Respir Res. 2025 Dec 12;26(1):344. doi: 10.1186/s12931-025-03415-2.
ABSTRACT
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common complication after type A aortic dissection surgery and often leads to worsened clinical outcomes for patients. The early prediction of postoperative ARDS is a crucial challenge in clinical practice; however, there have been few reports on related studies based on the 2023 global new definition.
METHODS: A retrospective analysis was conducted on the clinical data of 423 patients who were diagnosed with type A aortic dissection and who underwent surgery at Northern Jiangsu People's Hospital in Jiangsu Province from November 2019 to April 2025. A 7:3 random division was applied to the patients, resulting in a training set n = 296 and a validation set n = 127. Risk factors were identified via LASSO analysis, and a comprehensive risk prediction model was subsequently constructed by integrating five machine learning algorithms. The receiver operating characteristic (ROC) curve was utilised, and the model with the best predictive performance was selected based on the area under the curve (AUC).
RESULTS: Among the 423 included patients, 192 developed ARDS, with an incidence rate of 45.39%. LASSO analysis revealed 13 risk factors. Among the five machine learning models constructed based on these factors, the random forest model demonstrated the highest prediction efficiency for ARDS (AUC = 0.978), followed by the logistic regression (AUC = 0.965), decision tree (AUC = 0.881), support vector machine (AUC = 0.835), and K-nearest neighbour (AUC = 0.807) models.
CONCLUSION: The development of a nomogram model using machine learning algorithms for predicting ARDS risk in patients with type A aortic dissection after surgery could identifying high-risk patients at an early stage and enable timely implementations of preventive strategies.
TRIAL REGISTRATION: The medical research ethics committee of the Northern Jiangsu People's Hospital provided approval for this study (ethics number: 2024ky314). This study is registered in the Chinese Clinical Trial Registry under registration number ChiCTR2500099730.The registration date was March 27,2025.
PMID:41388298 | DOI:10.1186/s12931-025-03415-2