Development and validation of a predictive model for postoperative acute respiratory distress syndrome in patients with type A aortic dissection based on the 2023 updated definition
ChengBin Tang, Tianwei Wang, Haiqing Diao, Lulu Zhou, Haoran Wang, Tingting Yu, Jichao Zhai, Aipeng Hu, Jing Yuan, Jing Hang, Hailong Yu, Yuping Li, Ruiqiang Zheng, Jun Shao · 2025
Read Development and validation of a predictive model for postoperative acute respiratory distress syndrome in patients with type A aortic dissection based on the 2023 updated definition by ChengBin Tang, Tianwei Wang, Haiqing Diao, Lulu Zhou, Haoran Wang, Tingting Yu, Jichao Zhai, Aipeng Hu, Jing Yuan, Jing Hang, Hailong Yu, Yuping Li, Ruiqiang Zheng, Jun Shao (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12699891.txt.
Description
ChengBin Tang and co-authors built a model to predict which patients develop lung failure after surgery for a torn aorta. This complication, acute respiratory distress syndrome, strikes often after repair of a type A dissection and worsens outcomes. The team used the updated 2023 definition of the syndrome. They studied 423 patients at one hospital, splitting them into training and validation groups.
Nearly half the patients developed the lung complication. A screening method flagged thirteen risk factors from the records. Five machine learning models were built on those factors, and a random forest came out on top, sorting patients with very high accuracy. A classic regression model ran a close second. The authors offer the winning model as an early-warning tool for surgical teams, so high-risk patients can get protective care before breathing fails.
Catalog record
| CREATOR | ChengBin Tang, Tianwei Wang, Haiqing Diao, Lulu Zhou, Haoran Wang, Tingting Yu, Jichao Zhai, Aipeng Hu, Jing Yuan, Jing Hang, Hailong Yu, Yuping Li, Ruiqiang Zheng, Jun Shao |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 42 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |