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dc.contributor.authorSaczynski, Jane S.
dc.contributor.authorGurwitz, Jerry H.
dc.contributor.authorPadmanabhan, Sandhyasree
dc.contributor.authorGoldberg, Robert J.
dc.contributor.authorMagid, David J.
dc.contributor.authorSmith, David H.
dc.contributor.authorSung, Sue Hee
dc.contributor.authorGo, Alan S.
dc.date2022-08-11T08:08:12.000
dc.date.accessioned2022-08-23T15:46:10Z
dc.date.available2022-08-23T15:46:10Z
dc.date.issued2011-05-20
dc.date.submitted2011-09-01
dc.identifier.doi10.13028/a7h8-dd16
dc.identifier.urihttp://hdl.handle.net/20.500.14038/27693
dc.description.abstractBackground Heart failure (HF) carries a high burden of comorbidity with approximately one half of patients with HF having at least one additional comorbid condition present. Rates of comorbidity in patients with HF have steadily increased over the past 2 decades. Objective To examine patterns of comorbidity among older patients with HF in the Cardiovascular Research Network PRESERVE cohort. Methods PRESERVE Cohort Data are from the CVRN PRESERVE cohort which is a multicenter cohort of 37,054 patients [mean age = 74 years (SD = 12.4 yrs); 46% female] with HF diagnosed between 2005 and 2008 currently being conducted at 4 CVRN sites: KPNC, KPCO, KPNW, and FCHP. The primary data source for the PRESERVE cohort was the HMO Research Network Virtual Data Warehouse. Identification of Coexisting Diseases Coexisiting illnesses at the time of HF diagnosis were based on diagnoses and procedures mapped to relevant International Classification of Diseases, Ninth Edition (ICD-9) codes. For the purposes of characterizing clusters of comorbidities, we focused on coexisting conditions with a prevalence rate of ≥3%. Statistical Analysis We used the Agglomerative Clustering technique to characterize patterns of comorbidity. Over multiple iterations, each condition is clustered with the condition with which it has the highest squared correlation. This process is repeated to determine whether assigning a condition to a different cluster increases the amount of explained variance [ranging from 1.0 (all variance explained) to 0.0 (no variance explained)]. The conditions in each cluster are as correlated as possible among themselves and as uncorrelated as possible with conditions in other clusters. Results Burden of Comorbidity There was a high degree of comorbidity and multi-morbidity among patients with HF. (Table 1) Hypertension and arrhythmias were the comorbidities of HF that occurred most often in the absence of other chronic conditions (4.8% and 4.7%, respectively). The average number of comorbid conditions varied from 3.5 to 5.2. Patients with HF and unstable angina or other thromboembolic disorders had the highest multi-morbidity (mean = 5.2 conditions), whereas those with HF and hypertension had the lowest (mean = 3.5). Clustering of Comorbiditites A five-cluster structure was derived. Cluster 1: Dyslipidemia, Hypertension, Diabetes Mellitus, Visual Impairment Cluster 2: Acute Myocardial Infarction, Unstable Angina, Thromboembolic Disorder, Dementia Cluster 3: Aortic Valvular Disease, Cancer, Hearing Impairment, Arrthythmia Cluster 4: Peripheral Arterial Disease, Stroke Cluster 5: Lung Disease, Liver Disease, Depression Discussion and Conclusions Cluster analysis is an innovative approach to examining the co-occurrence of diseases and allows for identification of broad patterns of multi-morbidity beyond the pairings of diseases or disease counts. Patients with HF have a high rate of multi-morbidity, with an average of 4 co-occurring conditions. Intuitive and unintuitive patterns of clustering were identified. Randomized clinical trials in HF will need to include more diverse patient populations in order to adapt to the increasingly complex patient population. A cluster analysis approach to characterizing patterns of comorbidity may help indentify important patient subgroups.
dc.formatyoutube
dc.language.isoen_US
dc.rightsCopyright the Author(s)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/
dc.subjectCardiovascular Diseases
dc.subjectEpidemiology
dc.titlePatterns of Complex Comorbidity in Older Patients with Heart Failure
dc.typePoster
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=1023&context=cts_retreat&unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/cts_retreat/2011/posters/14
dc.identifier.contextkey2211850
refterms.dateFOA2022-08-23T15:46:10Z
html.description.abstract<p><strong>Background</strong></p> <p>Heart failure (HF) carries a high burden of comorbidity with approximately one half of patients with HF having at least one additional comorbid condition present. Rates of comorbidity in patients with HF have steadily increased over the past 2 decades.</p> <p><strong>Objective</strong></p> <p>To examine patterns of comorbidity among older patients with HF in the Cardiovascular Research Network PRESERVE cohort.</p> <p><strong>Methods</strong></p> <p><em>PRESERVE Cohort </em></p> <p>Data are from the CVRN PRESERVE cohort which is a multicenter cohort of 37,054 patients [mean age = 74 years (SD = 12.4 yrs); 46% female] with HF diagnosed between 2005 and 2008 currently being conducted at 4 CVRN sites: KPNC, KPCO, KPNW, and FCHP. The primary data source for the PRESERVE cohort was the HMO Research Network Virtual Data Warehouse.</p> <p><em>Identification of Coexisting Diseases </em></p> <p>Coexisiting illnesses at the time of HF diagnosis were based on diagnoses and procedures mapped to relevant International Classification of Diseases, Ninth Edition (ICD-9) codes. For the purposes of characterizing clusters of comorbidities, we focused on coexisting conditions with a prevalence rate of ≥3%.</p> <p><strong>Statistical Analysis</strong></p> <p>We used the Agglomerative Clustering technique to characterize patterns of comorbidity. Over multiple iterations, each condition is clustered with the condition with which it has the highest squared correlation. This process is repeated to determine whether assigning a condition to a different cluster increases the amount of explained variance [ranging from 1.0 (all variance explained) to 0.0 (no variance explained)]. The conditions in each cluster are as correlated as possible among themselves and as uncorrelated as possible with conditions in other clusters.</p> <p><strong>Results</strong></p> <p><em>Burden of Comorbidity </em></p> <p>There was a high degree of comorbidity and multi-morbidity among patients with HF. (Table 1) Hypertension and arrhythmias were the comorbidities of HF that occurred most often in the absence of other chronic conditions (4.8% and 4.7%, respectively). The average number of comorbid conditions varied from 3.5 to 5.2. Patients with HF and unstable angina or other thromboembolic disorders had the highest multi-morbidity (mean = 5.2 conditions), whereas those with HF and hypertension had the lowest (mean = 3.5).</p> <p><em>Clustering of Comorbiditites </em></p> <p>A five-cluster structure was derived. <ul> <li><strong>Cluster 1:</strong> Dyslipidemia, Hypertension, Diabetes Mellitus, Visual Impairment</li> </ul> <ul> <li><strong>Cluster 2:</strong> Acute Myocardial Infarction, Unstable Angina, Thromboembolic Disorder, Dementia</li> </ul> <ul> <li><strong>Cluster 3: </strong>Aortic Valvular Disease, Cancer, Hearing Impairment, Arrthythmia</li> </ul> <ul> <li><strong>Cluster 4: </strong>Peripheral Arterial Disease, Stroke</li> </ul> <ul> <li><strong>Cluster 5: </strong>Lung Disease, Liver Disease, Depression</li> </ul></p> <p><strong>Discussion and Conclusions</strong> <ul> <li>Cluster analysis is an innovative approach to examining the co-occurrence of diseases and allows for identification of broad patterns of multi-morbidity beyond the pairings of diseases or disease counts.</li> </ul> <ul> <li>Patients with HF have a high rate of multi-morbidity, with an average of 4 co-occurring conditions. Intuitive and unintuitive patterns of clustering were identified.</li> </ul> <ul> <li>Randomized clinical trials in HF will need to include more diverse patient populations in order to adapt to the increasingly complex patient population.</li> </ul> <ul> <li>A cluster analysis approach to characterizing patterns of comorbidity may help indentify important patient subgroups.</li> </ul></p>
dc.identifier.submissionpathcts_retreat/2011/posters/14


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