A Big-Data Neuro-Informatics Infrastructure to support Research Pipelines for Cerebrovascular Disease

Stroke(2023)

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摘要
Background: Cerebrovascular disease has significant gaps in translational and outcomes research. We provide the framework for a neuro-informatics pipeline, “Registry for Neurological Endpoints Assessment Among Patients with Ischemic and Hemorrhagic Stroke (REINAH)”, aimed at harnessing ‘big data’ across a 7-hospital certified stroke healthcare system. Methods: An automated extract, transform, load (ETL) process imports all clinical inpatient and outpatient data for patients with acute ischemic stroke (AIS), intracerebral hemorrhage (ICH), transient ischemic attack (TIA), subarachnoid hemorrhage (SAH), cerebral amyloid angiopathy (CAA) and matched controls. Following validation, a 2 nd stage ETL merges geocoding data for area deprivation index and generates domain specific dashboards. Neuroimaging and physiological wave form data are imported and analyzed for infarct / hemorrhage characteristics, cerebral small vessel disease burden, and hemodynamic variability. Cross linkages between REINAH and Medicare claims data have been established with > 98% overlap. Functional, cognitive, and quality of life outcomes are collected up till 365-days after discharge. Results: As of August 5, 2022, REINAH hosts data on 18,746 patients, including primary encounters for 1,863 ICH, 13,964 AIS, 1,264 SAH, and 3,307 TIA patients. Median (IQR) age for patients is 69 [58-79] years, including 51.7% Female, 15.6% Hispanic, 64.4% White, 24.6% Black, 5.4% Asian. Median Glasgow Coma Scale scores for AIS and ICH are 15 [14-15] and 13.5 [7-15], respectively. The successful contact rate for outcomes assessment is 70.9%, with 69.8% of responders providing consent for follow up. REINAH layout and demographics are shown in Figure. Conclusions: Mature data pipelines are needed to support validated and rapid evidence synthesis. REINAH provides a highly curated, clinically focused platform for stroke research that may impact patient care and post-stroke outcomes.
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关键词
Electronic Medical Record,Big Data,Patient Outcomes,Epidemiology,Learning Health System,Observational Research
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