Evidence map›Paper›PMID 33302974›Full record

ArticleJournal of orthopaedic surgery and research2020

Analysis of perioperative risk factors for deep vein thrombosis in patients with femoral and pelvic fractures.

Linqin Wu, Bo Cheng

Open access · goldAbstract read
In one paragraph

Article in Journal of orthopaedic surgery and research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 9% of its field
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

26 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Venous thromboembolism in patients with traumatic pelvic and acetabular fractures: a retrospective cohort study.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2025
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  14. Venous thromboembolism in orthopedic surgery: Global guidelines.Acta orthopaedica et traumatologica turcica · 2023
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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

2 authors at 1 institution in 1 country.

Linqin WuDepartment of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, 400000, China.
Bo ChengDepartment of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, 400000, China. 7300703@qq.com.
First Affiliated Hospital of Chongqing Medical University · CN

Funding

Fundamental Research Funds for the Key Research Program of Chongqing Science and Technology Commission Chongqing Medical Key Discipline project Yu Health Science and Education (2007)2National Key Clinical Specialty Discipline Construction Program of China Social Security Administration of the Ministry of Finance (2011) No. 170
6 · The paper itself

Abstract

objectiveClinical characteristics, anticoagulant protocols, and risk factors of deep vein thrombosis (DVT) in patients with femoral and pelvic fractures were analyzed throughout the perioperative period to provide references for early identification and optimization of risk factors.

methodsThis was a retrospective study. A total of 569 patients undergoing surgery of femoral and pelvic fractures from May 2018 to December 2019 were included. The clinical data including general conditions, trauma, surgery, anticoagulant protocols, and laboratory indexes were collected. According to the results of deep vein Doppler ultrasonography of the lower extremities, the patients were divided into non-DVT group and DVT group. Univariate analysis and multivariate logistic regression analysis were used to identify the independent risk factors of preoperative and postoperative DVT.

resultsThe incidence of DVT was 40.25% and preoperative DVT was 26.71%, which was higher than the incidence of postoperative DVT of 17.22%. Most of them were thrombus on the affected side (60.26%) and distal thrombus (81.66%). The average time of DVT formation was 6.55 ± 0.47 days after trauma and 6.67 ± 0.48 days after surgery. Chronic obstructive pulmonary disease (COPD), anemia, hypoproteinemia, non-anticoagulation before surgery, delayed anticoagulation after trauma and admission, high-energy trauma, multiple injuries, drinking history, and advanced age were independent risk factors for perioperative DVT. The increased level of fibrinogen degradation products was an independent risk factor for preoperative DVT. These risk factors were identified to be independently associated with postoperative DVT, including intraoperative blood transfusion, postoperative blood transfusion, pulmonary infection, preoperative non-anticoagulation, postoperative delayed anticoagulation, preoperative waiting time > 7 days, operative time > 2 h, c-reactive protein, fibrinogen level, platelet count 1 day after surgery, c-reactive protein, fibrinogen, and hemoglobin levels 3 days after surgery, comminuted fracture.

conclusionsAt present, anticoagulation and other DVT prevention and treatment programs have not changed the current situation that the incidence of DVT is still high. Through the analysis of the risk factors of DVT throughout the perioperative period, optimizing the perioperative blood transfusion, preoperative lung disease, hypoproteinemia, anemia, inflammation, etc., and surgery as soon as possible after trauma may further reduce its incidence.

Indexed as

AdultAgedAged, 80 and overAnticoagulantsFemaleFemoral FracturesHumansLower ExtremityMaleMiddle AgedPerioperative PeriodPostoperative ComplicationsRetrospective StudiesRisk FactorsUltrasonography, DopplerVenous ThrombosisAnticoagulantsDeep vein thrombosisFemoral fracturesPelvic fracturesPerioperative periodRisk factors

Identifiers

PMID33302974
PMCPMC7731763
OpenAlexW3112228451

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LicenceCC BY
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Registered trials

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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.