1449-P: Identification of Type 1 Diabetes by an Electronic Health Records Algorithm from the CURE-CKD Registry

Introduction 40 from “very likely” category, 40 from “likely” category, and 20 from “unlikely”. Results: T1D cohorts (N=31,695) were divided into likelihood categories based on confirming criteria (Figure). The cohort was 48.5% female and 75.7% White race identity. Their mean±SD for age was 42±19 years with HbA1c 8.2±2.1% and BMI 27.5±6.3 kg/m2. Positive predictive values were 100% for very likely, 98% for likely, and 40% for unlikely categories. Conclusions: T1D can be reliably identified by the CURE-CKD algorithm using clinical EHR data. Pending further validation, this algorithm can be used for T1D cohort selection in real-world studies of epidemiology, diagnosis, and treatment. Disclosure C. Greenbaum: Other Relationship; Sanofi, Takeda Pharmaceutical Company Limited, Bristol-Myers Squibb Company, Imcyse. C.L. Reynolds: None. C. Jones: None. L. Kornowske: None. K.B. Daratha: None. R.Z. Alicic: Advisory Panel; Bayer Inc., Boehringer-Ingelheim. Research Support; AstraZeneca, Bayer Inc., AstraZeneca, Novo Nordisk, Traveere Pharmaceuticals. J.J. Neumiller: Advisory Panel; Bayer Inc., Boehringer-Ingelheim, Eli Lilly and Company, Proteomics International. K.R. Tuttle: Consultant; Lilly Diabetes. Research Support; Boehringer-Ingelheim. Consultant; AstraZeneca. Research Support; Novo Nordisk, Bayer Inc., Traveere Pharmaceuticals. Consultant; Pfizer Inc. Funding CDC (75D301-21-P-12254)