Evidence map›Paper›PMID 39709353›Full record

ArticleBMC surgery2024

Study on risk factors of preoperative deep vein thrombosis in patients with lower limb fractures and construction and validation of risk prediction nomogram model.

Fan Zheng, Xiaobin Chen, Jianqiang Huang, Chen Lin

Abstract readValidation Study
In one paragraph

Article in BMC surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Spine surgery and major blood loss: how accurate is suction canister estimation?European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
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  6. Role of albumin in regulating platelet function.Frontiers in pharmacology · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Perioperative Deep Vein Thrombosis in Elderly Patients with Lower Extremity Fracture: A Comprehensive Review of Anesthetic Management Strategies.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    Review
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

4 authors.

Fan Zheng *Department of General Surgery, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, Fujian, 350025, P.R. China.
Xiaobin Chen *Department of General Surgery, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, Fujian, 350025, P.R. China. cxb104189@126.com.
Jianqiang HuangDepartment of General Surgery, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, Fujian, 350025, P.R. China.
Chen LinDepartment of General Surgery, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, Fujian, 350025, P.R. China. chenlin.med@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo explore the correlation between the levels of D-dimer (D-D), fibrinogen (FIB), fibrinogen degradation products (FDP) and platelets (PLT) in peripheral blood of patients with lower limb fractures and the formation of deep vein thrombosis in lower limbs, and to establish a new thrombosis prediction model for patients with lower limb fractures.

methodsThe patients were divided into DVT group and non DVT group according to whether there was deep vein thrombosis of the lower extremity. The differences in the levels of D-D, FIB, FDP and platelets between the two groups were analyzed and compared. ROC curve was used to evaluate the levels of D-D, FIB, FDP and PLT in the peripheral blood of patients with lower extremity fracture to predict the formation of deep vein thrombosis of the lower extremity. Logistic regression analysis was used to analyze the related risk factors of deep vein thrombosis, and the corresponding nomogram risk prediction model of lower limb deep vein thrombosis in patients with lower limb fractures was drawn according to the regression coefficient, which was verified by calibration curve, receiver operating characteristic curve (ROC) and consistency index (C-index).

resultsThe levels of D-D, FIB, FDP, and PLT in the DVT group were higher than those in the non DVT group, with statistical significance (P < 0.05); Moreover, FIB is superior to D-D, FDP, and PLT in predicting the risk of fractures and thrombosis, while PLT has the weakest predictive power. Multivariate logistic analysis showed that platelet, D-D, FIB and FDP were independent risk factors for deep vein thrombosis in patients with lower limb fractures (P < 0.05); Based on the independent risk factors mentioned above, the complex logistic regression formula was transformed into a visual column chart, and the consistency index (C-index) was 0.962 and 0.936, and the external verification C-index was 0.841. The calibration curve showed that the nomogram is in high agreement with the actual results. The AUC value of ROC curve indicated that the nomogram has high prediction value.

conclusionsThe levels of D-D, FIB, FDP and PLT in peripheral blood of patients with lower limb fracture and DVT were significantly increased. Early monitoring of D-D, FIB, FDP and PLT levels in patients with lower limb fracture can effectively screen for lower limb deep vein thrombosis.

Indexed as

NomogramsVenous ThrombosisAdultAgedFemaleFibrin Fibrinogen Degradation ProductsFibrinogenFractures, BoneHumansLower ExtremityMaleMiddle AgedRetrospective StudiesRisk AssessmentRisk FactorsROC CurveFibrin Fibrinogen Degradation Productsfibrin fragment DFibrinogenDeep vein thrombosis of lower extremityFibrinogenLower limb fractureNomogramRisk factors

Identifiers

PMID39709353
PMCPMC11662550

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LicenceCC BY-NC-ND
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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.