Establishment of a nomogram for early risk prediction of severe trauma in primary medical institutions: A multi-center study.
10.1016/j.cjtee.2025.10.001
- Author:
Wang BO
1
;
Ming-Rui ZHANG
1
;
Gui-Yan MA
1
;
Zhan-Fu YANG
2
;
Rui-Ning LU
3
;
Xu-Sheng ZHANG
4
;
Shao-Guang LIU
5
Author Information
1. Gansu University of Chinese Medicine, The First Clinical Medical College, Lanzhou, 730000, China.
2. Emergency Department, Guanghe County People's Hospital, Linxia, 731300, Gansu Province, China.
3. Emergency Department, Gansu Provincial People's Hospital, Lanzhou, 730000, China.
4. Health Management Center, The Second People's Hospital of Gansu Province, Lanzhou, 730000, China.
5. Emergency Department, Gansu Provincial People's Hospital, Lanzhou, 730000, China. Electronic address: liushaoguang@gszy.edu.cn.
- Publication Type:Multicenter Study
- Keywords:
Early warning scoring model;
Nomogram;
Severe trauma;
Trauma score
- MeSH:
Humans;
Nomograms;
Male;
Female;
Wounds and Injuries/diagnosis*;
Risk Factors;
Middle Aged;
Adult;
Injury Severity Score;
Risk Assessment;
ROC Curve;
Aged;
Logistic Models;
China;
Glasgow Coma Scale
- From:
Chinese Journal of Traumatology
2025;28(6):418-426
- CountryChina
- Language:English
-
Abstract:
PURPOSE:To analyze risk factors for severe trauma and establish a nomogram for early risk prediction, to improve the early identification of severe trauma.
METHODS:This study was conducted on the patients treated in 81 trauma treatment institutions in Gansu province from 2020 to 2022. Patients were grouped by year, with 5364 patients from 2020 to 2021 as the training set and 1094 newly admitted patients in 2020 as the external validation set. Based on the injury severity score (ISS), patients in the training set were classified into 2 subgroups of the severe trauma group (n = 478, ISS scores ≥25) and the non-severe trauma group (n = 4886, ISS scores <25). Univariate and binary logistic regression analyses were employed to identify independent risk factors for severe trauma. Subsequently, a predictive model was developed using the R software environment. Furthermore, the model was subjected to internal and external validation via the Hosmer-Lemeshow test and receiver operating characteristic curve analysis.
RESULTS:In total, 6458 trauma patients were included in this study. Initially, this study identified several independent risk factors for severe trauma, including multiple traumatic injuries (polytrauma), external hemorrhage, elevated shock index, elevated respiratory rate, decreased peripheral oxygen saturation, and decreased Glasgow coma scale score (all p < 0.05). For internal validation, the area under the receiver operating characteristic curve was 0.914, with the sensitivity and specificity of 88.4% and 87.6%, respectively; while for external validation, the area under the receiver operating characteristic curve was 0.936, with the sensitivity and specificity of 84.6% and 93.7%, respectively. In addition, a good model fitting was observed through the Hosmer-Lemeshow test and calibration curve analysis (p > 0.05).
CONCLUSION:This study establishes a nomogram for early risk prediction of severe trauma, which is suitable for primary healthcare institutions in underdeveloped western China. It facilitates early triage and quantitative assessment of trauma severity by clinicians prior to clinical interventions.
