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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Experimental Pharmacology and Drug Discovery
Volume 15 - 2024 | doi: 10.3389/fphar.2024.1486346
This article is part of the Research Topic Morphological Changes in Immune Cells for Precision Sepsis Treatment View all 3 articles

Predicting intra-abdominal candidiasis in elderly septic patients using machine learning based on lymphocyte subtyping: A prospective cohort study

Provisionally accepted
Jiahui Zhang Jiahui Zhang Guoyu Zhao Guoyu Zhao Xianli Lei Xianli Lei Na Cui Na Cui *
  • Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China

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

    Objective: Intra-abdominal candidiasis (IAC) is difficult to predict in elderly septic patients with intra-abdominal infection (IAI). This study aimed to develop and validate a nomogram based on lymphocyte subtyping and clinical factors for the early and rapid prediction of IAC in elderly septic patients.: A prospective cohort study of 284 consecutive elderly patients diagnosed with sepsis and IAI was performed. We assessed the clinical characteristics and parameters of lymphocyte subtyping at the onset of IAI. A machine-learning random forest model was used to select important variables, and multivariate logistic regression was used to analyze the factors influencing IAC. A nomogram model was constructed, and the discrimination, calibration, and clinical effectiveness of the model were verified. Results: According to the results of the random forest and multivariate analyses, gastrointestinal perforation, renal replacement therapy (RRT), T-cell count, CD28+CD8+ T-cell count and CD38+CD8+ T-cell count were independent predictors of IAC. Using the above parameters to establish a nomogram, the area under the curve (AUC) values of the nomogram in the training and testing cohorts were 0.840 (95%CI 0.778-0.902) and 0.783 (95%CI 0.682-0.883), respectively. The AUC in the training cohort was greater than the Candida score [0.840 (95%CI 0.778-0.902) vs. 0.539 (95%CI 0.464-0.615), p<0.001]. The calibration curve showed good predictive values and observed values of the nomogram; the DCA results showed that the nomogram had high clinical value. Conclusion: We established a nomogram based on the T-cell count, CD28+CD8+ T-cell count, CD38+CD8+ T-cell count and clinical risk factors that can help clinical physicians quickly rule out IAC or identify elderly patients at greater risk for IAC at the onset of infection.

    Keywords: intra-abdominal candidiasis, Elderly, Sepsis, Lymphocyte subtyping, risk stratification, machine learning, nomogram Clinical Trial Registration: chictr.org.cn, identifier ChiCTR2300069020

    Received: 26 Aug 2024; Accepted: 29 Nov 2024.

    Copyright: © 2024 Zhang, Zhao, Lei and Cui. 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: Na Cui, Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 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.