{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:24:39Z","timestamp":1774542279638,"version":"3.50.1"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T00:00:00Z","timestamp":1736726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>We aimed to explore the application value of unsupervised machine learning in identifying acute gastrointestinal injury (AGI) after extracorporeal circulation for acute type A aortic dissection (ATAAD).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Patients who underwent extracorporeal circulation for ATAAD at the First Hospital of Lanzhou University from January 2016 to January 2021 were included. Unsupervised machine learning algorithm was used to stratify patients into different phenogroups according to the similarity of their clinical features and laboratory test results. The differences in the incidence of perioperative AGI and other adverse events among different phenogroups were compared. Logistic regression was used to analyze the high-risk factors for AGI in each phenogroups and random forest (RF) algorithms were used to construct diagnostic models for AGI in different phenogroups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 188 patients were included, with 166 males and 22 females. Unsupervised Machine Learning stratified patients into three phenogroups (phenogroup A, B, and C). Compared with other phenogroups, phenogroup B patients were older (<jats:italic>P<\/jats:italic>\u2009&amp;lt;\u20090.01), had higher preoperative lactate and D-dimer levels, and had the highest incidence of AGI (52.5%, <jats:italic>P<\/jats:italic>\u2009&amp;lt;\u20090.001) and in-hospital mortality (18.6%, <jats:italic>P<\/jats:italic>\u2009=\u20090.002). The random forest model showed that the top four risk factors for AGI in phenogroup B were cardiopulmonary bypass time, operation time, aortic clamping time, and ventilator time, which were significantly different from other phenogroups. The areas under the curve (AUCs) for diagnosing postoperative AGI of phenogroup A, B, and C were 0.943 (0.854\u20130.992), 0.990 (0.966\u20131.000), and 0.964 (0.899\u20130.997) using the RF model, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Phenogroup stratification based on unsupervised learning can accurately identify high-risk populations for postoperative AGI in ATAAD, providing a new approach for implementing individualized preventive and therapeutic measures in clinical practice.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcvm.2024.1514751","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T06:10:50Z","timestamp":1736748650000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Unsupervised machine learning model for phenogroup-based stratification in acute type A aortic dissection to identify postoperative acute gastrointestinal injury"],"prefix":"10.3389","volume":"11","author":[{"given":"Yuhu","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenxiang","family":"Weng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baoping","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenxing","family":"Bao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peining","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yatao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1093\/eurheartj\/ehac757","article-title":"Acute aortic dissection: evidence, uncertainties, and future therapies","volume":"44","author":"Rylski","year":"2023","journal-title":"Eur Heart J"},{"key":"B2","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1097\/00003246-199802000-00016","article-title":"Perioperative determinants of morbidity and mortality in elderly patients undergoing cardiac surgery","volume":"26","author":"Rady","year":"1998","journal-title":"Crit Care Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.jacc.2015.05.029","article-title":"Presentation, diagnosis, and outcomes of acute aortic dissection: 17-year trends from the international registry of acute aortic dissection","volume":"66","author":"Pape","year":"2015","journal-title":"J Am Coll Cardiol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.ccl.2017.03.004","article-title":"Acute type A aortic dissection","volume":"35","author":"Elsayed","year":"2017","journal-title":"Cardiol Clin"},{"key":"B5","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1016\/j.jtcvs.2021.04.053","article-title":"2021 the American Association for thoracic surgery expert consensus document: surgical treatment of acute type A aortic dissection","volume":"162","author":"Malaisrie","year":"2021","journal-title":"J Thorac Cardiovasc Surg"},{"key":"B6","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1191\/0267659106pf871oa","article-title":"Gastrointestinal dysfunction following cardiac surgery","volume":"21","author":"Ohri","year":"2006","journal-title":"Perfusion"},{"key":"B7","first-page":"327","article-title":"Predictors and outcome of gastrointestinal complications after cardiac surgery","volume":"67","author":"Hashemzadeh","year":"2012","journal-title":"Minerva Chir"},{"key":"B8","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1186\/1749-8090-7-108","article-title":"Postoperative abdominal complications after cardiopulmonary bypass","volume":"7","author":"Dong","year":"2012","journal-title":"J Cardiothorac Surg"},{"key":"B9","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1186\/s12872-023-03475-6","article-title":"Incidence and influencing factors of acute gastrointestinal injury after cardiac surgery","volume":"23","author":"Lu","year":"2023","journal-title":"BMC Cardiovasc Disord"},{"key":"B10","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1051\/ject\/201446142","article-title":"Gastrointestinal complications and cardiac surgery","volume":"46","author":"Allen","year":"2014","journal-title":"J Extra Corpor Technol"},{"key":"B11","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1111\/jocs.16294","article-title":"Risk factors for ischemic gastrointestinal complications in patients undergoing open cardiac surgical procedures: a single-center retrospective experience","volume":"37","author":"Naar","year":"2022","journal-title":"J Card Surg"},{"key":"B12","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1186\/s13054-022-03977-3","article-title":"Machine learning derivation of four computable 24-h pediatric sepsis phenotypes to facilitate enrollment in early personalized anti-inflammatory clinical trials","volume":"26","author":"Qin","year":"2022","journal-title":"Critical Care (London, England)"},{"key":"B13","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1002\/ejhf.1333","article-title":"Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy","volume":"21","author":"Cikes","year":"2019","journal-title":"Eur J Heart Fail"},{"key":"B14","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1007\/s00134-011-2459-y","article-title":"Gastrointestinal function in intensive care patients: terminology, definitions and management. Recommendations of the ESICM working group on abdominal problems","volume":"38","author":"Reintam Blaser","year":"2012","journal-title":"Intensive Care Med"},{"key":"B15","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1097\/ALN.0000000000002960","article-title":"Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations","volume":"132","author":"Hashimoto","year":"2020","journal-title":"Anesthesiology"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1268","DOI":"10.1053\/j.jvca.2017.04.013","article-title":"Gastrointestinal complications after cardiac surgery: a nationwide population-based analysis of morbidity and mortality predictors","volume":"31","author":"Chaudhry","year":"2017","journal-title":"J Cardiothorac Vasc Anesth"},{"key":"B17","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1111\/ans.12134","article-title":"Gastrointestinal complications after cardiac surgery: 10-year experience of a single Australian centre","volume":"83","author":"Viana","year":"2013","journal-title":"ANZ J Surg"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1266","DOI":"10.1016\/j.athoracsur.2020.09.034","article-title":"Serious gastrointestinal complications after cardiac surgery and associated mortality","volume":"112","author":"Elgharably","year":"2021","journal-title":"Ann Thorac Surg"},{"key":"B19","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1177\/0218492320949084","article-title":"Gastrointestinal complications following cardiac surgery","volume":"28","author":"Chor","year":"2020","journal-title":"Asian Cardiovasc Thorac Ann"},{"key":"B20","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1177\/0218492310394803","article-title":"Predictors of survival after gastrointestinal complications in bypass grafting","volume":"19","author":"Vohra","year":"2011","journal-title":"Asian Cardiovasc Thorac Ann"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1007\/s10620-010-1418-8","article-title":"Altered gut flora are associated with septic complications and death in critically ill patients with systemic inflammatory response syndrome","volume":"56","author":"Shimizu","year":"2011","journal-title":"Dig Dis Sci"},{"key":"B22","doi-asserted-by":"publisher","first-page":"e17128","DOI":"10.1016\/j.heliyon.2023.e17128","article-title":"Relationship between the postoperative lactate dynamic levels, the acute gastrointestinal injury and the prognosis among patients who undergo surgical treatment for acute type A aortic dissection","volume":"9","author":"Wang","year":"2023","journal-title":"Heliyon"},{"key":"B23","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1016\/j.jemermed.2019.04.024","article-title":"Metformin-Associated lactic acidosis presenting like acute mesenteric ischemia","volume":"57","author":"Zhang","year":"2019","journal-title":"J Emerg Med"},{"key":"B24","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.path.2020.01.002","article-title":"Unsupervised machine learning in pathology: the next frontier","volume":"13","author":"Roohi","year":"2020","journal-title":"Surg Pathol Clin"}],"container-title":["Frontiers in Cardiovascular Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2024.1514751\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T06:10:53Z","timestamp":1736748653000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2024.1514751\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,13]]},"references-count":24,"alternative-id":["10.3389\/fcvm.2024.1514751"],"URL":"https:\/\/doi.org\/10.3389\/fcvm.2024.1514751","relation":{},"ISSN":["2297-055X"],"issn-type":[{"value":"2297-055X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,13]]},"article-number":"1514751"}}