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ORIGINAL RESEARCH article

Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 15 - 2024 | doi: 10.3389/fphys.2024.1501854

Predicting Postoperative Pulmonary Infection Risk in patients with diabetes Using Machine Learning

Provisionally accepted
Chunxiu Zhao Chunxiu Zhao 1*Bingbing Xiang Bingbing Xiang 2*Jie Zhang Jie Zhang 3Pingliang Yang Pingliang Yang 3*Qiaoli Liu Qiaoli Liu 3*Shun Wang Shun Wang 3*
  • 1 Chengdu Third People's Hospital, Chengdu, Sichuan Province, China
  • 2 West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
  • 3 Department of Anesthesiology, First Affiliated Hospital of Chengdu Medical College`, Chengdu, China

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

    Background: Patients with diabetes face an increased risk of postoperative pulmonary infection (PPI). However, precise predictive models specific to this patient group are lacking.: To develop and validate a machine learning model for predicting PPI risk in patients with diabetes. Methods: This retrospective study enrolled 1,269 patients with diabetes who underwent elective non-cardiac, non-neurological surgeries at our institution from January 2020 to December 2023. Predictive models were constructed using nine different machine learning algorithms. Feature selection was conducted using Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. Model performance was assessed via the Area Under the Curve (AUC), precision, accuracy, specificity and F1-score. Results: The Ada Boost classifier (ADA) model exhibited the best performance with an AUC of 0.901, Accuracy of 0.91, Precision of 0.82, specificity of 0.98, PPV of 0.82, and NPV of 0.82. LASSO feature selection identified six optimal predictive factors: postoperative transfer to the ICU, Age, American Society of Anesthesiologists (ASA) physical status score, chronic obstructive pulmonary disease (COPD) status, surgical department, and duration of surgery.Our study developed a robust predictive model using six clinical features, offering a valuable tool for clinical decision-making and personalized prevention strategies for PPI in patients with diabetes.

    Keywords: Diabetes Mellitus, Postoperative pulmonary infection, machine learning, risk prediction, Ada Boost classifier

    Received: 30 Sep 2024; Accepted: 21 Nov 2024.

    Copyright: © 2024 Zhao, Xiang, Zhang, Yang, Liu and Wang. 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:
    Chunxiu Zhao, Chengdu Third People's Hospital, Chengdu, Sichuan Province, China
    Bingbing Xiang, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
    Pingliang Yang, Department of Anesthesiology, First Affiliated Hospital of Chengdu Medical College`, Chengdu, China
    Qiaoli Liu, Department of Anesthesiology, First Affiliated Hospital of Chengdu Medical College`, Chengdu, China
    Shun Wang, Department of Anesthesiology, First Affiliated Hospital of Chengdu Medical College`, Chengdu, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.