ArticleInternational journal of environmental research and public health2020
A Framework to Understand the Progression of Cardiovascular Disease for Type 2 Diabetes Mellitus Patients Using a Network Approach.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 25 citations in OpenAlex.
- The effect of a new developed synbiotic yogurt consumption on metabolic syndrome components in adults with metabolic syndrome: a randomized controlled clinical trial.Nutrition & diabetes · 2024Trial
- Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease.Frontiers in neuroscience · 2025Article
- An update of new/potential cardiovascular markers: a narrative review.Molecular biology reports · 2024Review
- Prediction of complications in health economic models of type 2 diabetes: a review of methods used.Acta diabetologica · 2023Review
- Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning.BMC medical informatics and decision making · 2023Article
- Development and validation of an insulin resistance model for a population without diabetes mellitus and its clinical implication: a prospective cohort study.EClinicalMedicine · 2023Article
- Machine learning models for classification and identification of significant attributes to detect type 2 diabetes.Health information science and systems · 2022Article
- Phenotypic Disease Network-Based Multimorbidity Analysis in Idiopathic Cardiomyopathy Patients with Hospital Discharge Records.Journal of clinical medicine · 2022Article
- Cardiovascular informatics: building a bridge to data harmony.Cardiovascular research · 2022Review
- Phenotypic Disease Network Analysis to Identify Comorbidity Patterns in Hospitalized Patients with Ischemic Heart Disease Using Large-Scale Administrative Data.Healthcare (Basel, Switzerland) · 2022Article
- Generalizability of the Results of Cardiovascular Outcome Trials of Glucagon-Like Peptide 1 Receptor Agonists in Chinese Patients with Type 2 Diabetes Mellitus.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2021Article
- Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities.Briefings in bioinformatics · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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