What question did this study set out to answer?

The study aims to develop a risk prediction model specifically for DVT in postoperative patients using vascular access devices.

April 16, 2026Open Access

Construction and Validation of a Risk Prediction Model for Postoperative Lower Extremity Deep Vein Thrombosis in Patients with Vascular Access Devices: A Retrospective Analysis

Key Points

The study aims to develop a risk prediction model specifically for DVT in postoperative patients using vascular access devices.
Retrospective cohort study with 156 postoperative VAD users.
Participants assigned to a conventional nursing group and an access management group.
Clinical data collected with screening via univariate and multivariate logistic regression.
Model performance validated using ROC curves and 5-fold cross-validation.
Central venous catheter use and prior thrombosis history are significant predictors of DVT.
Model demonstrates good discriminative ability (AUC=0.83).
DVT incidence increases with risk categories (1.85%, 6.94%, 16.67%).
Access management group shows lower DVT incidence and higher prevention effectiveness.

Abstract

Objective: Postoperative patients with indwelling vascular access devices (VADs) face a high risk of lower extremity deep vein thrombosis (DVT). This study sought to develop a VAD-specific DVT risk prediction model and verify the efficacy of a targeted three-phase VAD management system. Methods: A retrospective cohort study enrolled 156 postoperative VAD users (August 2023–August 2024), equally assigned to a conventional nursing group and an access management group. Clinical data were collected, and variables were screened via clinical relevance and univariate logistic regression (P< 0.05), followed by multivariate logistic regression to build the prediction model. The model’s performance was validated using ROC curves, Hosmer–Lemeshow test, and Brier score, with 5-fold cross-validation for stability. The three-phase system included pre-infusion VAD optimization, intra-infusion targeted monitoring, and post-infusion risk stratification with DVT screening. Results: Central venous catheter (CVC) use (OR=2.92, P=0.005) and prior thrombosis history (OR=3.61, P=0.002) were identified as independent predictors. The model exhibited good discriminative ability (AUC=0.83, 95% CI: 0.76– 0.90) and stability (average AUC=0.81± 0.03). DVT incidence showed a significant risk gradient (1.85% vs.6.94% vs.16.67%, P=0.013). The access management group had significantly lower VAD-related complications and DVT incidence, with higher DVT prevention effectiveness and cognitive scores (all P< 0.05). Conclusion: The VAD-specific DVT risk prediction model achieves effective risk stratification for postoperative VAD users, and the three-phase management system markedly reduces VAD-related complications and lower extremity DVT incidence. This integrated strategy bridges the gap between VAD care and DVT prevention, providing actionable clinical guidance to enhance postoperative VAD use safety with evidence-based support. Keywords: lower extremity deep vein thrombosis, vascular access device, risk prediction model, catheter-related thrombosis, nursing intervention

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