Abstract 242: Assessing Variation In Data Captured By Clinical Registries And The Electronic Health Record: Clinical Phenotypes Of Patients Undergoing Cardiac Catheterization

Abstract

Background: Cardiovascular registries are built through expert abstraction of summary clinical information often represented as binary flags used to define diagnosis status. We considered underlying variation in clinical data obtained from the electronic health record (EHR) with respect to binary flags denoting presence or absence of cardiometabolic disease in a clinical data registry. Methods: We linked data abstracted for percutaneous coronary intervention (PCI) visits registered in NCDR CathPCI® with EHR data from Yale New Haven Hospital. We identified registry flags indicating diagnoses of hyperlipidemia (HLD), hypertension (HTN) and diabetes mellitus (DM) and evaluated EHR data with respect to registry diagnosis status. Results: Among 3,473 patients, mean age was 67 ± 12 years and 960 (28%) patients were female. HLD, HTN and DM registry diagnoses were present (+) in 2694 (78%), 2833 (82%) and 1358 (39%) patients, and absent (-) in 779 (22%), 640 (18%) and 2115 (61%) patients. EHR data revealed mean baseline total cholesterol of 168 ± 51 mg/dL (+HLD) vs 182 ± 44 mg/dL (-HLD), presenting systolic blood pressure of 130 ± 19 mmHg (+HTN) vs 120 ± 17 mmHg (-HTN), and A1c of 7.9% ± 1.8% (+DM) vs 5.8% ± 0.7% (-DM). Prescription histories of patients with HLD, HTN or DM diagnoses included 1 or more relevant drug class in 2409 (89%), 2620 (92%) and 1222 (90%) patients, and 2 or more drug classes in 450 (17%), 2020 (71%) and 820 (60%) patients. Patients without these diagnoses had been prescribed 1 or more drug class in 268 (34%), 223 (35%) and 55 (3%) patients, and 2 or more drug classes in 28 (4%), 71 (11%) and 9 (0.4%) patients (Figure 1). Conclusions: Binary diagnosis flags used in cardiovascular registries do not capture the full variation of clinical phenotypes underlying cardiometabolic disease processes. Harnessing comprehensive EHR data may provide a fuller picture of cardiometabolic disease, enhance quality and utility of national registries and improve outcomes research.