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<p>Treatment of patients with severe trauma remains challenging. This study aimed to identify risk factors for all-cause mortality in ICU trauma patients to construct a predictive model. 2205 trauma patients were selected from the MIMIC-IV database, and 49 ICU indicators were obtained. All trauma patients were divided into training and testing datasets in a ratio of 7:3. Standardized mean difference (SMD) were conducted to ensure no significant difference between the two datasets. Subsequently, the least absolute shrinkage and selection operator and multivariate logistic regression analyses were conducted to identify the core variables from all ICU indicators, followed by constructing and evaluating a nomogram model. The regression analyses selected hepatopathy, obesity, chloride, body temperature, white blood cell (WBC) count, and acute physiology score III (APS III) as core variables from the remaining indicators. Furthermore, the nomogram model showed that six core variables influenced the mortality of trauma patients. Additionally, the calibration curves, decision curve analysis, and area under the receiver operating characteristic curves (<i>p</i> &gt; 0.05) all verified the good prediction performance of the model.</p>

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Development of a nomogram to predict in-hospital mortality of trauma patients in the ICU: an analysis of the MIMIC-IV database

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Yiqian Zeng,
  • Nieqiong Tan,
  • Xiaoyan He,
  • Suna Peng,
  • Eryue Qiu

摘要

Treatment of patients with severe trauma remains challenging. This study aimed to identify risk factors for all-cause mortality in ICU trauma patients to construct a predictive model. 2205 trauma patients were selected from the MIMIC-IV database, and 49 ICU indicators were obtained. All trauma patients were divided into training and testing datasets in a ratio of 7:3. Standardized mean difference (SMD) were conducted to ensure no significant difference between the two datasets. Subsequently, the least absolute shrinkage and selection operator and multivariate logistic regression analyses were conducted to identify the core variables from all ICU indicators, followed by constructing and evaluating a nomogram model. The regression analyses selected hepatopathy, obesity, chloride, body temperature, white blood cell (WBC) count, and acute physiology score III (APS III) as core variables from the remaining indicators. Furthermore, the nomogram model showed that six core variables influenced the mortality of trauma patients. Additionally, the calibration curves, decision curve analysis, and area under the receiver operating characteristic curves (p > 0.05) all verified the good prediction performance of the model.