Evidence map›Paper›PMID 42222482›Full record

SynthesisPeerJ2026

Risk prediction models for venous thromboembolism among patients with multiple myeloma: a systematic review and meta-analysis.

Jin Yang, You Pu, Yuqin Li, Xuelian Li, Xia Jiang, Liping Zou

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jin Yang *Department of Hematology, Sichuan Mianyang 404 Hospital, Mianyang, Sichuan, China.
You Pu *Department of Oncology, Sichuan Mianyang 404 Hospital, Mianyang, Sichuan, China.
Yuqin LiDepartment of Oncology, Sichuan Provincial People's Hospital, Chengdu, Sichuan, China.
Xuelian LiDepartment of Cardiology, Sichuan Mianyang 404 Hospital, Mianyang, Sichuan, China.
Xia JiangDepartment of Cerebrovascular Surgery, Sichuan Mianyang 404 Hospital, Mianyang, Sichuan, China.
Liping ZouDepartment of Hematology, Sichuan Mianyang 404 Hospital, Mianyang, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Multiple MyelomaVenous ThromboembolismHumansPrediction AlgorithmsRisk AssessmentRisk FactorsMeta-analysisMultiple myelomaPredictionRisk modelsVenous thromboembolism

Identifiers

PMID42222482
PMCPMC13220810

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.