Untreated brain arteriovenous malformation: Patient-level meta-analysis of hemorrhage predictors

MARS Coinvestigators, Helen Kim, Rustam Al-Shahi Salman, Charles E McCulloch, Christian Stapf, William L Young

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

OBJECTIVE: To identify risk factors for intracranial hemorrhage in the natural history course of brain arteriovenous malformations (AVMs) using individual patient data meta-analysis of 4 existing cohorts.

METHODS: We harmonized data from Kaiser Permanente of Northern California (n = 856), University of California San Francisco (n = 787), Columbia University (n = 672), and the Scottish Intracranial Vascular Malformation Study (n = 210). We censored patients at first treatment, death, last visit, or 10-year follow-up, and performed stratified Cox regression analysis of time-to-hemorrhage after evaluating hemorrhagic presentation, sex, age at diagnosis, deep venous drainage, and AVM size as predictors. Multiple imputation was performed to assess impact of missing data.

RESULTS: A total of 141 hemorrhage events occurred during 6,074 patient-years of follow-up (annual rate of 2.3%, 95% confidence interval [CI] 2.0%-2.7%), higher for ruptured (4.8%, 3.9%-5.9%) than unruptured (1.3%, 1.0%-1.7%) AVMs at presentation. Hemorrhagic presentation (hazard ratio 3.86, 95% CI 2.42-6.14) and increasing age (1.34 per decade, 1.17-1.53) independently predicted hemorrhage and remained significant predictors in the imputed dataset. Female sex (1.49, 95% CI 0.96-2.30) and exclusively deep venous drainage (1.60, 0.95-2.68, p = 0.02 in imputed dataset) may be additional predictors. AVM size was not associated with intracerebral hemorrhage in multivariable models (p > 0.5).

CONCLUSION: This large, individual patient data meta-analysis identified hemorrhagic presentation and increasing age as independent predictors of hemorrhage during follow-up. Additional AVM cohort data may further improve precision of estimates, identify new risk factors, and allow validation of prediction models.

Original languageEnglish
Pages (from-to)590-7
Number of pages8
JournalNeurology
Volume83
Issue number7
DOIs
Publication statusPublished - 12 Aug 2014

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