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Objective <p>The objective of this study was to evaluate risk assessment models (RAMs) for venous thromboembolism (VTE) in surgical inpatients.</p> Background <p>VTE significantly contributes to morbidity and mortality among surgical inpatients, with the postoperative period being particularly vulnerable. Accurate risk assessment is essential for guiding thromboprophylaxis. Although various RAMs have been developed, their comparative effectiveness in surgical populations remains unclear.</p> Methods <p>This retrospective cohort study, SURG-VTE, was conducted across four centers from 2020 to 2022. This study evaluated the predictive performance of six RAMs: Caprini, Padua, Wells, Geneva, Autar, and IMPROVE scores. The primary outcome was objectively confirmed symptomatic VTE. The association between RAMs and VTE was examined using logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to assess each model’s discrimination. Calibration was assessed using the GiViTI calibration belt, while overall performance was quantified by the Brier score. Decision Curve Analysis (DCA) was performed to evaluate the clinical utility of the RAMs. Sensitivity and subgroup analyses were conducted to further assess the models’ performance.</p> Results <p>Of the 4851 surgical inpatients, 826 (17.0%) experienced VTE, with significant differences in VTE rates between high- and low-risk groups as classified by most RAMs. However, the overall discriminative performance of the RAMs was poor, with AUCs ranging from 0.594 for the Geneva score to 0.713 for the Padua score. Sensitivity was highest for the Autar score (70.5%) and specificity for the Wells score (99.2%). The clinical utility of RAMs was further questioned as the positive net benefit was suboptimal for all models. Similar results were observed in sensitivity and subgroup analyses.</p> Conclusions <p>While RAMs can distinguish between high- and low-risk groups for VTE, but show poor discrimination, limited precision, and suboptimal calibration (except IMPROVE), suggesting the need for more reliable and clinically relevant VTE risk prediction strategies in surgical care.</p> Trial registration ClinicalTrials.gov <p>NCT06502600</p>

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Risk assessment models for venous thromboembolism in surgical inpatients: a multicenter retrospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Mengying Xie,
  • Zhiliang Zhou,
  • Fan Fei,
  • Yingbin Deng,
  • Chen Liu,
  • Wenyi Jin,
  • Fengyu Chen,
  • Liang Wang,
  • Chan Chen,
  • Zhiyi Wang,
  • Jie Weng,
  • Zhe Xu

摘要

Objective

The objective of this study was to evaluate risk assessment models (RAMs) for venous thromboembolism (VTE) in surgical inpatients.

Background

VTE significantly contributes to morbidity and mortality among surgical inpatients, with the postoperative period being particularly vulnerable. Accurate risk assessment is essential for guiding thromboprophylaxis. Although various RAMs have been developed, their comparative effectiveness in surgical populations remains unclear.

Methods

This retrospective cohort study, SURG-VTE, was conducted across four centers from 2020 to 2022. This study evaluated the predictive performance of six RAMs: Caprini, Padua, Wells, Geneva, Autar, and IMPROVE scores. The primary outcome was objectively confirmed symptomatic VTE. The association between RAMs and VTE was examined using logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to assess each model’s discrimination. Calibration was assessed using the GiViTI calibration belt, while overall performance was quantified by the Brier score. Decision Curve Analysis (DCA) was performed to evaluate the clinical utility of the RAMs. Sensitivity and subgroup analyses were conducted to further assess the models’ performance.

Results

Of the 4851 surgical inpatients, 826 (17.0%) experienced VTE, with significant differences in VTE rates between high- and low-risk groups as classified by most RAMs. However, the overall discriminative performance of the RAMs was poor, with AUCs ranging from 0.594 for the Geneva score to 0.713 for the Padua score. Sensitivity was highest for the Autar score (70.5%) and specificity for the Wells score (99.2%). The clinical utility of RAMs was further questioned as the positive net benefit was suboptimal for all models. Similar results were observed in sensitivity and subgroup analyses.

Conclusions

While RAMs can distinguish between high- and low-risk groups for VTE, but show poor discrimination, limited precision, and suboptimal calibration (except IMPROVE), suggesting the need for more reliable and clinically relevant VTE risk prediction strategies in surgical care.

Trial registration ClinicalTrials.gov

NCT06502600