Using Routine Data to Categorize Poor Control Diabetic Patients: An Application of Cluster Analysis Technique
Farid Abolhassani Shahreza1, Narjes Hazar2
1Health Services Division, National Institute of Health Research, Tehran University of Medical Sciences, Tehran, Iran.
Iranian Journal of Public Health
|April 29, 2017
Summary
This study identified distinct patient groups for better diabetes management. Men without risk factors formed one group, while illiterate, obese women with or without dyslipidemia formed two others, highlighting targeted intervention needs.
Area of Science:
- Endocrinology
- Public Health
- Data Science
Background:
- Diabetes mellitus is a leading global cause of morbidity and mortality.
- Chronic hyperglycemia drives microvascular and macrovascular complications.
- Effective management requires understanding patient heterogeneity.
Purpose of the Study:
- To identify homogenous subgroups of poorly controlled diabetic patients using cluster analysis.
- To inform targeted interventions for improved diabetes care.
- To analyze demographic and clinical data for risk stratification.
Main Methods:
- Utilized a two-step cluster analysis on medical records from 2087 diabetic patients.
- Data collected from 23 healthcare centers in Iran (2010-2014).
- Included demographic, physical, and biochemical measurements.
Main Results:
- Three distinct patient clusters emerged.
- Cluster 1: Men without apparent risk factors.
- Clusters 2 & 3: Illiterate women with obesity; Cluster 2 also showed dyslipidemia.
Conclusions:
- Masculinity appears significant in diabetes control.
- Obesity, illiteracy, and dyslipidemia in women are linked to poor diabetes control.
- Findings enable tailored interventions for identified patient subgroups.
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