| Category |
Cardiac & Cardiovascular Systems |
| Manuscript Type |
Observational Study |
| Article Title |
Cardiac comorbidity role in the diagnosis of diabetes mellitus using single lead electrocardiogram utilizing machine learning model
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Anna Dmitrievna Karbovskaya, Basheer Abdullah Marzoog, Snezhana Nikolaevna Demchuk, Anastasia Stroeva, Daniil Andreevich Zelenov, Peter Chomakhidze, Natalia Kuznetsova, Syrkin Abromavich, Valentin V Fadeev, Irina V Poluboyarinova, Sevindzh M Ismailova and Philipp Kopylov |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| the Application of Mass Spectrometry and Exhaled Air Emission Spectrometry for Cardiovascular Risk Stratification |
1023022600020-6 |
| the Priority 2030 Program of the Ministry of Science and Higher Education of Russia |
03.000.B.163, 03.000. B. 166 |
|
| Corresponding Author |
Basheer Abdullah Marzoog, PhD, Post Doctoral Researcher, Postdoc, Postdoctoral Fellow, Research Fellow, Researcher, Senior Postdoctoral Fellow, Senior Research Fellow, Senior Researcher, Senior Scientist, Institute of Personalized Cardiology of The Center “Digital Biodesign and Personalized Healthcare” of Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, 8-2 Trubetskaya Street, Moscow 119991, Moskva, Russia. marzug@mail.ru |
| Key Words |
Diabetes mellitus; Single-lead electrocardiography; Machine learning; Cardiovascular disease; Model calibration; Decision-curve analysis |
| Core Tip |
This study demonstrates that single-lead electrocardiography combined with anthropometric variables provides moderate internal discrimination of type 1 and type 2 diabetes mellitus, with median areas under the receiver operating characteristic curve of 0.693 and 0.661, respectively. Although model performance varied across cardiac-comorbidity subgroups, none of these differences remained statistically significant after adjustment for multiple comparisons. A reduced seven-predictor model demonstrated broadly comparable discrimination, suggesting that fewer variables may be sufficient. However, substantial probability miscalibration and threshold-dependent clinical net benefit limit immediate clinical applicability. These findings highlight the potential of electrocardiography-based machine learning while emphasizing the need for patient-level and external validation before clinical implementation. |
| Citation |
Karbovskaya AD, Marzoog BA, Demchuk SN, Stroeva A, Zelenov DA, Chomakhidze P, Kuznetsova N, Abromavich S, Fadeev VV, Poluboyarinova IV, Ismailova SM, Kopylov P. Cardiac comorbidity role in the diagnosis of diabetes mellitus using single lead electrocardiogram utilizing machine learning model. World J Cardiol 2026; In press
|
| PDF |
126754-in-press.pdf
|
| ISSN |
1949-8462 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
