Risk prediction models for deep venous thrombosis in patients with acute stroke: A systematic review and meta-analysis

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BackgroundThe number of risk prediction models for deep venous thrombosis (DVT) in patients with acute stroke is increasing, while the quality and applicability of these models in clinical practice and future research remain unknown. ObjectiveTo systematically review published studies on risk prediction models for DVT in patients with acute stroke. DesignSystematic review and meta-analysis of observational studies. MethodsChina National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), SinoMed, PubMed, Web of Science, The Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Embase were searched from inception to November 7, 2022. Data from selected studies were extracted, including study design, data source, outcome definition, sample size, predictors, model development and performance. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was used to assess the risk of bias and applicability. ResultsA total of 940 studies were retrieved, and after the selection process, nine prediction models from nine studies were included in this review. All studies utilized logistic regression to establish DVT risk prediction models. The incidence of DVT in patients with acute stroke ranged from 0.4 % to 28 %. The most frequently used predictors were D-dimer and age. The reported area under the curve (AUC) ranged from 0.70 to 0.912. All studies were found to have a high risk of bias, primarily due to inappropriate data sources and poor reporting of the analysis domain. The pooled AUC value of the five validated models was 0.76 (95 % confidence interval: 0.70–0.81), indicating a fair level of discrimination. ConclusionAlthough the included studies reported a certain level of discrimination in the prediction models of DVT in patients with acute stroke, all of them were found to have a high risk of bias according to the PROBAST checklist. Future studies should focus on developing new models with larger samples, rigorous study designs, and multicenter external validation. RegistrationThe protocol for this study is registered with PROSPERO (registration number: CRD42022370287).