Application of the Artificial Intelligence Model for Detection of Electrocardiographic Signs of Coronary Occlusion in Patients with Non ST-Elevation Acute Coronary Syndrome

Author:

Kalashnikov Sviatoslav A.ORCID,Salo Sergii V.ORCID,Stepaniuk Andrii V.ORCID,Sandu SabiORCID,Lazoryshynets Vasyl V.ORCID

Abstract

The aim. This study aimed to determine the effectiveness of the OMI AI deep learning model for the diagnosis of myocardial infarction in patients with non ST-elevation acute coronary syndrome. Materials and methods. This single-center retrospective observational study analyzed the data of 238 patients admitted to the National Amosov Institute of Cardiovascular Surgery of the National Academy of Medical Sciences of Ukraine with a primary diagnosis of non ST-elevation acute coronary syndrome. The inclusion criteria for the study were: age ≥18 years, symptoms of acute coronary syndrome, at least one 10-second 12-lead electrocardiography on admission, no changes typical of ST-segment elevation myocardial infarction on electrocardiography, and at least one laboratory blood test for biomarkers of myocardial damage. Results. The final analysis included data from 116 patients, 69 (59.5%) men and 47 (40.5%) women aged 43 to 88 years (mean age 67±11 years), of whom 34 were older patients (≥75 years). Of these, 29 (25%) patients were discharged with a diagnosis of acute myocardial infarction, 60 (51.7%) with a diagnosis of unstable angina, and 27 (23.3%) patients with other diagnoses. When analyzing electrocardiographic data by the OMI AI model, true positive results were obtained in 23 cases (19.8%), true negative results in 76 cases (65.5%), false positive results in 11 cases (9.5%), and false negative results in 6 cases (5%). Accordingly, the model’s sensitivity was 67% and specificity was 93%. The positive and negative predictive values for the model under study were 0.793 and 0.874, respectively. The accuracy of the model was 85.34% (95% CI: 77.78% to 90.64%). Conclusions. The use of the artificial intelligence tools has the potential to improve the accuracy of diagnosis of myocardial infarction during hospitalization, accelerate the provision of specialized care and improve prognosis in patients with non ST-elevation acute coronary syndrome.

Publisher

Professional Edition Eastern Europe

Reference11 articles.

1. Number of deaths by specific causes of death 2021. Kyiv: State Statistics Service of Ukraine; c2022 [cited 2024 Apr 13]. Available from: https://ukrstat.gov.ua/operativ/operativ2021/ds/kpops/arh_kpops2021_u.html

2. Reports of the NHSU on the fulfillment of contracts for medical services under the medical guarantees program 2022. Kyiv: National Health Service of Ukraine; c2022 [cited 2024 Apr 13]. Available from: https://edata.e-health.gov.ua/e-data/zviti

3. Byrne RA, Rossello X, Coughlan JJ, Barbato E, Berry C, Chieffo A, et al.; ESC Scientific Document Group. 2023 ESC Guidelines for the management of acute coronary syndromes. Eur Heart J. 2023;44(38):3720-3826. https://doi.org/10.1093/eurheartj/ehad191

4. Bhatt DL, Lopes RD, Harrington RA. Diagnosis and Treatment of Acute Coronary Syndromes: A Review. JAMA. 2022;327(7):662-675. https://doi.org/10.1001/jama.2022.0358

5. Haq IU, Chhatwal K, Sanaka K, Xu B. Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects. Vasc Health Risk Manag. 2022;18:517-528. https://doi.org/10.2147/VHRM.S279337

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3