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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Viral Immunology
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1445618
This article is part of the Research Topic Mathematical Modeling in Discovery and Analysis of Immune Responses View all articles

Prediction of the risk of mortality in older patients with coronavirus disease 2019 using blood markers and machine learning

Provisionally accepted
Linchao Zhu Linchao Zhu Yimin Yao Yimin Yao *
  • Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China

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

    The mortality rate among older people infected with severe acute respiratory syndrome coronavirus 2 is alarmingly high. This study aimed to explore the predictive value of a novel model for assessing the risk of death in this vulnerable cohort.We enrolled 199 older patients with coronavirus disease 2019 (COVID-19) from Zhejiang Provincial Hospital of Chinese Medicine (Hubin) between December 16, 2022, and January 17, 2023. Additionally, 90 patients from two other centers (Qiantang and Xixi) formed an external independent testing cohort. Univariate and multivariate analyses were used to identify the risk factors for mortality. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select variables associated with COVID-19 mortality. Nine machine-learning algorithms were used to predict mortality risk in older patients, and their performance was assessed using receiver operating characteristic curves, area under the curve (AUC), calibration curve analysis, and decision curve analysis.Neutrophil-monocyte ratio, neutrophil-lymphocyte ratio, C-reactive protein, interleukin 6, and D-dimer were considered to be relevant factors associated with the death risk of COVID-19-related death by LASSO regression. The Gaussian Naïve Bayes model was the best-performing model. In the validation cohort, the model had an AUC of 0.901, whereas in the testing cohort, the model had an AUC of 0.952. The calibration curve showed good correlation between the actual and predicted probabilities, and the decision curve indicated a strong clinical benefit. Furthermore, the model had an AUC of 0.873 in an external independent testing cohort.In this study a predictive machine-learning model was developed with an online prediction tool designed to assist clinicians in evaluating mortality risk factors and devising targeted and effective treatments for older patients with COVID-19, potentially reducing the mortality rates.

    Keywords: COVID-19, older patients, Mortality, Blood markers, machine learning, Gaussian naïve bayes

    Received: 07 Jun 2024; Accepted: 11 Oct 2024.

    Copyright: © 2024 Zhu and Yao. 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: Yimin Yao, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China

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