Abstract WP170: Validation Of Magnetically Induced Physiological Response Assessment To Identify Patients Likely Harboring Silent Brain Infarcts

Abstract

Background: Silent brain infarcts (SBIs) are among the most potent risk factors for future symptomatic stroke and vascular cognitive impairment, increasing the hazard ratio up to 3.3-fold. But high costs make MRI screening for SBIs in asymptomatic patients cost-ineffective. The AHA/ASA 2017 Scientific Statement on silent cerebrovascular disease calls for research to develop techniques to identify people at higher risk of SBIs who could go on to targeted neuroimaging and intensive prevention intervention. We performed a derivation and validation study of such a tool, a magnetically induced physiological response assessment of brain network integrity. Methods: Multicenter international study conducted at 5 sites (3 US, 2 OUS), assessing direct electrophysiological imaging (DELPHI) technology that records brain network magnetically induced physiological response. One site recruited the derivation cohort and 4 the validation cohort. Healthy subjects ages 50-75 with at least one vascular RF underwent multiple evaluations including computerized cognitive testing, MR assessment of SBIs, gray matter volume, white matter integrity, and brain network physiological function using DELPHI measures of connectivity. MRI findings, including SBIs, were assessed by 3 blinded independent neuroradiologists. Results: The derivation cohort enrolled 220 subjects and validation cohort 264 subjects. Across both groups, age was 61.5 (±7.1), 57% were female, and leading vascular risk factors were obesity/overweight in 67%, hypertension in 51%, and dyslipidemia in 45%. Silent brain infarcts were present in 10.8% of individuals, among whom 92% had 1-4 SBIs and 8% had ≥ 5 SBIs. The validation cohort was tested using the DELPHI thresholds established in the derivation cohort. In the validation cohort, DELPHI assessment discriminated SBI positive patients with AUC=0.70, sensitivity 82%, specificity 65%, p=0.001. Conclusion: Brain network physiology evaluation using the DELPHI technology detects silent brain infarcts with substantial accuracy in the outpatient office setting. This system is a safe and reliable means to identify older individuals likely harboring silent brain infarcts who could go on to targeted neuroimaging and intensified prevention treatment.