Risk Prediction Models for Deep Vein Thrombosis in Patients With Spontaneous Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis

ObjectivesTo systematically review and critically evaluate the risk prediction models for DVT in patients with sICH and provide references for clinical practice.MethodsPubMed, Web of Science, The Cochrane Library, CINAHL, Embase, Scopus, CNKI, Wanfang Database, and China Science and VIP were searched from inception to July 15, 2025. Two researchers independently screened the literature, extracted data and assessed the risk of bias and applicability using the PROBAST checklist. The AUC values were pooled using a random-effects model. Sensitivity and subgroup analyses were conducted to explore potential sources of heterogeneity.ResultsA total of 9414 studies were initially identified, 19 risk prediction models were developed across the 15 included studies. The reported AUC across the 15 included studies ranged from 0.710 to 0.988. The pooled AUC for the 11 validated models was 0.82 (95% confidence interval: 0.78-0.85). Age, D-dimer levels, hematoma volume, and GCS score on admission emerged as the most frequently significant predictors of DVT in sICH patients.ConclusionsExisting models show promising but not yet robust discriminative properties. Owing to the high risk of bias and substantial heterogeneity across studies, these models cannot be directly applied to routine clinical decision-making. Key variables including age, D-dimer level, hematoma volume and GCS score can support qualitative clinical risk stratification, and provide practical reference for formulating precise and individualized thromboprophylaxis strategies for sICH patients.