Letter to the Editor: Do we need an artificial intelligence-assisted electrocardiographic tool to diagnose diabetes mellitus or to predict its unseen cardiovascular consequences?

Abstract

Diabetes mellitus (DM) has a significant negative impact on the global health. Its burden on the medical sector is tremendous as its complications affect all body organs across all age groups. The most dramatic complications of DM include cardiovascular and neurological disorders. Microvascular damage can start years before the diagnosis of type 2 diabetes (T2DM) is made; therefore, early screening is of utmost value. Moreover, subclinical electrocardiographic (ECG) changes are common in patients with T2DM without evident cardiac disease. I have read with great interest the recent study published in World Journal of Cardiology by Karbovskaya et al, on the utility of a single-lead ECG for diagnosing DM, using machine learning and multinomial regression. The utility of single-lead ECG for predicting glycemic levels appears questionable. It should be interpreted with caution, particularly in light of the model’s limited explanatory power and elevated maximum error. A single-center, non-randomized study with a small sample size in the DM groups and misclassification bias are limitations of the study. A simple, easily accessible tool for early detection or prediction of cardiac dysfunction in DM or in people at risk is more valuable than merely distinguishing healthy from DM or type 1 diabetes from T2DM. A single-lead ECG, especially with artificial intelligence, can flag risk and help bridge some gaps in predicting event risk. However, this study needs validation with a precise aim to predict high-risk diabetics and not only to diagnose DM.

Keywords: Diabetes mellitus; Electrocardiography; Artificial intelligence-assisted; Diagnostic tool; Machine learning; Cardiovascular; Photoplethysmography; Electrocardiographic

Core Tip: Diabetes mellitus (DM) has a significant negative impact on the global health. The most dramatic complications of diabetes include the microvascular damage that can start years before the diagnosis of type 2 diabetes (T2DM) is made; therefore, early screening is of utmost value. Moreover, subclinical electrocardiographic (ECG) changes are common in patients with T2DM without evident cardiac disease. A simple, easily accessible tool for early detection or prediction of cardiac dysfunction in DM or in people at risk is more valuable than merely distinguishing healthy individuals from those with diabetes or type 1 diabetes from T2DM. A single-lead ECG, especially with artificial intelligence, can red-flagging risk and help bridge some gaps in predicting event risk. However, this study needs validation with a precise aim to predict high-risk diabetics and not only to diagnose DM.