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Comorbidity Patterns in Older Patients Undergoing Hip Fracture Surgery: A Comorbidity Network Analysis Study
Chiyoung Lee1, Sijia Wei2, Eleanor S McConnell3,4
1School of Nursing & Health Studies, University of Washington Bothell, Bothell, WA, USA.
Insights
Comorbidity network analysis revealed significant disease interconnections in older hip fracture patients. Key comorbidities like heart failure and cerebrovascular disease were central, guiding better clinical management for improved outcomes.
Area of Science:
- Geriatric Medicine
- Network Science
- Health Informatics
Background:
- Hip fractures are a significant health issue in older adults, often associated with multiple comorbidities.
- Understanding comorbidity patterns is crucial for effective patient management and improving surgical outcomes.
Purpose of the Study:
- To apply Comorbidity Network Analysis (CNA) to identify and characterize comorbidity patterns in elderly patients undergoing hip fracture surgery.
- To determine central and interconnected diseases within this patient cohort to inform clinical practice.
Main Methods:
- Retrospective observational cohort study using electronic health records (EHR) from 1,171 patients (≥65 years) with hip fractures.
- Comorbidity Network Analysis (CNA) was performed on 17 Charlson Comorbidity Index diagnoses, quantifying associations using Observed-to-Expected Ratio (OER).
- Network centrality measures and cluster detection algorithms were employed to analyze disease interconnections and identify comorbidity clusters.
Main Results:
- Twelve diseases showed significant interconnections (OER > 1, p < .05).
- Strongest associations included metastatic carcinoma/mild liver disease, myocardial infarction/congestive heart failure, and hemi/paraplegia/cerebrovascular disease (OER > 2.5).
- Cerebrovascular disease, congestive heart failure, and myocardial infarction emerged as central nodes. Two clusters were identified, with the largest containing 10 diseases, predominantly cardiometabolic and cognitive disorders.
Conclusions:
- CNA effectively identified significant comorbidity patterns in older hip fracture patients.
- Central diseases and distinct comorbidity clusters were highlighted, offering insights for targeted clinical assessment and interventions.
- Findings can guide management strategies to improve outcomes for elderly patients with hip fractures and complex comorbidities.
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.
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