BACKGROUND: Once considered inoperable lesions in inviolable territory, brainstem cavernous malformations (BSCM) are now surgically curable with acceptable operative morbidity. Recommending surgery is a difficult decision that would be facilitated by a grading system designed specifically for BSCMs that predicted surgical outcomes.
OBJECTIVE: Informed by our efforts to develop a supplementary grading system for arteriovenous malformations, we hypothesized that a similar system might predict long-term outcomes and guide clinical decision-making.
METHODS: A consecutive, single-surgeon series of 104 patients was used to assess preoperative clinical and imaging predictors of microsurgical outcomes. Univariable logistic regression identified predictors and a multivariable logistic regression model tested the association of the combined predictors with final modified Rankin Scale scores. A grading system assigned points for lesion size, location crossing the brainstem’s midpoint, presence of developmental venous anomaly, age, and time from last hemorrhage to surgery.
RESULTS: Average maximal diameter of BSCMs was 19.5 mm; 50% crossed the axial midpoint; 54.8% had developmental venous anomalies; mean age was 42.1 years; and median time from last hemorrhage to surgery was 60 days. One patient died (0.96%), and 15 patients (14.4%) experienced worsened cranial nerve or motor dysfunction, of which 10 increased their modified Rankin Scale scores (9.6%). BSCM grades ranged from 0 to 7 points and predicted outcomes with high accuracy (receiver-operating characteristics = 0.86, 95% confidence interval: 0.78-0.94).
CONCLUSION: Rather than developing a grading system for all cerebral cavernous malformations that is weak with BSCMs, we propose a system for the patients who need it most. The BSCM grading system differentiates patients who might expect favorable surgical outcomes and offers guidance to neurosurgeons forced to select these patients.
From: Brainstem Cavernous Malformations: Surgical Results in 104 Patients and a Proposed Grading System to Predict Neurological Outcomes by Garcia et al.
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