Machine learning prediction of ARDS after heart valve surgery: development and validation in Northwest China.

Xuhua Li, Hao Chen, Aoxiang Chen, Wenhao Zhan, Hengxi Zhang, Qiyuan Bai, Yalan Zhang, Bing Song

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Article in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Xuhua Li *The First Clinical Medical College of Lanzhou University, Lanzhou, China.

Hao Chen *The First Clinical Medical College of Lanzhou University, Lanzhou, China.

Aoxiang Chen *Mozi Laboratory, Zhengzhou, China.

Wenhao ZhanSchool of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China.

Hengxi ZhangSchool of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China.

Qiyuan BaiThe First Clinical Medical College of Lanzhou University, Lanzhou, China.

Yalan ZhangDepartment of Cardiovascular Surgery, The First Hospital of Lanzhou University, Lanzhou, China.

Bing SongThe First Clinical Medical College of Lanzhou University, Lanzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To develop an AI-based predictive model for acute respiratory distress syndrome (ARDS) following cardiopulmonary bypass (CPB)-assisted heart valve replacement (HVR) to enable early identification of high-risk patients. Methods: We retrospectively analyzed 400 patients who underwent CPB-assisted HVR between January 2023 and February 2025. After data preprocessing and feature selection, the dataset was split into training ( Results: Among 400 patients, 56 (14%) developed ARDS postoperatively. Key predictors included Age, absolute monocyte count,right atrial transverse diameter, intraoperative blood loss, platelet count, main pulmonary artery diameter. The XGBoost model achieved excellent performance with an AUC of 0.853 and demonstrated good calibration (HL test Conclusion: The XGBoost model accurately predicts ARDS risk following CPB-assisted HVR using six clinically relevant predictors, providing a valuable tool for early risk stratification and potential intervention in high-risk patients.

Indexed as

acute respiratory distress syndrome (ARDS)extracorporeal circulationheart valve diseasemachine learningpredictive modelrisk factors

Identifiers

PMID41647807

DOI10.3389/fcvm.2025.1696326

PMCPMC12868288

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.