Evidence map›Paper›PMID 42039878›Full record

ArticleRisk management and healthcare policy2026

Development and Validation of a Postoperative LEDVT Risk Assessment System for Severe Traumatic Brain Injury.

Minling Mo, Dahong Zhai, Nina Tao, Na Zhang, Yajuan Zhang, Guqing Zeng

Abstract read
In one paragraph

Article in Risk management and healthcare policy, 2026. 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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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Minling MoDepartment of Nursing, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, People's Republic of China.
Dahong ZhaiSchool of Nursing, University of South China, Hengyang, Hunan Province, 421001, People's Republic of China.
Nina TaoSchool of Nursing, University of South China, Hengyang, Hunan Province, 421001, People's Republic of China.
Na ZhangDepartment of Nursing, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, People's Republic of China.
Yajuan ZhangDepartment of Nursing, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, People's Republic of China.
Guqing ZengSchool of Nursing, University of South China, Hengyang, Hunan Province, 421001, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to develop a structured risk assessment system for postoperative lower extremity deep vein thrombosis (LEDVT) in patients with severe traumatic brain injury (sTBI), and to validate its content and reliability through expert consensus. Methods: The system was designed based on evidence from a comprehensive literature review and refined through a two-round Delphi expert consensus process involving 16 multidisciplinary experts. Indicator weights were assigned using an analytic hierarchy process combined with expert scoring. Results: The finalized framework incorporated 6 primary domains-demographic characteristics, trauma-related indicators, coagulation parameters, comorbidities, iatrogenic factors, and venous thromboembolism prophylaxis-encompassing 19 secondary indicators. Response rates for two rounds of the expert questionnaire were 100%. The expert authority coefficient was 0.931. The coefficients of variation for the second-round indicators ranged from 0% to 7.01% and Kendall's coefficient of concordance was 0.670 ( Conclusion: A comprehensive risk prediction indicator system for LEDVT following sTBI surgery was established. The system is suitable for direct integration into hospital information systems, with weighted indicators reflecting clinical priorities and supporting the development of real-time monitoring modules.By enabling early identification of high-risk patients, this tool can facilitate timely prophylactic interventions, enhance clinical decision-making, and ultimately reduce the incidence of postoperative LEDVT, thereby improving patient safety and overall quality of care.

Indexed as

assessment indicatorsDelphi methodlower extremity deep venous thrombosisnursingrisk factorssevere traumatic brain injury

Identifiers

PMID42039878
PMCPMC13110019

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