Risk stratification for 25-year cardiovascular disease incidence in type 1 diabetes: Tree-structured survival analysis of the Pittsburgh Epidemiology of Diabetes Complications study. (July 2016)

• Record Type: Journal Article
• Authors: Miller, Rachel G; Anderson, Stewart J; Costacou, Tina; Sekikawa, Akira; Orchard, Trevor J
• Is Part Of: Diabetes & vascular disease research. Volume 13:Number 4(2016:Jul.)
• Page Start: 250
• Page End: 259
• Publication Date: 2016-07
• Subjects: Decision trees -- type 1 diabetes -- cardiovascular disease -- risk stratification; Diabetic angiopathies -- Periodicals; 616.462005
• DOI: 10.1177/1479164116629353
• Languages: English
• ISSNs: 1479-1641

Abstract

Background: The formal identification of subgroups with varying levels of risk is uncommon in observational studies of cardiovascular disease, although such insight might be useful for clinical management. Methods: Tree-structured survival analysis was utilized to determine whether there are meaningful subgroups at varying levels of cardiovascular disease risk in the Pittsburgh Epidemiology of Diabetes Complications study, a prospective cohort study of childhood-onset (<17 years old) type 1 diabetes. Results: Of the 561 participants free of cardiovascular disease (coronary artery disease, stroke or lower extremity arterial disease) at baseline, 263 (46.9%) had an incident cardiovascular disease event over the 25-year follow-up. Tree-structured survival analysis revealed a range of risk groups, from 24% to 85%, which demonstrate that those with short diabetes duration and elevated non–high-density lipoprotein cholesterol have similar cardiovascular disease risk to those with long diabetes duration and that renal disease is a better discriminator of risk in men than in women. Conclusion: Our findings suggest that subgroups with major cardiovascular disease risk differences exist in this type 1 diabetes cohort. Using tree-structured survival analysis may help to identify these groups and the interrelationships between their associated risk factors. This approach may improve our understanding of various clinical pathways to cardiovascular disease and help target intervention strategies.