• H Nina Kim, Ayushi Gupta, Kristine Lan, Jenell Stewart, Shireesha Dhanireddy, and Maria A Corcorran.
• Department of Medicine, University of Washington, Seattle, Washington.
• Medicine (Baltimore). 2021 Dec 23; 100 (51): e28354e28354.

AbstractStudies of infective endocarditis (IE) have relied on International Classification of Disease (ICD) codes to identify cases, a method vulnerable to misclassification. Clinical narrative data could offer greater accuracy and richness to cohort identification. We evaluated two algorithms: 1. a standard query of ICD-9/10 billing codes, with or without procedure codes for echocardiogram and 2. a text query of discharge summaries (DS) that selected on the term “endocarditis” in fields headed by “Discharge Diagnosis” or “Admission Diagnosis” or similar. Further coding extracted valve involved and organism responsible if present. All cases were chart reviewed using pre-specified criteria. Positive predictive value (PPV), sensitivity and specificity were calculated. The ICD-based query identified 612 individuals from July 2015 to July 2019 who had a hospital billing code for infective endocarditis; of these, 534 had an echocardiogram. The DS query identified 387 cases. PPV for the DS query was 84.5% (95% CI 80.6%, 87.8%) compared with 72.4% (95% CI 68.7%, 75.8%) for ICD only (P < .001) and 75.8% (95% CI 72.0%, 79.3%) for ICD + echo queries (P = .002). Sensitivity was 75.9% for DS query and 86.8% to 93.4% for ICD queries (P < .02 for these comparisons). Specificity was high for all queries >94%. The DS query also yielded valve data (prosthetic, tricuspid, aortic, etc) in 60% and microbiologic agent in 73% of identified cases with an accuracy of 94% and 90%, respectively when assessed by chart review. Compared with ICD-based queries, text-based queries of discharge summaries have the potential to improve precision of IE case ascertainment and extract key clinical variables.

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