Existing VTE risk prediction models for multiple myeloma patients demonstrated low predictive performance, with combined AUCs ranging from 0.57 to 0.68 and an overall VTE incidence of 7.9%.
Meta-Analysis
What is the predictive performance of existing VTE risk models in patients with multiple myeloma?
Existing VTE risk models for multiple myeloma patients show low predictive performance (pooled AUC < 0.7) and have limited clinical utility, highlighting the need for model updating and external validation.
Effect estimate: AUC 0.57-0.68 (95% CI 6.2-10.1)
Background Risk prediction models help identify multiple myeloma (MM) patients at high risk of venous thromboembolism (VTE) and guide clinical decisions. However, their applicability and accuracy remain unclear. This study aims to systematically review existing VTE risk models in MM patients. Methods We systematically searched PubMed, Embase, Cochrane Library, and Web of Science for studies on VTE risk prediction models in patients with MM, up to March 31, 2026. Two investigators independently screened the literature, extracted data, and assessed the risk of bias and applicability of the included studies using the PROBAST tool. Data analysis was performed using the “meta” and “metafor” packages in R software. Results A total of 14 studies on VTE risk prediction models in MM patients were included, involving the development and/or validation of seven risk assessment tools. Meta-analysis showed that the overall VTE incidence in MM patients was 7.9% (95% CI 6.2–10.1%). The combined area under the curve (AUCs) of the seven tools ranged from 0.57 to 0.68, with the IMPEDE VTE and IMPEDED VTE scores showing the best performance. Only two studies used the Hosmer-Lemeshow test for model calibration, and all studies presented the models in formula form. All included studies were at high risk of bias, mainly in the outcome and analysis domains. Conclusion Existing VTE risk models for MM patients show low predictive performance (pooled AUC < 0.7) and limited clinical use. Future research should focus on model updating and external validation to improve accuracy and applicability. PROSPERO Registration number ID: CRD420251024346.
Yang et al. (Tue,) conducted a meta-analysis in Multiple myeloma. VTE risk prediction models was evaluated on Model predictive performance (AUC) and overall VTE incidence (AUC 0.57-0.68, 95% CI 6.2-10.1). Existing VTE risk prediction models for multiple myeloma patients demonstrated low predictive performance, with combined AUCs ranging from 0.57 to 0.68 and an overall VTE incidence of 7.9%.