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ORIGINAL RESEARCH article

Front. Cardiovasc. Med.
Sec. Cardiovascular Surgery
Volume 11 - 2024 | doi: 10.3389/fcvm.2024.1514751

Unsupervised Machine Learning Model for Phenogroup-Based Stratification in Acute Type A Aortic Dissection to Identify Postoperative Acute Gastrointestinal Injury

Provisionally accepted
Ma Yuhu Ma Yuhu 1Xiaofang Yang Xiaofang Yang 1Chenxiang Weng Chenxiang Weng 2Xiaoqing Wang Xiaoqing Wang 1Baoping Zhang Baoping Zhang 1Ying Liu Ying Liu 2Rui Wang Rui Wang 1Zhenxing Bao Zhenxing Bao 1Peining Yang Peining Yang 1Hong Zhang Hong Zhang 1Yatao Liu Yatao Liu 1*
  • 1 First Hospital of Lanzhou University, Lanzhou, China
  • 2 Lanzhou University, Lanzhou, Gansu Province, China

The final, formatted version of the article will be published soon.

    Objective: 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).Methods: 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.Results: 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 (P<0.01), had higher preoperative lactate and D-dimer levels, and had the highest incidence of AGI (52.5%, P<0.001) and in-hospital mortality (18.6%, P=0.002). 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 (0.854-0.992), 0.990 (0.966-1.000), and 0.964 (0.899, 0.997) using the RF model, respectively.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.

    Keywords: Acute type A aortic dissection, Unsupervised machine learning, Phenogroups, random forest, prediction

    Received: 21 Oct 2024; Accepted: 30 Dec 2024.

    Copyright: © 2024 Yuhu, Yang, Weng, Wang, Zhang, Liu, Wang, Bao, Yang, Zhang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yatao Liu, First Hospital of Lanzhou University, Lanzhou, China

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