Disease, China &Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Chuangshi WangNational Clinical Research Center for Cardiovascular Diseases, China &Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Yujia HuangCenter of Cardiac Surgery in Adults, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Weinan ChenInformation Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Ya SongCardiopulmonary Rehabilitation Center, National Center for Cardiovascular Disease, China &Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Bomiao LiuCardiopulmonary Rehabilitation Center, National Center for Cardiovascular Disease, China &Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Liu DuCardiopulmonary Rehabilitation Center, National Center for Cardiovascular Disease, China &Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Xue FengCardiopulmonary Rehabilitation Center, National Center for Cardiovascular Disease, China &Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

Funding

National High Level Hospital 2023-GSP-QN-38National High Level Hospital 2023-GSP-RC-07National Natural Science Foundation of China 82370401
6 · The paper itself

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.

methodsIn 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).

resultsA 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.

Indexed as

Cardiac surgical proceduresModels, statisticalPostoperative complicationsRespiratory insufficiencyRisk factors

Identifiers

PMID41755471
PMCPMC12961339

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