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Updated: May 30, 2025
Murine Model of Intestinal Ischemia-reperfusion Injury
Published on: May 11, 2016
Unsupervised machine learning model for phenogroup-based stratification in acute type A aortic dissection to identify
Yuhu Ma1, Xiaofang Yang2, Chenxiang Weng3
1Department of Anesthesiology and Operation, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
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.
Conclusion:
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.
