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Exploring clinical associations using '-omics' based enrichment analyses.

TitleExploring clinical associations using '-omics' based enrichment analyses.
Publication TypeJournal Article
Year of Publication2009
AuthorsHanauer, DA, Rhodes, DR, Chinnaiyan, AM
JournalPLoS One
Date Published2009
KeywordsDatabases, Factual, Decision Support Systems, Clinical, Gene Expression Profiling, Genetic Predisposition to Disease, Genomics, Humans, Medical Records Systems, Computerized, Odds Ratio, Reminder Systems

BACKGROUND: The vast amounts of clinical data collected in electronic health records (EHR) is analogous to the data explosion from the "-omics" revolution. In the EHR clinicians often maintain patient-specific problem summary lists which are used to provide a concise overview of significant medical diagnoses. We hypothesized that by tapping into the collective wisdom generated by hundreds of physicians entering problems into the EHR we could detect significant associations among diagnoses that are not described in the literature.METHODOLOGY/PRINCIPAL FINDINGS: We employed an analytic approach original developed for detecting associations between sets of gene expression data, called Molecular Concept Map (MCM), to find significant associations among the 1.5 million clinical problem summary list entries in 327,000 patients from our institution's EHR. An odds ratio (OR) and p-value was calculated for each association. A subset of the 750,000 associations found were explored using the MCM tool. Expected associations were confirmed and recently reported but poorly known associations were uncovered. Novel associations which may warrant further exploration were also found. Examples of expected associations included non-insulin dependent diabetes mellitus and various diagnoses such as retinopathy, hypertension, and coronary artery disease. A recently reported association included irritable bowel and vulvodynia (OR 2.9, p = 5.6x10(-4)). Associations that are currently unknown or very poorly known included those between granuloma annulare and osteoarthritis (OR 4.3, p = 1.1x10(-4)) and pyloric stenosis and ventricular septal defect (OR 12.1, p = 2.0x10(-3)).CONCLUSIONS/SIGNIFICANCE: Computer programs developed for analyses of "-omic" data can be successfully applied to the area of clinical medicine. The results of the analysis may be useful for hypothesis generation as well as supporting clinical care by reminding clinicians of likely problems associated with a patient's existing problems.

Alternate JournalPLoS ONE
PubMed ID19365550
PubMed Central IDPMC2664474
Grant List / / Howard Hughes Medical Institute / United States
David Hanauer
University of Michigan Rogel Cancer Center at North Campus Research Complex
1600 Huron Parkway, Bldg 100, Rm 1004 
Mailing Address: 2800 Plymouth Rd, NCRC 100-1004
Ann Arbor, MI 48109-2800 

Research reported in this publication was supported by the National Cancer Institutes of
Health under Award Number P30CA046592. The content is solely the responsibility
of the authors and does not necessarily represent the official views of the
National Institutes of Health.

Research reported in this publication was supported by the National Cancer Institutes of
Health under Award Number P30CA046592 by the use of the following Cancer Center
Shared Resource(s): Biostatistics, Analytics & Bioinformatics; Flow Cytometry;
Transgenic Animal Models; Tissue and Molecular Pathology; Structure & Drug
Screening; Cell & Tissue Imaging; Experimental Irradiation; Preclinical
Imaging & Computational Analysis; Health Communications; Immune Monitoring;

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