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Development and validation of a machine learning model for diagnosis of ischemic heart disease using single-lead
Basheer Abdullah Marzoog1, Peter Chomakhidze2, Daria Gognieva2
1World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya Street, 119991, Moscow, Russia. marzug@mail.ru.
Insights
Single-lead electrocardiogram (ECG) shows higher diagnostic accuracy than bicycle ergometry for detecting ischemic heart disease (IHD). Further research is needed to explore the full potential of ECG in IHD diagnosis.
Area of Science:
- Cardiology
- Medical Imaging
- Biomedical Engineering
Background:
- Ischemic heart disease (IHD) is a leading cause of mortality globally.
- Accurate diagnosis of IHD is crucial for improving patient outcomes.
Purpose of the Study:
- To compare the diagnostic accuracy of single-lead electrocardiogram (ECG) parameters versus bicycle ergometry.
- To evaluate these methods against computed tomography (CT) myocardial perfusion imaging in individuals with and without IHD.
Main Methods:
- An observational study involving 80 participants aged ≥ 40 years.
- Comparison of resting and exertion single-lead ECG (Cardio-Qvark) with CT myocardial perfusion imaging.
- Machine learning (LASSO regression) applied to analyze ECG parameters and identify associations with perfusion defects.
Main Results:
- Bicycle ergometry showed limited diagnostic performance (AUC 50.7%).
- The Cardio-Qvark single-lead ECG demonstrated superior performance (AUC 67%), with higher specificity (75.5%) and comparable sensitivity (51.6%).
Conclusions:
- Single-lead ECG, analyzed with machine learning, offers higher diagnostic accuracy than bicycle ergometry for IHD, though not statistically significant.
- Further investigation is warranted to fully elucidate the diagnostic capabilities of single-lead ECG in IHD.
Background:
Ischemic heart disease (IHD) impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally.
Aim:
To compare variations in the parameters of the single-lead electrocardiogram (ECG) during resting conditions and physical exertion in individuals diagnosed with IHD and those without the condition using vasodilator-induced stress computed tomography (CT) myocardial perfusion imaging as the diagnostic reference standard.
Methods:
This single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study. Both groups, G1 (n = 31) with and G2 (n = 49) without post stress induced myocardial perfusion defect, passed cardiologist consultation, anthropometric measurements, blood pressure and pulse rate measurement, echocardiography, cardio-ankle vascular index, bicycle ergometry, recording 3-min single-lead ECG (Cardio-Qvark) before and just after bicycle ergometry followed by performing CT myocardial perfusion. The LASSO regression with nested cross-validation was used to find the association between Cardio-Qvark parameters and the existence of the perfusion defect. Statistical processing was performed with the R programming language v4.2, Python v.3.10 [^R], and Statistica 12 program.
Results:
Bicycle ergometry yielded an area under the receiver operating characteristic curve of 50.7% [95% confidence interval (CI): 0.388-0.625], specificity of 53.1% (95%CI: 0.392-0.673), and sensitivity of 48.4% (95%CI: 0.306-0.657). In contrast, the Cardio-Qvark test performed notably better with an area under the receiver operating characteristic curve of 67% (95%CI: 0.530-0.801), specificity of 75.5% (95%CI: 0.628-0.88), and sensitivity of 51.6% (95%CI: 0.333-0.695).
Conclusion:
The single-lead ECG has a relatively higher diagnostic accuracy compared with bicycle ergometry by using machine learning models, but the difference was not statistically significant. However, further investigations are required to uncover the hidden capabilities of single-lead ECG in IHD diagnosis.
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