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Use of Natural Language Processing to Objectively Identify Hepatic Encephalopathy in Multiple Cohorts
Scott Silvey1, Nilang Patel2, Brian C Davis2
1Department of Population Health, Virginia Commonwealth University, Richmond, Virginia, USA.
Introduction:
Despite being a major clinical, financial, and psychosocial burden, hepatic encephalopathy (HE) is not specifically prioritized for liver transplant. Moreover, subjectivity in HE definition can hinder outcome analysis for multicenter trials. We used natural language processing (NLP) to determine terms associated with an inpatient HE episode across derivation and 2 validation cohorts.
Methods:
NLP terms related to HE were selected using guideline review and physician evaluation. Derivation cohort: Charts from cirrhosis inpatients at Richmond Veterans Affairs (VA) Medical Center with MELD 3.0 ≥ 15 underwent NLP. Terms with highest sensitivity/negative predictive value (NPV) were then validated in 2 independent cohorts from North American Consortium of Study of End-stage Liver Disease (cohorts in Richmond VA and Virginia Commonwealth University). Individual contribution of terms toward HE diagnosis was studied with machine-learning cross-validation.
Results:
Derivation cohort consisted of four hundred thirty-two Veterans (4,048 notes, 34% HE diagnosis) showed 5 variables associated with HE (asterixis, altered mental status, confusion, initiation/continuation of lactulose/rifaximin) with 85.8%-sensitivity/82.1%-NPV. One hundred sixty-four patients (1,536 notes, 26% HE diagnosis) were included for internal validation, where the sensitivity/NPV of these 5 terms was 100% each. Overall, 145 patients (6,547 notes, 35% HE) were included with 94.1% sensitivity and 84.2%-NPV in an external validation cohort. On cross-validated machine learning, the top 5 features in both validation cohorts were similar to the derivation cohort. Contribution of the combination of these terms was superior to individual terms in defining HE.
Discussion:
Five signs, symptoms, and treatment decisions with simple phrases extracted from inpatient charts using NLP define HE across 3 cohorts. This objectivity could aid future policy on liver transplant priority and HE-event adjudication in clinical trials.
