Evidence map›Paper›PMID 41612447›Full record

ArticlePerioperative medicine (London, England)2026

Predicting deep vein thrombosis risk in patients with knee fractures: a nomogram-based study.

Ximin Jin, Rui Gong, Cong Wang, Yongkui Zhang

Abstract read
In one paragraph

Article in Perioperative medicine (London, England), 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

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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

4 authors.

Ximin JinShandong University of Traditional Chinese Medicine, First Clinical College, Jinan, 250355, China.
Rui GongShandong University of Traditional Chinese Medicine, First Clinical College, Jinan, 250355, China.
Cong WangShandong University of Traditional Chinese Medicine, First Clinical College, Jinan, 250355, China.
Yongkui ZhangDepartment of Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250012, China. xiaoniu0928@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aims to develop a predictive model based on clinical and laboratory indicators to assess the risk of deep vein thrombosis (DVT) in patients with knee fractures, thereby facilitating early identification of high-risk individuals and guiding personalised interventions.

methodPatients with knee fractures were randomly assigned to either a training or validation cohort in a 7:3 ratio. Discrepancy analysis was conducted to compare the variables between the two cohorts. A multiple logistic regression model was employed to identify the independent predictors of lower limb DVT in patients with knee fractures. Receiver operating characteristic (ROC) curves were subsequently utilized to evaluate the diagnostic precision of risk markers, with predictive efficacy assessed through area under the curve calculations.

resultAmong 923 hospitalised patients meeting the study criteria, we identified six independent factors—age, PTA, FIB, TT, D-dimer, and BMI—for nomogram construction from 21 variables. The area under the ROC curve for predicting lower extremity DVT was 0.75 (95% confidence interval [CI]: 0.71–0.79) in the training set and 0.76 in the validation set. Decision curve analysis confirmed the nomogram’s clinical applicability.

conclusionThe nomogram developed in this study shows intense discrimination, calibration, and clinical utility, suggesting promise for predicting lower extremity DVT and functioning as a quantitative instrument in clinical settings.

Indexed as

DVTKnee joint fracturesNomogramRisk factors

Identifiers

PMID41612447
PMCPMC12980941

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