An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records.

Schildcrout JS, Basford MA, Pulley JM, Masys DR, Roden DM, Wang D, Chute CG, Kullo IJ, Carrell D, Peissig P, Kho A, Denny JC
J Biomed Inform. 2010 43 (6): 914-23

PMID: 20688191 · PMCID: PMC2991387 · DOI:10.1016/j.jbi.2010.07.011

We describe a two-stage analytical approach for characterizing morbidity profile dissimilarity among patient cohorts using electronic medical records. We capture morbidities using the International Statistical Classification of Diseases and Related Health Problems (ICD-9) codes. In the first stage of the approach separate logistic regression analyses for ICD-9 sections (e.g., "hypertensive disease" or "appendicitis") are conducted, and the odds ratios that describe adjusted differences in prevalence between two cohorts are displayed graphically. In the second stage, the results from ICD-9 section analyses are combined into a general morbidity dissimilarity index (MDI). For illustration, we examine nine cohorts of patients representing six phenotypes (or controls) derived from five institutions, each a participant in the electronic MEdical REcords and GEnomics (eMERGE) network. The phenotypes studied include type II diabetes and type II diabetes controls, peripheral arterial disease and peripheral arterial disease controls, normal cardiac conduction as measured by electrocardiography, and senile cataracts.

Copyright © 2010 Elsevier Inc. All rights reserved.

MeSH Terms (10)

Cohort Studies Diabetes Mellitus, Type 2 Electronic Health Records Humans International Classification of Diseases Morbidity Peripheral Arterial Disease Phenotype Prevalence United States

Connections (3)

This publication is referenced by other Labnodes entities: