Format

Send to

Choose Destination
See comment in PubMed Commons below
J Biomed Inform. 2010 Dec;43(6):914-23. doi: 10.1016/j.jbi.2010.07.011. Epub 2010 Aug 3.

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

Author information

1
Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232-2156, USA. jonathan.schildcrout@vanderbilt.edu

Abstract

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.

PMID:
20688191
PMCID:
PMC2991387
DOI:
10.1016/j.jbi.2010.07.011
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science Icon for PubMed Central
    Loading ...
    Support Center