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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

Overall network diagram containing 1106 nodes and 1939 edges showing the most significant problem category associations using an odds ratio>100.0 and p-value<1.0×10−10 as thresholds for inclusion.Nodes are roughly proportional to the number of times each problem appears in the problem summary list (PSL) and only nodes with more than 100 occurrences are shown. Problems are color-coded based on the general area in medicine in which the problem would likely be diagnosed or followed. At this level several clusters of related problems can be seen, some of which are labeled above.
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pone-0005203-g001: Overall network diagram containing 1106 nodes and 1939 edges showing the most significant problem category associations using an odds ratio>100.0 and p-value<1.0×10−10 as thresholds for inclusion.Nodes are roughly proportional to the number of times each problem appears in the problem summary list (PSL) and only nodes with more than 100 occurrences are shown. Problems are color-coded based on the general area in medicine in which the problem would likely be diagnosed or followed. At this level several clusters of related problems can be seen, some of which are labeled above.

Mentions: We explored numerous associations among diagnoses in our electronic medical record using the Molecular Concept Maps (MCM) web application. The analysis uncovered 753,574 associations among the problems, of which 483,802 associations had an odds ratio greater than 3.0 and a p-value less than 1.0×10−3. These associations represented just 0.2% of the possible pairs based on the original list of 20,705 problems. A network graph with the strongest associations is shown in Figure 1. Clusters of diagnoses within similar medical categories can be seen in this high-level view.


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

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

Overall network diagram containing 1106 nodes and 1939 edges showing the most significant problem category associations using an odds ratio>100.0 and p-value<1.0×10−10 as thresholds for inclusion.Nodes are roughly proportional to the number of times each problem appears in the problem summary list (PSL) and only nodes with more than 100 occurrences are shown. Problems are color-coded based on the general area in medicine in which the problem would likely be diagnosed or followed. At this level several clusters of related problems can be seen, some of which are labeled above.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0005203-g001: Overall network diagram containing 1106 nodes and 1939 edges showing the most significant problem category associations using an odds ratio>100.0 and p-value<1.0×10−10 as thresholds for inclusion.Nodes are roughly proportional to the number of times each problem appears in the problem summary list (PSL) and only nodes with more than 100 occurrences are shown. Problems are color-coded based on the general area in medicine in which the problem would likely be diagnosed or followed. At this level several clusters of related problems can be seen, some of which are labeled above.
Mentions: We explored numerous associations among diagnoses in our electronic medical record using the Molecular Concept Maps (MCM) web application. The analysis uncovered 753,574 associations among the problems, of which 483,802 associations had an odds ratio greater than 3.0 and a p-value less than 1.0×10−3. These associations represented just 0.2% of the possible pairs based on the original list of 20,705 problems. A network graph with the strongest associations is shown in Figure 1. Clusters of diagnoses within similar medical categories can be seen in this high-level view.

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