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Published on: November 29, 2024
A Framework to Understand the Progression of Cardiovascular Disease for Type 2 Diabetes Mellitus Patients Using a
Md Ekramul Hossain1, Shahadat Uddin1, Arif Khan1
1Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Darlington, NSW 2008, Australia.
Insights
Understanding the progression of cardiovascular disease (CVD) in patients with type 2 diabetes mellitus (T2DM) is crucial. This study used network analysis to identify higher risks of renal failure, hypertension, and obesity in patients with both T2DM and CVD.
Area of Science:
- Health Informatics
- Network Science
- Epidemiology
Background:
- Rising global prevalence of chronic disease comorbidity necessitates better understanding of disease progression.
- Comorbidity negatively impacts quality of life, mobility, and incurs significant financial burden.
- Administrative data offers valuable insights into patient health conditions for comorbidity research.
Purpose of the Study:
- To investigate the comorbidity progression of cardiovascular disease (CVD) in patients with type 2 diabetes mellitus (T2DM) within the Australian healthcare context.
- To develop and propose a novel network-based framework for representing and analyzing the progression of multiple chronic diseases.
- To identify specific comorbidities that frequently co-occur during the progression of CVD in T2DM patients.
Main Methods:
- Utilized administrative health insurance data from Australia to select study cohorts.
- Employed graph theory and social network analysis techniques to construct disease progression networks.
- Developed a normalized network model by adjusting weights between two baseline disease networks.
Main Results:
- Patients with both CVD and T2DM exhibited significantly higher prevalence of renal failure, fluid and electrolyte disorders, hypertension, and obesity compared to T2DM-only patients.
- Identified key comorbidities frequently occurring during CVD progression in T2DM patients.
- The network-based model revealed distinct progression patterns between T2DM and CVD recurrence.
Conclusions:
- The proposed network-based model aids in understanding high-risk diseases and progression patterns in T2DM and CVD comorbidity.
- This framework can inform healthcare providers in managing complex patient cases.
- The approach offers valuable insights for policymakers and health insurers to develop targeted preventive health programs for high-risk populations.
Abstract:
The prevalence of chronic disease comorbidity has increased worldwide. Comorbidity-i.e., the presence of multiple chronic diseases-is associated with adverse health outcomes in terms of mobility and quality of life as well as financial burden. Understanding the progression of comorbidities can provide valuable insights towards the prevention and better management of chronic diseases. Administrative data can be used in this regard as they contain semantic information on patients' health conditions. Most studies in this field are focused on understanding the progression of one chronic disease rather than multiple diseases. This study aims to understand the progression of two chronic diseases in the Australian health context. It specifically focuses on the comorbidity progression of cardiovascular disease (CVD) in patients with type 2 diabetes mellitus (T2DM), as the prevalence of these chronic diseases in Australians is high. A research framework is proposed to understand and represent the progression of CVD in patients with T2DM using graph theory and social network analysis techniques. Two study cohorts (i.e., patients with both T2DM and CVD and patients with only T2DM) were selected from an administrative dataset obtained from an Australian health insurance company. Two baseline disease networks were constructed from these two selected cohorts. A final disease network from two baseline disease networks was then generated by weight adjustments in a normalized way. The prevalence of renal failure, fluid and electrolyte disorders, hypertension and obesity was significantly higher in patients with both CVD and T2DM than patients with only T2DM. This showed that these chronic diseases occurred frequently during the progression of CVD in patients with T2DM. The proposed network-based model may potentially help the healthcare provider to understand high-risk diseases and the progression patterns between the recurrence of T2DM and CVD. Also, the framework could be useful for stakeholders including governments and private health insurers to adopt appropriate preventive health management programs for patients at a high risk of developing multiple chronic diseases.
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