Machine Learning may Identify High-Risk Diabetic Cardiomyopathy Phenotype: Study
Researchers have found that using a machine learning-based method, it is possible to identify individuals with diabetes who are most at risk for developing heart failure due to diabetic cardiomyopathy (DbCM). DbCM is a recognized stage of subclinical myocardial abnormalities that precede clinical heart failure (HF), at which echocardiographic and cardiac biomarker abnormalities exist but symptoms of heart failure are absent. The recent study was conducted by Segar and colleagues and was published in the European Journal of Heart Failure.
1,199 participants in diabetes without cardiovascular disease who also did not have causes of cardiomyopathy were included from the cohort of Atherosclerosis Risk in Communities (ARIC). Unsupervised hierarchical approach of clustering stratified individuals based on 25 echocardiographic parameters as well as markers of cardiac biomarkers: neurohormonal markers of stress and markers of myocardial injury. Cluster analysis produced three phenogroups that were differentiated with one labeled as high risk for HF, given that it is associated with an increased rate of HF events at the 5-year follow-up. The data were used to train a DeepNN classifier, which was validated subsequently in two cohorts: the Cardiovascular Health Study (CHS) cohort of n=802 and the EHR cohort from the University of Texas Southwestern Medical Center of n=5071.
• Clustering analysis identified three phenogroups among diabetic patients. Phenogroup-3, consisting of 27% of the ARIC cohort, had a significantly higher 5-year HF incidence rate of 12.1% compared with Phenogroups 1 and 2, which had HF incidence rates of 3.1% and 4.6%, respectively. This high-risk phenotype was associated with higher NT-proBNP levels, increased left ventricular mass, larger left atrial size, and poorer diastolic function-all important markers of heightened HF risk.
• The DeepNN classifier had excellent predictive capability and identified 16% of high-risk DbCM cases in the CHS cohort and 29% in the UT Southwestern EHR cohort. Of great interest, the incidence of HF was significantly increased among the high-risk DbCM phenotype individuals identified by the classifier, with hazard ratios of 1.61 (95% CI 1.18–2.19) in the CHS cohort and 1.34 (95% CI 1.08–1.65) in the UT Southwestern EHR cohort.
This study has shown the potential of machine learning-based approaches to identify individuals with diabetes as having a high-risk DbCM phenotype, an opportunity for targeted HF prevention strategies. Researchers believe that predictive models in clinical practice will manage and mitigate the risk of HF in diabetic patients by earlier and more aggressive intervention strategies.
Reference:
