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

Hanauer DA, Rhodes DR, Chinnaiyan AM - PLoS ONE (2009)

Bottom Line: A subset of the 750,000 associations found were explored using the MCM tool.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Pediatrics, University of Michigan, Ann Arbor, Michigan, United States of America. hanauer@umich.edu

ABSTRACT

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.

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Related in: MedlinePlus

Examples of network graphs used to help identify unexpected associations and form hypotheses about the meaning of the associations.Figure 3A shows a network graph with selected associations for the diagnosis “vulvodynia” using a threshold for edges as odds ratio of 2.5 or more and p-value of 1.0×10−3 or less. “Fibromyalgia” and “irritable bowel” are associated with “vulvodynia” independently from the other inter-related gynecologic diagnoses. Figure 3B displays a network graph showing the associations between “shingles”, “hypothyroidism”, and other cancer-related diagnoses, using a threshold for edges as odds ratio of 1.75 or more and p value of 1.0×10−4 or less. Use of such a network helps to determine that the relationship between “shingles” and “hypothyroidism” may be due to cancer therapies. Node size represents the approximate number of diagnoses in the database. Node colors are designated according to the legend in Figure 1.
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pone-0005203-g003: Examples of network graphs used to help identify unexpected associations and form hypotheses about the meaning of the associations.Figure 3A shows a network graph with selected associations for the diagnosis “vulvodynia” using a threshold for edges as odds ratio of 2.5 or more and p-value of 1.0×10−3 or less. “Fibromyalgia” and “irritable bowel” are associated with “vulvodynia” independently from the other inter-related gynecologic diagnoses. Figure 3B displays a network graph showing the associations between “shingles”, “hypothyroidism”, and other cancer-related diagnoses, using a threshold for edges as odds ratio of 1.75 or more and p value of 1.0×10−4 or less. Use of such a network helps to determine that the relationship between “shingles” and “hypothyroidism” may be due to cancer therapies. Node size represents the approximate number of diagnoses in the database. Node colors are designated according to the legend in Figure 1.

Mentions: We used the MCM network graphs to identify unexpected associations and form hypotheses about why such associations might exist. Significant associations with the diagnosis of “vulvodynia” are shown in Figure 3A. While most of the associations in the network are related to gynecology, which would be expected, both “irritable bowel” (OR 2.9, p = 5.6×10−4), and “fibromyalgia” (OR 5.0, p = 2.5×10−5) are not. Two recent articles by Arnold et al reported associations between vulvodynia and both irritable bowel (ORs 1.86 and 3.11) and fibromyalgia (ORs 2.15 and 3.84 ).[9], [10] This compares reasonably well with our findings in MCM.


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

Hanauer DA, Rhodes DR, Chinnaiyan AM - PLoS ONE (2009)

Examples of network graphs used to help identify unexpected associations and form hypotheses about the meaning of the associations.Figure 3A shows a network graph with selected associations for the diagnosis “vulvodynia” using a threshold for edges as odds ratio of 2.5 or more and p-value of 1.0×10−3 or less. “Fibromyalgia” and “irritable bowel” are associated with “vulvodynia” independently from the other inter-related gynecologic diagnoses. Figure 3B displays a network graph showing the associations between “shingles”, “hypothyroidism”, and other cancer-related diagnoses, using a threshold for edges as odds ratio of 1.75 or more and p value of 1.0×10−4 or less. Use of such a network helps to determine that the relationship between “shingles” and “hypothyroidism” may be due to cancer therapies. Node size represents the approximate number of diagnoses in the database. Node colors are designated according to the legend in Figure 1.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2664474&req=5

pone-0005203-g003: Examples of network graphs used to help identify unexpected associations and form hypotheses about the meaning of the associations.Figure 3A shows a network graph with selected associations for the diagnosis “vulvodynia” using a threshold for edges as odds ratio of 2.5 or more and p-value of 1.0×10−3 or less. “Fibromyalgia” and “irritable bowel” are associated with “vulvodynia” independently from the other inter-related gynecologic diagnoses. Figure 3B displays a network graph showing the associations between “shingles”, “hypothyroidism”, and other cancer-related diagnoses, using a threshold for edges as odds ratio of 1.75 or more and p value of 1.0×10−4 or less. Use of such a network helps to determine that the relationship between “shingles” and “hypothyroidism” may be due to cancer therapies. Node size represents the approximate number of diagnoses in the database. Node colors are designated according to the legend in Figure 1.
Mentions: We used the MCM network graphs to identify unexpected associations and form hypotheses about why such associations might exist. Significant associations with the diagnosis of “vulvodynia” are shown in Figure 3A. While most of the associations in the network are related to gynecology, which would be expected, both “irritable bowel” (OR 2.9, p = 5.6×10−4), and “fibromyalgia” (OR 5.0, p = 2.5×10−5) are not. Two recent articles by Arnold et al reported associations between vulvodynia and both irritable bowel (ORs 1.86 and 3.11) and fibromyalgia (ORs 2.15 and 3.84 ).[9], [10] This compares reasonably well with our findings in MCM.

Bottom Line: A subset of the 750,000 associations found were explored using the MCM tool.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Pediatrics, University of Michigan, Ann Arbor, Michigan, United States of America. hanauer@umich.edu

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus