Comorbidity Patterns in Older Patients Undergoing Hip Fracture Surgery: A Comorbidity Network Analysis Study

Comorbidity network analysis (CNA) is a technique in which mathematical graphs encode correlations (edges) among diseases (nodes) inferred from the disease co-occurrence data of a patient group. The present study applied this network-based approach to identifying comorbidity patterns in older patien...

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Veröffentlicht in:Clinical nursing research 2024-01, Vol.33 (1), p.70-80
Hauptverfasser: Lee, Chiyoung, Wei, Sijia, McConnell, Eleanor S., Tsumura, Hideyo, Xue, Tingzhong (Michelle), Pan, Wei
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container_title Clinical nursing research
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creator Lee, Chiyoung
Wei, Sijia
McConnell, Eleanor S.
Tsumura, Hideyo
Xue, Tingzhong (Michelle)
Pan, Wei
description Comorbidity network analysis (CNA) is a technique in which mathematical graphs encode correlations (edges) among diseases (nodes) inferred from the disease co-occurrence data of a patient group. The present study applied this network-based approach to identifying comorbidity patterns in older patients undergoing hip fracture surgery. This was a retrospective observational cohort study using electronic health records (EHR). EHR data were extracted from the one University Health System in the southeast United States. The cohort included patients aged 65 and above who had a first-time low-energy traumatic hip fracture treated surgically between October 1, 2015 and December 31, 2018 (n = 1,171). Comorbidity includes 17 diagnoses classified by the Charlson Comorbidity Index. The CNA investigated the comorbid associations among 17 diagnoses. The association strength was quantified using the observed-to-expected ratio (OER). Several network centrality measures were used to examine the importance of nodes, namely degree, strength, closeness, and betweenness centrality. A cluster detection algorithm was employed to determine specific clusters of comorbidities. Twelve diseases were significantly interconnected in the network (OER > 1, p-value  2.5). Cerebrovascular disease, congestive heart failure, and myocardial infarction were identified as the central diseases that co-occurred with numerous other diseases. Two distinct clusters were noted, and the largest cluster comprised 10 diseases, primarily encompassing cardiometabolic and cognitive disorders. The results highlight specific patient comorbidities that could be used to guide clinical assessment, management, and targeted interventions that improve hip fracture outcomes in this patient group.
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The present study applied this network-based approach to identifying comorbidity patterns in older patients undergoing hip fracture surgery. This was a retrospective observational cohort study using electronic health records (EHR). EHR data were extracted from the one University Health System in the southeast United States. The cohort included patients aged 65 and above who had a first-time low-energy traumatic hip fracture treated surgically between October 1, 2015 and December 31, 2018 (n = 1,171). Comorbidity includes 17 diagnoses classified by the Charlson Comorbidity Index. The CNA investigated the comorbid associations among 17 diagnoses. The association strength was quantified using the observed-to-expected ratio (OER). Several network centrality measures were used to examine the importance of nodes, namely degree, strength, closeness, and betweenness centrality. A cluster detection algorithm was employed to determine specific clusters of comorbidities. Twelve diseases were significantly interconnected in the network (OER &gt; 1, p-value &lt; .05). The most robust associations were between metastatic carcinoma and mild liver disease, myocardial infarction and congestive heart failure, and hemi/paraplegia and cerebrovascular disease (OER &gt; 2.5). Cerebrovascular disease, congestive heart failure, and myocardial infarction were identified as the central diseases that co-occurred with numerous other diseases. Two distinct clusters were noted, and the largest cluster comprised 10 diseases, primarily encompassing cardiometabolic and cognitive disorders. 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Twelve diseases were significantly interconnected in the network (OER &gt; 1, p-value &lt; .05). The most robust associations were between metastatic carcinoma and mild liver disease, myocardial infarction and congestive heart failure, and hemi/paraplegia and cerebrovascular disease (OER &gt; 2.5). Cerebrovascular disease, congestive heart failure, and myocardial infarction were identified as the central diseases that co-occurred with numerous other diseases. Two distinct clusters were noted, and the largest cluster comprised 10 diseases, primarily encompassing cardiometabolic and cognitive disorders. The results highlight specific patient comorbidities that could be used to guide clinical assessment, management, and targeted interventions that improve hip fracture outcomes in this patient group.</abstract><cop>Los Angeles, CA</cop><pub>SAGE Publications</pub><pmid>37932937</pmid><doi>10.1177/10547738231209367</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-9491-6519</orcidid><orcidid>https://orcid.org/0000-0001-6860-452X</orcidid></addata></record>
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subjects Aged
Associations
Cancer
Cerebrovascular disease
Cerebrovascular Disorders
Clinical assessment
Closeness
Cognitive impairment
Cohort analysis
Cohort Studies
Comorbidity
Computerized medical records
Electronic health records
Fractured hips
Fractures
Graphs
Health records
Heart attacks
Heart Failure
Hip Fractures - epidemiology
Hip Fractures - surgery
Hip joint
Humans
Liver diseases
Myocardial Infarction
Network analysis
Network centrality
Older people
Paraplegics
Retrospective Studies
Risk Factors
Surgery
United States
title Comorbidity Patterns in Older Patients Undergoing Hip Fracture Surgery: A Comorbidity Network Analysis Study
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