You are here

Data mining for identifying novel associations and temporal relationships with Charcot foot.

TitleData mining for identifying novel associations and temporal relationships with Charcot foot.
Publication TypeJournal Article
Year of Publication2014
AuthorsMunson, ME, Wrobel, JS, Holmes, CM, Hanauer, DA
JournalJ Diabetes Res
Volume2014
Pagination214353
Date Published2014
ISSN2314-6753
KeywordsAcademic Medical Centers, Ambulatory Care Facilities, Ankle, Arrhythmias, Cardiac, Arthropathy, Neurogenic, Cohort Studies, Data Mining, Delayed Diagnosis, Diabetes Mellitus, Type 2, Diabetic Foot, Diagnostic Errors, Electronic Health Records, Foot, Humans, Hypovolemia, International Classification of Diseases, Michigan, Models, Biological, Pulmonary Eosinophilia, Risk Factors
Abstract

INTRODUCTION. Charcot foot is a rare and devastating complication of diabetes. While some risk factors are known, debate continues regarding etiology. Elucidating other associated disorders and their temporal occurrence could lead to a better understanding of its pathogenesis. We applied a large data mining approach to Charcot foot for elucidating novel associations. METHODS. We conducted an association analysis using ICD-9 diagnosis codes for every patient in our health system (n = 1.6 million with 41.2 million time-stamped ICD-9 codes). For the current analysis, we focused on the 388 patients with Charcot foot (ICD-9 713.5). RESULTS. We found 710 associations, 676 (95.2%) of which had a P value for the association less than 1.0 × 10⁻⁵ and 603 (84.9%) of which had an odds ratio > 5.0. There were 111 (15.6%) associations with a significant temporal relationship (P < 1.0 × 10⁻³). The three novel associations with the strongest temporal component were cardiac dysrhythmia, pulmonary eosinophilia, and volume depletion disorder. CONCLUSION. We identified novel associations with Charcot foot in the context of pathogenesis models that include neurotrophic, neurovascular, and microtraumatic factors mediated through inflammatory cytokines. Future work should focus on confirmatory analyses. These novel areas of investigation could lead to prevention or earlier diagnosis.

DOI10.1155/2014/214353
Alternate JournalJ Diabetes Res
PubMed ID24868558
PubMed Central IDPMC4020407
People: 
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 
Ph. (734) 764-8848 Fax. (734) 615-0507

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;
Pharmacokinetics)

Copyright © Cancer Center Informatics-2011 Regents of the University of Michigan