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Risk prediction models for venous thromboembolism in stroke: a systematic review and meta-analysis
Lina Fu1,2, Chunyan Cui2, Kairu Feng1
1Institute of Environment and Health, South China Hospital, Medical School, Shenzhen University, Shenzhen, China.
Frontiers in Cardiovascular Medicine
|May 11, 2026
Summary
This systematic review evaluated venous thromboembolism (VTE) risk prediction models in stroke patients. All models showed high risk of bias and lacked external validation, despite apparent good performance.
Area of Science:
- Neurology
- Cardiology
- Public Health
Background:
- Venous thromboembolism (VTE) is a significant complication in stroke patients.
- Accurate risk prediction models are crucial for timely intervention and improved patient outcomes.
- Existing VTE risk prediction models for stroke patients require systematic evaluation.
Purpose of the Study:
- To systematically evaluate the risk prediction models for VTE in stroke patients.
- To assess the performance, risk of bias, and applicability of existing VTE prediction models.
- To provide a reference for future model development and clinical research in stroke VTE prediction.
Main Methods:
- A systematic literature search was performed across multiple databases (PubMed, Embase, Web of Science, etc.) up to September 1, 2025.
- The PROBAST checklist was used to assess the risk of bias and applicability of the included prediction models.
- Meta-analyses were conducted to estimate pooled VTE incidence and model performance (Area Under the Curve - AUC).
Main Results:
- Seven VTE risk prediction models were identified from 2,726 retrieved records.
- Reported VTE incidence ranged from 9.8% to 38.9%; AUC values were between 0.781 and 0.978.
- All included models exhibited a high overall risk of bias and lacked independent external validation; pooled AUC was 0.87.
Conclusions:
- Despite favorable apparent predictive performance, all evaluated VTE risk prediction models for stroke patients demonstrated a high risk of bias.
- The lack of external validation necessitates caution when interpreting the performance of these models.
- Future research should focus on rigorous validation and refinement of existing models or development of new, methodologically sound models for better clinical decision-making.
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