Natural Language Processing for Adjudication of Heart Failure in a Multicenter Clinical Trial

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Abstract
Heart failure (HF) is the leading cause of hospitalization in the US.1 Identification and classification of HF events is challenging due to clinical and phenotypic heterogeneity as well as the absence of definitive and scalable diagnostic testing. Yet the validity of HF randomized clinical trials, epidemiology, and outcomes research depends on accurate identification of HF hospitalization events. The time-intensive gold-standard for defining HF outcomes in most randomized clinical trials is for local sites to identify potential events and submit medical records to a central clinical events committee (CEC), a group of expert physicians who manually review the records and determine whether the case meets established criteria.2 For larger (primarily observational) studies for which CEC adjudication is impractical, International Classification of Disease (ICD) codes are commonly used to define HF hospitalizations.3,4 But ICD codes for HF are known to be imprecise: approximately 40% of hospitalizations with primary position ICD codes for HF do not meet established criteria on medical record review.5-8 Automated adjudication of HF hospitalizations from medical records at scale could streamline or reduce the need for physician CEC review and therefore may represent 1 step toward larger and less expensive clinical trials and more precise clinical outcome data for epidemiology research.

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