Predictive model for assessing the prognosis of rhabdomyolysis patients in the intensive care unit

This study specifically targets patients with RM admitted to ICU and presents a novel clinical prediction model that surpasses the conventional SOFA score. By integrating specific prognostic indicators tailored to RM, the model significantly enhances prediction accuracy, thereby enabling a more targeted and effective approach to managing RM patients.

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Publication Details

Journal
Frontiers in Medicine
Published
2025-01-10
DOI
https://doi.org/10.3389/fmed.2024.1518129
Citations
2
Primary Topic
Muscle and Compartmental Disorders
Type
article
Field-Weighted Citation Impact
4.01
Controls
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article

Predictive model for assessing the prognosis of rhabdomyolysis patients in the intensive care unit

Quankuan Gu, Hongyu Shi, Jun Lyu, Xianglin Meng et al.
2 citations
Frontiers in Medicine
Muscle and Compartmental Disorders
4.01
article

Predictive model for assessing the prognosis of rhabdomyolysis patients in the intensive care unit

Quankuan Gu, Hongyu Shi, Jun Lyu, Xianglin Meng, Yuan Xiong, Jianpeng Wang, Weilan Kong, Mingyan Zhao, Yu Song
article en
2 citations

Abstract

This study specifically targets patients with RM admitted to ICU and presents a novel clinical prediction model that surpasses the conventional SOFA score. By integrating specific prognostic indicators tailored to RM, the model significantly enhances prediction accuracy, thereby enabling a more targeted and effective approach to managing RM patients.

Frontiers in MedicineVol. 11
Harbin Medical University (CN), Fudan University Shanghai Cancer Center (CN), First Affiliated Hospital of Harbin Medical University (CN), First Affiliated Hospital of Jinan University (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 9%
Muscle and Compartmental Disorders
4.01
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Predictive model for assessing the prognosis of rhabdomyolysis patients in the intensive care unit — Quankuan Gu, Hongyu Shi, et al. · Frontiers in Medicine (2025) | TGRS Research Map | TGRS