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

Munson ME, Wrobel JS, Holmes CM, Hanauer DA - J Diabetes Res (2014)

Bottom Line: 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.CONCLUSION.Future work should focus on confirmatory analyses.

View Article: PubMed Central - PubMed

Affiliation: Division 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, USA.

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.

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Related in: MedlinePlus

Association diagram displaying the new or poorly known diagnoses association with Charcot foot listed in Table 2 (red nodes). The red node in the center of the square is Charcot foot. Note that all of the red nodes are associated with Charcot foot, as reported in Table 2, even if they are not connected to Charcot foot in the figure. The reason is that the thresholds used to build the diagram were lower than the P values of some of the associations. This was done to reduce the large number of other connections that would have appeared with the nodes displayed. Temporal relationships are not shown in this figure. This figure was made using the following criteria: association P value < 1.0 × 10−154; association odds ratio > 50; temporal P value not considered.
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fig2: Association diagram displaying the new or poorly known diagnoses association with Charcot foot listed in Table 2 (red nodes). The red node in the center of the square is Charcot foot. Note that all of the red nodes are associated with Charcot foot, as reported in Table 2, even if they are not connected to Charcot foot in the figure. The reason is that the thresholds used to build the diagram were lower than the P values of some of the associations. This was done to reduce the large number of other connections that would have appeared with the nodes displayed. Temporal relationships are not shown in this figure. This figure was made using the following criteria: association P value < 1.0 × 10−154; association odds ratio > 50; temporal P value not considered.

Mentions: Figure 2 displays an association diagram that includes the new or poorly known associations with Charcot foot that are listed in Table 2. The specific diagnoses from the table are shown as red nodes. Some of the diagnoses associated with the red nodes are highly interrelated, which can be seen by the various “clusters” and the large number of edges (lines) connecting each of the related nodes to one another. Other red nodes are less connected. These clusters suggest that there are potentially multiple etiological pathways that could lead to Charcot foot.


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

Munson ME, Wrobel JS, Holmes CM, Hanauer DA - J Diabetes Res (2014)

Association diagram displaying the new or poorly known diagnoses association with Charcot foot listed in Table 2 (red nodes). The red node in the center of the square is Charcot foot. Note that all of the red nodes are associated with Charcot foot, as reported in Table 2, even if they are not connected to Charcot foot in the figure. The reason is that the thresholds used to build the diagram were lower than the P values of some of the associations. This was done to reduce the large number of other connections that would have appeared with the nodes displayed. Temporal relationships are not shown in this figure. This figure was made using the following criteria: association P value < 1.0 × 10−154; association odds ratio > 50; temporal P value not considered.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4020407&req=5

fig2: Association diagram displaying the new or poorly known diagnoses association with Charcot foot listed in Table 2 (red nodes). The red node in the center of the square is Charcot foot. Note that all of the red nodes are associated with Charcot foot, as reported in Table 2, even if they are not connected to Charcot foot in the figure. The reason is that the thresholds used to build the diagram were lower than the P values of some of the associations. This was done to reduce the large number of other connections that would have appeared with the nodes displayed. Temporal relationships are not shown in this figure. This figure was made using the following criteria: association P value < 1.0 × 10−154; association odds ratio > 50; temporal P value not considered.
Mentions: Figure 2 displays an association diagram that includes the new or poorly known associations with Charcot foot that are listed in Table 2. The specific diagnoses from the table are shown as red nodes. Some of the diagnoses associated with the red nodes are highly interrelated, which can be seen by the various “clusters” and the large number of edges (lines) connecting each of the related nodes to one another. Other red nodes are less connected. These clusters suggest that there are potentially multiple etiological pathways that could lead to Charcot foot.

Bottom Line: 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.CONCLUSION.Future work should focus on confirmatory analyses.

View Article: PubMed Central - PubMed

Affiliation: Division 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, USA.

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.

Show MeSH
Related in: MedlinePlus