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Analysis of risk factors and prediction model construction of deep vein thrombosis in patients with lumbar degenerative diseases before surgery
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Analysis of risk factors and prediction model construction of deep vein thrombosis in patients with lumbar degenerative diseases before surgery
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Journal Article
Analysis of risk factors and prediction model construction of deep vein thrombosis in patients with lumbar degenerative diseases before surgery
2025
Overview
Patients with lumbar degenerative diseases (LDD) are particularly susceptible to preoperative deep vein thrombosis (DVT) due to prolonged immobility and associated pathophysiological changes. If left undetected, preoperative DVT may progress postoperatively and lead to life-threatening complications such as pulmonary embolism, underscoring the importance of accurate risk assessment. This retrospective study aims to develop and validate a nomogram for predicting the risk of preoperative DVT in LDD patients using available clinical data. A total of 568 patients with LDD were included, of whom 39 (6.87%) were diagnosed with preoperative DVT. Variables were initially screened using the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariate logistic regression to identify independent predictors. Five risk factors—age, walking impairment, diabetes mellitus, activated partial thromboplastin time (APTT), and D-dimer—were ultimately selected. Then, the dataset was randomly divided into a training cohort (
n
= 398) and a validation cohort (
n
= 170) in a 7:3 ratio. A predictive nomogram incorporating these risk factors was developed and validated. The predictive performance of the nomogram was evaluated using the concordance index (C-index), calibration curves, and the area under the receiver operating characteristic (ROC) curve (AUC). Decision curve analysis (DCA) was conducted to assess the clinical utility and applicability of the nomogram. The nomogram demonstrated excellent predictive accuracy (Training cohort AUC: 0.87, Validation cohort AUC: 0.97; Training cohort C-index: 0.874, Validation cohort C-index: 0.967), calibration, and clinical applicability. Additionally, a dynamic online nomogram was created for practical clinical application [
https://yangt.shinyapps.io/myDynNom/
].
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ 692/499
/ Adult
/ Aged
/ Blood
/ Diabetes
/ Embolism
/ Female
/ Humanities and Social Sciences
/ Humans
/ Intervertebral Disc Degeneration - complications
/ Intervertebral Disc Degeneration - surgery
/ Lumbar Vertebrae - pathology
/ Male
/ Nomogram
/ Patients
/ Science
/ Software
/ Training
