Accurate prediction of diabetes and pre-diabetes by AI + ECG heart trace

Families with at least one known case of type 2 diabetes and living in Nagpur, which has a high density of Sindhi people, were enrolled in the study.

Participants provided details of their personal and family medical histories, and their normal diet, and underwent a full range of blood tests and clinical assessments. Their average age was 48 and 61% of them were women.

Pre-diabetes and diabetes were identified from the diagnostic criteria specified by the American Diabetes Association.

The prevalence of both type 2 diabetes and pre-diabetes was high: around 30% and 14%, respectively. And the prevalence of insulin resistance was also high-35%---as was the prevalence of other influential coexisting conditions—high blood pressure (51%), obesity (around 40%), and disordered blood fats (36%).

A standard 12-lead ECG heart trace lasting 10 seconds was done for each of the 1262 participants included. And 100 unique structural and functional features for each lead were combined for each of the 10,461 single heartbeats recorded to generate a predictive algorithm (diabetes).

Important ECG features consistently matched the known biological triggers underpinning cardiac changes that are typical of diabetes and pre-diabetes.

Ref:

Dr Hemant Kulkarni et. al, Machine-learning algorithm to noninvasively detect diabetes and prediabetes from an electrocardiogram, BMJ Innovations, 9-Aug-2022,10.1136/bmjinnov-2021-000759