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

Author:

Lee Chiyoung1ORCID,Wei Sijia2,McConnell Eleanor S.34,Tsumura Hideyo5,Xue Tingzhong (Michelle)34ORCID,Pan Wei36

Affiliation:

1. School of Nursing & Health Studies, University of Washington Bothell, Bothell, WA, USA

2. Northwestern University Feinberg School of Medicine, Chicago, IL, USA

3. Duke University School of Nursing, Durham, NC, USA

4. Durham Veterans Affairs Health Care System, Durham, NC, USA

5. Duke University Health System, Durham, NC, USA

6. Duke University School of Medicine, Durham, NC, USA

Abstract

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 < .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 > 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.

Funder

Sigma Theta Tau International’s Psi-at-Large Chapter

Publisher

SAGE Publications

Subject

General Nursing

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