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Open AccessArticle

A Framework to Understand the Progression of Cardiovascular Disease for Type 2 Diabetes Mellitus Patients Using a Network Approach

1
Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Darlington, NSW 2008, Australia
2
School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(2), 596; https://doi.org/10.3390/ijerph17020596
Received: 9 January 2020 / Accepted: 14 January 2020 / Published: 16 January 2020
(This article belongs to the Special Issue Network Analytics in Healthcare Decision Making)
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. View Full-Text
Keywords: chronic disease; comorbidity; administrative data; graph theory; social network analysis chronic disease; comorbidity; administrative data; graph theory; social network analysis
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Hossain, M.E.; Uddin, S.; Khan, A.; Moni, M.A. A Framework to Understand the Progression of Cardiovascular Disease for Type 2 Diabetes Mellitus Patients Using a Network Approach. Int. J. Environ. Res. Public Health 2020, 17, 596.

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