Abstract Body (Do not enter title and authors here):
Introduction Accurately identifying and characterizing patients with hypertrophic cardiomyopathy (HCM) is critical for population management and care optimization.

Research Question To develop natural language processing (NLP) algorithms to identify and characterize obstructive (oHCM) and non-obstructive (nHCM) HCM patients directly from echocardiograms, and to compare with the presence or absence of HCM-related diagnosis codes.

Methods We developed and validated NLP algorithms to identify HCM from all adult (age≥18yrs) echocardiograms performed from 2010-2019 in Kaiser Permanente Northern CA (KPNC), capturing measures of any HCM, HCM subtype, hypertrophy subtype, septal and posterior LV wall thickness, resting and stress/Valsalva LVOT gradients, and systolic anterior motion. We developed a rules-based algorithm (following AHA/ACC criteria) to classify patients as having HCM, including oHCM or nHCM subtypes, and possible HCM (defined as wall thickness ≥2cm without other criteria meeting an HCM definition). We evaluated the presence of HCM-related ICD-9/10 diagnosis codes among patients classified as HCM/non-HCM from echocardiograms using NLP, and linked baseline demographics and clinical parameters from our integrated electronic medical record.

Results Among 472,405 adults with echocardiograms, we identified 2,892 patients with HCM based upon NLP-derived measures (all NLP measures achieved >95% positive predictive value and >95% negative predictive value), including 1,585 (55%) with oHCM, 1,145 (40%) with nHCM, and 162 (6%) which could not be classified (Figure). Among those 2,892 patients, 1,283 did not have any associated HCM ICD-9/10 diagnosis codes (Table). Among 469,513 patients with no identified HCM from NLP-based algorithms, HCM ICD-9/10 diagnosis codes existed in 1,567 patients (Table). We also identified 4,593 patients with possible HCM by NLP, only 4.5% of whom had an associated HCM code. Among confirmed HCM patients by NLP, oHCM patients were slightly older (66 vs 61 yrs), more likely female (53% vs 43%), had similar mean septal wall thickness (1.7cm vs 1.7cm), but were more likely to have a septal hypertrophy subtype (46% vs 28%) compared to nHCM patients.

Conclusions Echocardiogram-based NLP methods can improve the identification of and care for HCM patients. Many patients with possible HCM may be underdiagnosed, representing an opportunity for quality improvement.