ArticleScientific reports2024
Development and validation of a predictive model for preoperative deep vein thrombosis following traumatic thoracolumbar fractures.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning prediction models for deep vein thrombosis in hospitalized patients: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Article
- Dynamic Evolution Characteristics and Nursing Intervention Windows of Perioperative Deep Vein Thrombosis in Patients with Lower Extremity Traumatic Fractures: A Prospective Cohort Study.Therapeutics and clinical risk management · 2026Article
- Article
- Nonlinear relationship and threshold effect of D-dimer on preoperative deep vein thrombosis in patients with ankle fractures: a retrospective study.Frontiers in surgery · 2026Article
- Analysis of risk factors and prediction model construction of deep vein thrombosis in patients with lumbar degenerative diseases before surgery.Scientific reports · 2025Article
- A clinical risk prediction model for perioperative lower extremity DVT in patients undergoing spinal fracture surgery.Frontiers in surgery · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Although a sequential work-up for deep vein thrombosis has reached agreement worldwide, the mysterious nature of DVT following fractures brings challenges to early diagnosis and intervention. The objective of the present study was to develop and validate a nomogram for predicting preoperative DVT risk in patients with thoracolumbar fractures using readily available clinical data. Of the 1350 patients, 930 were randomly assigned to the training cohort. A prediction model was established and visualized as a nomogram based on eight predictors related to preoperative DVT. The performance of the model was tested by the receiver operating characteristic curve, Hosmer-Lemeshow test, calibration curve, and decision curve analysis. We further verified the model in the validation cohort. The AUCs of the prediction model were 0.876 and 0.853 in training and validation cohorts, respectively. The Hosmer-Lemeshow test demonstrated good fitness in the training set (X
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Registered trials
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 Socratic graph.