Evidence map›Paper›PMID 41158977›Full record

ArticleRisk management and healthcare policy2025

Epidemiology, Risk Factors, and Predictive Modelling of Post-Traumatic Sepsis: A Retrospective Cohort Study.

Xue Fu, Zhen-Yi Wang, Yan-Jun Qin, Xue-Xia Cao, Wang-Sheng Deng, Chen Dai, De-Zheng Hu, Shi-Min Dong

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Article in Risk management and healthcare policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Xue Fu *Department of Emergency Medicine, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, 050011, People's Republic of China.
Zhen-Yi Wang *Department of Emergency Medicine, Shenzhen Longhua District People's Hospital, Shenzhen, Guangdong, 518000, People's Republic of China.
Yan-Jun QinDepartment of Emergency Medicine, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, 050011, People's Republic of China.
Xue-Xia CaoDepartment of Emergency Medicine, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, 050011, People's Republic of China.
Wang-Sheng DengDepartment of Emergency Medicine, Shenzhen Longhua District People's Hospital, Shenzhen, Guangdong, 518000, People's Republic of China.
Chen DaiDepartment of Emergency Medicine, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, 050011, People's Republic of China.
De-Zheng HuDepartment of Emergency Medicine, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, 050011, People's Republic of China.
Shi-Min DongDepartment of Emergency Medicine, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, 050011, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to evaluate the distribution spectrum and epidemiological characteristics associated with post-traumatic sepsis and to identify associated risk factors. Methods: This retrospective study analyzed data from 722 patients with traumatic injuries admitted to the Emergency Department of the Hebei Medical University Third Hospital between January 1, 2021, and November 30, 2023. Participants were categorized into two groups: those who developed sepsis and those who did not. Patients diagnosed with sepsis were further categorized into survival and non-survival subgroups. Patient demographics, injury characteristics, and clinical variables were collected. Sepsis occurrence was assessed within the first week post-injury. Multivariate logistic regression analysis was performed to identify independent risk factors for post-traumatic sepsis. Results: Among 722 trauma patients, 189 developed sepsis. In the sepsis cohort, injuries were mainly from traffic accidents (54.5%), falls from heights (17.46%), crush injuries (13.76%), and falls/collisions (11.64%). In contrast, non-sepsis cases (n=533) were predominantly due to falls/collisions (43.15%) and traffic accidents (36.02%). Pulmonary infection was the leading site in both survivors (95.62%) and non-survivors (100%), with some patients presenting multiple infection sites. A predictive model for post-traumatic sepsis, incorporating 10 variables such as hospitalization length and injury site number, achieved excellent performance (AUROC 0.998). A sepsis mortality model, based on five variables including age and injury sites, also showed high accuracy (AUROC 0.969). Conclusion: Traffic accidents were the primary cause of post-traumatic sepsis. Key risk factors included injury severity, CRP level, and hospitalization duration. Independent predictors of 28-day mortality included age, organ failure score, and vasoactive drug use.

Indexed as

epidemiological characteristicsISSpost-traumatic sepsispredictive modellingreceiver operating characteristic curverisk factorROC curveSOFA score

Identifiers

PMID41158977
PMCPMC12558096

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