Transcranial Doppler is associated with future ischemic events in moyamoya arteriopathy

Moyamoya arteriopathy confers a high stroke risk. We hypothesized that elevated cerebral blood flow velocities (CBFV), as measured by transcranial Doppler (TCD) ultrasound, are associated with future ischemic events in individuals with moyamoya.

In this prospective observational study, participants ≤ 26-years-old with moyamoya or moyamoya-like arteriopathy who had not undergone surgical revascularization underwent TCD. Receiver operating characteristic curve analysis was performed using ischemic event (clinical stroke, silent cerebral infarct, or transient ischemic attack) following TCD acquisition as the dependent variable and middle cerebral artery (MCA) CBFV as the independent variable. Optimal cutoff velocity was determined using the Youden index.

Nineteen participants with moyamoya, median age 7.4 years, were enrolled. Over median follow-up of 2.4 years, four participants (21%) experienced a total of six ischemic events and seven participants (37%) underwent surgical revascularization. Median MCA velocity at enrollment was higher in participants with subsequent side-congruent ischemic events (179 cm/s vs 127 cm/s, p = 0.021). MCA velocity had excellent discernment of risk for future side-congruent strokes (AUC 0.819, 95% CI, 0.60-1.0). The optimal cutoff velocity was 164 cm/s (sensitivity 0.80, specificity 0.87).

TCD may have a role in stroke risk stratification and surgical decision-making in moyamoya arteriopathy.

Middle cerebral artery blood flow velocities, as measured by transcranial Doppler (TCD) ultrasound, are high in patients with moyamoya. Middle cerebral artery velocity has excellent discernment of risk for future side-congruent strokes. Optimal cutoff velocity of the receiver operating characteristic curve analysis was 164 cm/s, which yielded a sensitivity of 0.80 and specificity of 0.87. If validated in future studies, TCD parameters may help to determine optimal imaging frequency, and incorporation of these parameters into precision-medicine risk stratification models may improve surgical decision-making.