Frontiers in medicine · 2024
Development and validation of a risk prediction model for multiple organ dysfunction syndrome secondary to severe heat stroke based on immediate assessment indicators on ICU admission.
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Ren Entong, Chen Hao, Guo Chenjiao, Peng Yuanyuan, Tian Li, Yan Lulu, Tong Huasheng, Liu Anwei, Li Weihua
Аннотация
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: Two hundred eighty-four 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 h 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.
METHODS: Two hundred eighty-four 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 h 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.