Knowledgebase strategies to aid interpretation of clinical correlation research

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 30; no. 7; pp. 1257 - 1265
• Main Authors: Stead, William W, Lewis, Adam, Giuse, Nunzia B, Koonce, Taneya Y, Bastarache, Lisa
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 20.06.2023
• Subjects: Blood Pressure; Cohort Studies; Genome-Wide Association Study; Humans; Hypertension; Phenotype; Polymorphism, Single Nucleotide; Research and Applications; PheWAS; electronic health records; knowledgebase; phecode; hypertension
• ISSN: 1067-5027; 1527-974X
• DOI: 10.1093/jamia/ocad078

Abstract Objective Knowledgebases are needed to clarify correlations observed in real-world electronic health record (EHR) data. We posit design principles, present a unifying framework, and report a test of concept. Materials and Methods We structured a knowledge framework along 3 axes: condition of interest, knowledge source, and taxonomy. In our test of concept, we used hypertension as our condition of interest, literature and VanderbiltDDx knowledgebase as sources, and phecodes as our taxonomy. In a cohort of 832 566 deidentified EHRs, we modeled blood pressure and heart rate by sex and age, classified individuals by hypertensive status, and ran a Phenome-wide Association Study (PheWAS) for hypertension. We compared the correlations from PheWAS to the associations in our knowledgebase. Results We produced PhecodeKbHtn: a knowledgebase comprising 167 hypertension-associated diseases, 15 of which were also negatively associated with blood pressure (pos+neg). Our hypertension PheWAS included 1914 phecodes, 129 of which were in the PhecodeKbHtn. Among the PheWAS association results, phecodes that were in PhecodeKbHtn had larger effect sizes compared with those phecodes not in the knowledgebase. Discussion Each source contributed unique and additive associations. Models of blood pressure and heart rate by age and sex were consistent with prior cohort studies. All but 4 PheWAS positive and negative correlations for phecodes in PhecodeKbHtn may be explained by knowledgebase associations, hypertensive cardiac complications, or causes of hypertension independently associated with hypotension. Conclusion It is feasible to assemble a knowledgebase that is compatible with EHR data to aid interpretation of clinical correlation research.