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ORIGINAL RESEARCH article

Front. Med.
Sec. Intensive Care Medicine and Anesthesiology
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1481097

Development and validation of a risk prediction model for multiple organ dysfunction syndrome (MODS) secondary to severe heat stroke based on immediate assessment indicators on ICU admission

Provisionally accepted
Entong Ren Entong Ren 1Hao Chen Hao Chen 2Chenjiao Guo Chenjiao Guo 2*Yuanyuan Peng Yuanyuan Peng 2*Li Tian Li Tian 1*Lulu Yan Lulu Yan 1*Huasheng Tong Huasheng Tong 3Anwei Liu Anwei Liu 3*Weihua Li Weihua Li 4*
  • 1 Health science center, Yangtze University, Jingzhou, China
  • 2 Guangdong Pharmaceutical University, Guangzhou, Guangdong Province, China
  • 3 Department of Critical Care Medicine, General Hospital of Southern Theatre Command of PLA, Guangzhou, China
  • 4 Department of Nursing, General Hospital of Southern Theatre Command of PLA, Guangzhou, China

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

    Introduction: Early prediction of multiple organ dysfunction syndrome (MODS) secondary to severe heat stroke (SHS) is crucial for improving patient outcomes. This study aims to develop and validate a risk prediction model for those patients based on immediate assessment indicators on ICU admission. Methods: 284 cases with SHS in our hospital between July 2009 and April 2024 were retrospectively reviewed, and categorized into non-MODS and MODS groups. Logistic regression analyses were performed to identify risk factors for MODS, and then to construct a risk prediction model, which was visualized by a nomogram. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow (HL) test, calibration curve, and decision curve analysis (DCA). Finally, the AUCs of the prediction model was compared with other scoring systems. Results: Acute gastrointestinal injury (AGI), heart rate (HR) >100 bpm, a decreased Glasgow Coma Scale (GCS) score, and elevated total bilirubin (TBil) within the first 24 hours of ICU admission are identified as independent risk factors for the development of MODS in SHS patients. The model demonstrated good discriminative ability, and the AUC was 0.910 (95% CI: 0.856-0.965). Applying the predictive model to the internal validation dataset demonstrated good discrimination with an AUC of 0.933 (95% CI: 0.880-0.985) and good fit and calibration. The DCA of this model showed a superior clinical net benefit. Discussion: The risk prediction model based on AGI, HR, GCS, and TBil shows robust predictive performance and clinical utility, which could serve as a reference for assessing and screening the risk of MODS in SHS patients.

    Keywords: Severe heat stroke, multiple organ dysfunction syndrome, Prediction model, Assessment indicators, Icu admission

    Received: 15 Aug 2024; Accepted: 05 Dec 2024.

    Copyright: © 2024 Ren, Chen, Guo, Peng, Tian, Yan, Tong, Liu and Li. 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:
    Chenjiao Guo, Guangdong Pharmaceutical University, Guangzhou, 510006, Guangdong Province, China
    Yuanyuan Peng, Guangdong Pharmaceutical University, Guangzhou, 510006, Guangdong Province, China
    Li Tian, Health science center, Yangtze University, Jingzhou, China
    Lulu Yan, Health science center, Yangtze University, Jingzhou, China
    Anwei Liu, Department of Critical Care Medicine, General Hospital of Southern Theatre Command of PLA, Guangzhou, China
    Weihua Li, Department of Nursing, General Hospital of Southern Theatre Command of PLA, Guangzhou, 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.