Early prediction and warning of MODS following major trauma via identification of cytokine storm: A prospective cohort study.
10.1016/j.cjtee.2025.05.001
- Author:
Panpan CHANG
1
;
Rui LI
2
;
Jiahe WEN
3
;
Guanjun LIU
3
;
Feifei JIN
2
;
Yongpei YU
4
;
Yongzheng LI
5
;
Guang ZHANG
6
;
Tianbing WANG
7
Author Information
1. Trauma Medicine Center of Peking University People's Hospital, Beijing, 100044, China.
2. Key Laboratory of Trauma and Neural Regeneration (Peking University) Ministry of Education, National Center for Trauma Medicine of China, Beijing, 100044, China.
3. Academy of Systems Engineering of Academy of Military Science of the Chinese PLA, Tianjin, 300161, China.
4. Institute of Advanced Clinical Medicine in Peking University, Beijing, 100191, China.
5. Department of Pathogeny Biology, College of Basic Medical Sciences, Jilin University, Changchun, 130021, China.
6. Academy of Systems Engineering of Academy of Military Science of the Chinese PLA, Tianjin, 300161, China. Electronic address: zhangguang01@hotmail.com.
7. Trauma Medicine Center of Peking University People's Hospital, Beijing, 100044, China; Key Laboratory of Trauma and Neural Regeneration (Peking University) Ministry of Education, National Center for Trauma Medicine of China, Beijing, 100044, China. Electronic address: wangtianbing@pkuph.edu.cn.
- Publication Type:Journal Article
- Keywords:
Cytokine storm;
Major trauma;
Multiple organ dysfunction syndrome;
Prognosis prediction model;
Prospective observational cohort study
- MeSH:
Humans;
Prospective Studies;
Middle Aged;
Male;
Female;
Adult;
Aged;
Cytokine Release Syndrome/etiology*;
Adolescent;
Young Adult;
Aged, 80 and over;
Wounds and Injuries/complications*;
Cytokines/blood*;
Multiple Organ Failure/diagnosis*;
Machine Learning
- From:
Chinese Journal of Traumatology
2025;28(6):391-398
- CountryChina
- Language:English
-
Abstract:
PURPOSE:Early mortality in major trauma has decreased, but MODS remains a leading cause of poor outcomes, driven by trauma-induced cytokine storms that exacerbate injuries and organ damage.
METHODS:This prospective cohort study included 79 major trauma patients (ISS >15) treated in the National Center for Trauma Medicine, Peking University People's Hospital, from September 1, 2021, to July 31, 2023. Patients (1) with ISS >15 (according to AIS 2015), (2) aged 15-80 years, (3) admitted within 6 h of injury, (4) having no prior treatment before admission, were included. Exclusion criteria were (1) GCS score <9 or AIS score ≥3 for TBI, (2) confirmed infection, infectious disease, or high infection risk, (3) pregnancy, (4) severe primary diseases affecting survival, (5) recent use of immunosuppressive or cytotoxic drugs within the past 6 months, (6) psychiatric patients, (7) participation in other clinical trials within the past 30 days, (8) patients with incomplete data or missing blood samples. Admission serum inflammatory cytokines and pathophysiological data were analyzed to develop machine learning models predicting MODS within 7 days. LR, DR, RF, SVM, NB, and XGBoost were evaluated based on the area under the AUROC. The SHAP method was used to interpret results.
RESULTS:This study enrolled 79 patients with major trauma, and the median (Q1, Q3) age was 51 (35, 59) years (52 males, 65.8%). The inflammatory cytokine data were collected for all participants. Among these patients, 35 (44.3%) developed MODS, and 44 (55.7%) did not. Additionally, 2 patients (2.5%) from the MODS group succumbed. The logistic regression model showed strong performance in predicting MODS. Ten key cytokines, IL-18, Eotaxin, MCP-4, IP-10, CXCL12, MIP-3α, MCP-1, IL-1RA, Cystatin C, and MRP8/14 were identified as critical to the trauma-induced cytokine storm and MODS development. Early elevation of these cytokines achieved high predictive accuracy, with an AUROC of 0.887 (95% CI 0.813-0.976).
CONCLUSION:Trauma-induced cytokine storms are strongly associated with MODS. Early identification of inflammatory cytokine changes enables better prediction and timely interventions to improve outcomes.
