Evidence map›Paper›PMID 38725838›Full record

ArticleFrontiers in cardiovascular medicine2024

Prevalence and risk factors for proximal deep vein thrombosis at admission in patients with traumatic fractures: a multicenter retrospective study.

Xiaobing Liu, Peng Pang, Zhenguo Luo, Wenbo Cai, Wangyang Li, Jianhong Hao

Abstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Risk Factors for Deep Vein Thrombosis in Hospitalized Patients With Periarticular Knee Fractures Caused by High-Energy Injuries.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xiaobing LiuDepartment of Anaesthesiology, HongHui Hospital, Xi'an JiaoTong University, Xi'an, Shaanxi, China.
Peng PangDepartment of Anaesthesiology, Binzhou Medical College Affiliated Hospital, Binzhou, Shandong, China.
Zhenguo LuoDepartment of Anaesthesiology, HongHui Hospital, Xi'an JiaoTong University, Xi'an, Shaanxi, China.
Wenbo CaiDepartment of Anaesthesiology, HongHui Hospital, Xi'an JiaoTong University, Xi'an, Shaanxi, China.
Wangyang LiEmergency Department, Linfen Hospital Affiliated to Shanxi Medical University, Linfen, Shanxi, China.
Jianhong HaoDepartment of Anaesthesiology, HongHui Hospital, Xi'an JiaoTong University, Xi'an, Shaanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to determine the associated risk factors for proximal deep vein thrombosis (DVT) in patients with lower extremity and pelvic-acetabular fractures. Methods: The medical records of 4,056 patients with lower extremity and pelvic-acetabular fractures were retrospectively reviewed. The patients were classified into proximal or non-proximal DVT groups. Logistic regression models were used to determine the independent risk variables for proximal DVT. The predictive value of the related risk factors was further analyzed using receiver operating characteristic curves. Results: The prevalence of proximal DVT was 3.16%. Sex, body mass index (BMI), fracture site, injury mechanism, diabetes, coronary heart disease (CHD), injury-to-admission interval, hematocrit, platelet counts, and D-dimer levels differed significantly between the two groups. BMI ≥ 24.0 kg/m Conclusion: BMI ≥ 24.0 kg/m

Indexed as

admissionlower extremity fracturepelvic-acetabular fractureproximal venous thromboembolismrisk factor

Identifiers

PMID38725838
PMCPMC11079222

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.