Abstract 125: Natural Language Processing Identifies an Association Between Canadian Cardiovascular Society Angina Severity and Mortality Within the Department of Veterans Affairs

Background: Stable angina is estimated to affect more than 10 million Americans and is the presenting symptom in half of patients diagnosed with coronary disease. Documentation of angina severity resides as unstructured data and is often unavailable in large datasets. We used natural language processing (NLP) to identify Canadian Cardiovascular Society (CCS) angina class and determine the association with all-cause mortality in an integrated health system’s electronic records (EHR). Methods: We performed a historic cohort study using national Veterans Health Administration data between 1/1/06 and 12/31/13. Veterans with incident stable angina were identified by ICD-9-CM codes. We developed an NLP tool to extract CCS class from free text notes. Risk ratios (RR) for all-cause mortality at one year associated with CCS class were calculated using Poisson regression. Results: There were 299,577 Veterans with angina, of which 14,216 had at least one CCS class extracted via NLP. Mean age was 66.6 years, 98% were male sex, and 82% were white. Diabetes increased with CCS class, but other comorbidities were stable (Table). There were 719 deaths at one year follow-up. The adjusted RR for all-cause mortality at one-year comparing Class III to Class I and Class IV to Class I was 1.40 (95% CI 1.16 - 1.68) and 1.52 (95% CI 1.13 - 2.04), respectively. Conclusion: NLP-derived CCS class was independently associated with one year all-cause mortality. Its application may be limited by inadequate EHR documentation of angina severity.