What is the clinical evidence from this study?

Study design: Observational. Population: Surgical patients aged 75 years and older (n=686). Primary outcome: Incidence of deep vein thrombosis (DVT) diagnosed by ultrasound within 7 days post-surgery (ORs for predictors in model).

What does this research mean for the field?

A nomogram incorporating malignancy, sex, anesthesia duration, D-dimer, platelet count, and pneumatic tourniquet application effectively predicts deep vein thrombosis risk with an AUC of 0.786 in surgical patients aged 75 years and older. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aims to identify risk factors for DVT and develop a predictive tool for older surgical patients.

March 7, 2026Open Access

Development and validation of a deep vein thrombosis risk assessment tool for surgical patients aged 75 years and older

HYHeqing YeUniversity of Science and Technology of China JSJun SunSichuan University LDLibing DuUniversity of Science and Technology of China

Key Result

A nomogram incorporating malignancy (OR 7.590), sex (OR 0.387), anesthesia duration (OR 1.010), D-dimer (OR 1.210), platelet count (OR 1.010), and pneumatic tourniquet application (OR 2.700) predicted DVT with an AUC of 0.786 in surgical patients aged 75 years and older.

Key Points

This study aims to identify risk factors for DVT and develop a predictive tool for older surgical patients.
Conducted a retrospective analysis of surgical patients aged 75 and older.
Utilized LASSO regression for predictor selection and multivariable logistic regression for model development.
Applied decision curve analysis to evaluate clinical efficacy and performed internal validation via bootstrap resampling and 10-fold cross-validation.
Identified six significant predictors for DVT: malignancy, sex, anesthesia duration, D-dimer, platelet count, and pneumatic tourniquet application.
Achieved a DVT incidence of 14.7% among patients analyzed.
Developed nomogram demonstrated good discrimination (AUC = 0.786) and calibration (P = 0.588).
The model sustained strong performance with a C-index of 0.791 upon internal validation.

Study Design

Type

Observational (n=686)

Multicenter

Structured PICO

Population

686 surgical patients aged 75 years and older at a tertiary general hospital in Hefei, China. Median age 78, 65.8% female. Excluded if preoperative diagnosis of DVT, known coagulation disorder, or history of severe cardiovascular or cerebrovascular disease.

Outcome

Incidence of deep vein thrombosis (DVT) diagnosed by lower limb compression ultrasound within 7 days post-surgeryhard clinical

A novel nomogram incorporating six readily available clinical variables provides accurate individualized risk prediction for deep vein thrombosis in surgical patients aged 75 years and older.

Main Result

Effect estimate: ORs for predictors in model

Limitations

Retrospective design potentially limits causal inference.
Single-center study limits generalizability.
No external validation reported beyond internal bootstrap and cross-validation.
Missing data imputed, which may introduce bias despite sensitivity analyses.

Abstract

Deep vein thrombosis (DVT) is a common and severe medical condition characterized by the formation of thrombi in deep veins, primarily affecting older surgical patients. The present study aimed to identify risk factors for DVT in surgical patients aged 75 years and older and subsequently develop and validate a risk assessment tool for this patient population. A retrospective study was conducted on surgical patients (n = 686) aged 75 years and older at a tertiary general hospital in Hefei, China, from January to December 2024. Predictors for the model were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by multivariable logistic regression. Area under the curve, calibration curve, and decision curve analysis (DCA) were used to examine the discriminative power, calibration, and clinical efficacy of the predictive models. Internal validation was performed using both bootstrap resampling and 10-fold cross-validation. The incidence of DVT among surgical patients aged 75 years and older was 14.7% (n = 101/686). Six predictors were identified and used to establish a nomogram: malignancy (OR: 7.590, 95% CI: 2.670–21.500), sex (OR: 0.387, 95% CI: 0.195–0.724), anesthesia duration (OR: 1.010, 95% CI: 1.006–1.014), D-dimer (OR: 1.210, 95% CI: 1.130–1.310), platelet count (OR: 1.010, 95% CI: 1.005–1.015), and pneumatic tourniquet application (OR: 2.700, 95% CI: 1.470–5.170). The nomogram demonstrated excellent discrimination (AUC = 0.786, 0.786 (95% CI, 0.738–0.834) and good calibration (Hosmer-Lemeshow test, P = 0.588). Upon interval validation, the model achieved a concordance index (C-index) of 0.791 (95% CI, 0.780–0.800). Finally, DCA demonstrated the net clinical benefit of this nomogram. This study constructed a practical model to predict DVT in surgical patients aged 75 years and older. This model incorporates demographic characteristics and clinical risk factors, enabling individualized prediction.

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