Data Mining for Identifying Novel Associations and Temporal Relationships with Charcot Foot
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 dat...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Diabetes Research |
| Online Access: | http://dx.doi.org/10.1155/2014/214353 |
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| author | Michael E. Munson James S. Wrobel Crystal M. Holmes David A. Hanauer |
| author_facet | Michael E. Munson James S. Wrobel Crystal M. Holmes David A. Hanauer |
| author_sort | Michael E. Munson |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3fc3d1f771e64708be84094c8a9bdaf4 |
| institution | Kabale University |
| issn | 2314-6745 2314-6753 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Research |
| spelling | doaj-art-3fc3d1f771e64708be84094c8a9bdaf42025-02-03T01:09:30ZengWileyJournal of Diabetes Research2314-67452314-67532014-01-01201410.1155/2014/214353214353Data Mining for Identifying Novel Associations and Temporal Relationships with Charcot FootMichael E. Munson0James S. Wrobel1Crystal M. Holmes2David A. Hanauer3Division of Metabolism, Endocrinology and Diabetes, Department of Internal Medicine, University of Michigan Medical School, 24 Frank Lloyd Wright Drive, Lobby C, Suite 1300, Ann Arbor, MI 48106, USADivision of Metabolism, Endocrinology and Diabetes, Department of Internal Medicine, University of Michigan Medical School, 24 Frank Lloyd Wright Drive, Lobby C, Suite 1300, Ann Arbor, MI 48106, USADivision of Metabolism, Endocrinology and Diabetes, Department of Internal Medicine, University of Michigan Medical School, 24 Frank Lloyd Wright Drive, Lobby C, Suite 1300, Ann Arbor, MI 48106, USADepartment of Pediatrics, University of Michigan Medical School, 5312 CC, SPC 5940, 1500 E. Medical Center Drive, Ann Arbor, MI 48109, USAIntroduction. 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.http://dx.doi.org/10.1155/2014/214353 |
| spellingShingle | Michael E. Munson James S. Wrobel Crystal M. Holmes David A. Hanauer Data Mining for Identifying Novel Associations and Temporal Relationships with Charcot Foot Journal of Diabetes Research |
| title | Data Mining for Identifying Novel Associations and Temporal Relationships with Charcot Foot |
| title_full | Data Mining for Identifying Novel Associations and Temporal Relationships with Charcot Foot |
| title_fullStr | Data Mining for Identifying Novel Associations and Temporal Relationships with Charcot Foot |
| title_full_unstemmed | Data Mining for Identifying Novel Associations and Temporal Relationships with Charcot Foot |
| title_short | Data Mining for Identifying Novel Associations and Temporal Relationships with Charcot Foot |
| title_sort | data mining for identifying novel associations and temporal relationships with charcot foot |
| url | http://dx.doi.org/10.1155/2014/214353 |
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