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Risk Prediction Models for Deep Vein Thrombosis in Patients With Spontaneous Intracerebral Hemorrhage: A Systematic
Yana Xing1, Weixin Cai2, Lin Wang1
1Intensive Care Unit, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Summary
Risk models for deep vein thrombosis (DVT) in patients with spontaneous intracerebral hemorrhage (sICH) show promise. However, current models require further validation before clinical use due to bias and heterogeneity.
Area of Science:
- Medical Research
- Clinical Epidemiology
- Neurology
Background:
- Spontaneous intracerebral hemorrhage (sICH) patients face a high risk of deep vein thrombosis (DVT).
- Accurate risk prediction models are crucial for effective thromboprophylaxis in this population.
- Existing models need systematic evaluation for clinical applicability.
Purpose of the Study:
- To systematically review and critically evaluate risk prediction models for DVT in sICH patients.
- To provide evidence-based references for clinical practice regarding DVT risk stratification.
- To identify key predictors for DVT in sICH.
Main Methods:
- Comprehensive literature search across multiple databases (PubMed, Embase, Scopus, etc.) up to July 2025.
- Independent screening, data extraction, and risk of bias assessment using the PROBAST checklist.
- Meta-analysis of AUC values for validated models and subgroup analyses to address heterogeneity.
Main Results:
- 19 risk prediction models were identified across 15 studies.
- Reported AUC values ranged from 0.710 to 0.988; pooled AUC for 11 validated models was 0.82 (95% CI: 0.78-0.85).
- Age, D-dimer, hematoma volume, and GCS score were significant DVT predictors in sICH.
Conclusions:
- Current DVT risk models for sICH demonstrate promising discriminative ability but lack robustness.
- High risk of bias and heterogeneity limit direct application in routine clinical decision-making.
- Key predictors (age, D-dimer, hematoma volume, GCS) can aid qualitative risk stratification and individualized thromboprophylaxis.
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