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. 2020 Jan 16;17(2):596.
doi: 10.3390/ijerph17020596.

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

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Free PMC article

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

Md Ekramul Hossain et al. Int J Environ Res Public Health. .
Free PMC article

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.

Keywords: administrative data; chronic disease; comorbidity; graph theory; social network analysis.

Conflict of interest statement

The authors declare no conflict of interest in this manuscript.

Figures

Figure 1
Figure 1
Construction of baseline disease network. First, individual disease networks are developed from medical data of the corresponding patients and are then aggregated to generate the baseline disease network.
Figure 2
Figure 2
Proposed framework to understand the progression of cardiovascular disease in patients with type 2 diabetes. SNA: social network analysis.
Figure 3
Figure 3
Flow diagram of selecting the patients of cohorts.
Figure 4
Figure 4
Top 10 comorbidities that attributed most to the progress of CVD in patients with T2DM.
Figure 5
Figure 5
Final disease network of T2DM patients progressing towards CVD. The node size and labels are proportional to the prevalence of the corresponding comorbidity. The thickness of an edge between two comorbidities is proportional to its weight.

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