Risk factors and predictors of venous thromboembolism in patients with acute spontaneous intracerebral hemorrhage: A systematic review and meta-analysis

Abstract

Background

Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a common and preventable complication of patients with acute spontaneous intracerebral hemorrhages (ICH). Knowledge of VTE risk factors in patients with acute spontaneous ICH continues to evolve while remains controversial. Therefore, this study aims to summarize the risk factors and predictors of VTE in patients with acute spontaneous ICH.

Methods

EMBASE, PubMed, Web of Science and Cochrane databases were searched for articles containing Mesh words "Cerebral hemorrhage" and "Venous thromboembolism." Eligibility screening, data extraction, and quality assessment of the retrieved articles were conducted independently by two reviewers. We performed meta-analysis to determine risk factors for the development of VTE in acute spontaneous ICH patients. Sensitivity analysis were performed to explore the sources of heterogeneity.

Results

Of the 12,362 articles retrieved, 17 cohort studies were included.Meta-analysis showed that longer hospital stay [OR=15.46, 95 % CI (12.54, 18.39), P<0.00001], infection [OR=5.59, 95 % CI (1.53, 20.42), P=0.009], intubation [OR=4.32, 95 % CI (2.79, 6.69), P<0.00001] and presence of intraventricular hemorrhage (IVH) [OR=1.89, 95 % CI (1.50, 2.38), P<0.00001] were significant risk factors for VTE in acute spontaneous ICH patients. Of the 17 studies included, five studies reported six prediction models, including 15 predictors. The area under the receiver operating curve (AUC) ranged from 0.71 to 0.95. One of the models was externally validated.

Conclusion

Infection, the intubation, presence of IVH and longer hospital stay were risk factors for the development of VTE in acute spontaneous ICH patients. Prediction models of VTE based on acute spontaneous ICH patients have been poorly reported and more research will be needed before such models can be applied in clinical settings.