Abstract T P191: Using Clinical Trial Data to Generate Causative Classification System (CCS) Ischemic Stroke Phenotypes for the NINDS Stroke Genetics Network (SiGN)

Abstract

Background: Recruitment and phenotyping constitute major costs in stroke genetics research. Clinical trial datasets that include biological samples provide an opportunity to leverage richly phenotyped cases for genomic research. However, the heterogeneity of stroke and methodological differences across studies present challenges. We developed and validated a systematic method of mapping data abstracted from a multicenter randomized clinical trial to the inputs of the CCS stroke subtyping system for the SiGN study. Methods: The SiGN Phenotype Committee and the Secondary Prevention of Small Subcortical Strokes (SPS3) research team systematically reviewed SPS3 case report forms (CRF) to identify key elements required to generate CCS subtyping, and drafted and refined mapping rules using a derivation set of 30 charts. The resultant algorithm was compared to manual entry of clinical information into the CCS in a test set of 30 randomly selected charts. This identified problems due to multiple versions of CRFs (up to 7 versions) and prompted revision of the mapping rules. We assessed the revised algorithm in an independent test set. Results: None of the subtype classifications agreed in the initial testing prompting revision to allow capture information from different versions of the CRFs. The revised mapping algorithm applied to the same test set performed well; 29/30 agreed - 20 small artery occlusion (SAO) evident, 4 SAO possible, 2 SAO probable and 3 supra-aortic large artery atherosclerosis evident). The one chart that disagreed was classified as SAO evident by manual entry but as undetermined by the data mapping rules. In the 2nd independent test set, 30/30 agreed: 22 SAO evident, 4 SAO possible, 3 supra-aortic large artery atherosclerosis evident and 1 undetermined unknown - incomplete evaluation. Conclusions: We created mapping rules using CRFs from a clinical trial to allow reliable subtype classification using the CCS. Issues related to multiple iterations of CRFs presented challenges. Highly phenotyped stroke cases from clinical trials represent a cost-effective opportunity for genomic research.