In-Hospital Prognostic Value of Electrocardiographic Parameters Other Than ST-Segment Changes in Acute Myocardial Infarction: Literature Review and Future Perspectives

Hayıroğlu, Mert İlker; Lakhani, Ishan; Tse, Gary; Çınar, Tufan; Çinier, Göksel; Tekkeşin, Ahmet İlker

doi:10.1016/j.hlc.2020.04.011

Cited by 9 publications

(5 citation statements)

References 67 publications

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“…Therefore, it is necessary to investigate novel indicators to identify ACO. With the emergence of new ECG indicators [ 6 , 7 ] and risk scores [ 8 , 9 ] like Aslanger’s pattern for the identification of acute obstructive myocardial infarction, more ACO cases will be identified early and quickly and given timely revascularization in the future.…”

Section: Discussionmentioning

confidence: 99%

A patient with acute myocardial infarction with electrocardiogram Aslanger’s pattern

Liu,

Li,

et al. 2024

BMC Cardiovasc Disord

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Background Aslanger’s pattern in electrocardiogram (ECG) indicates that patients may have acute inferior myocardial infarction(AMI) with concomitant critical stenoses on other coronary arteries, which needs to be evaluated the timing of revascularization as risk equivalents of ST elevation myocardial infarction(STEMI). Case Presentation The patient was a 62-year-old male with chief complaint of intermittent exertional subxiphoid pain for 20 days from 30th June. One day after the last episode (19th July), the 18-lead electrocardiogram showed ST segment elevation of 0.05-0.1mV in lead III, ST segment depression in leads I, avL, and V2-V6, T wave inversion with positive terminal vector in lead V4-V5, and positive T wave in lead V6, which indicated Aslanger’s pattern. With increased Troponin I (0.162ng/mL, 0-0.02), The patient was diagnosed as acute non-ST-segment elevation myocardial infarction (NSTEMI) and admitted to coronary ward on 20th July. The coronary angiography showed 95% stenosis in the distal left main coronary artery (LM) to the ostium of the left anterior descending artery (LAD), 90% stenosis in the proximal segment of the LAD, and 80% stenosis in the middle segment of the LAD, and TIMI blood flow was graded score 2. Three drug-eluting stents were implanted at the lesions. The patient’s ECG returned close to normal one month after revascularization. Conclusion We presented an acute coronary syndrome case whose ECG showed with Aslanger’s pattern (i.e., isolated ST-segment elevation in lead III, associated ST-segment depression in lead V4-V6 with positive T wave/terminal vector, and greater ST-segment elevation in lead V1 than in lead V2), and was confirmed severe stenosis of the LM and the proximal segment of the LAD via coronary angiography. In clinical practice, especially in the emergency, patients with ECG presenting Aslanger’s pattern should be urgently evaluated with prompt treatment, and the timing of emergency coronary angiography and revascularization should be evaluated to avoid adverse outcomes caused by delayed treatment.

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Section: Discussionmentioning

confidence: 99%

A patient with acute myocardial infarction with electrocardiogram Aslanger’s pattern

Liu,

Li,

et al. 2024

BMC Cardiovasc Disord

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“…Among these ECG parameters, ST-segment change has been commonly used to identify CHD. Nevertheless, in recent decades, accumulative evidence has suggested that other than ST-segment change, other parameters such as QTc and Tp-e can also be used to screen and identify the possibility of CHD 22 . In the current study, we only used the ST-segment change which has also shown a good predictive value.…”

Section: Discussionmentioning

confidence: 99%

Utilization of Risk Scores for Coronary Heart Disease Diagnosis in Rural China

Liao¹,

Chen²,

Liu³

et al. 2022

IJGM

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Objective The current study was to design a cardiovascular risk score for the diagnosis of coronary heart disease (CHD) in the rural area of China and the sensitivity and specificity of this score would be assessed. Methods A total of 520 patients were enrolled and based on the results from coronary artery angiography, patients were divided into three groups: CHD group (coronary artery ≥50% stenosis), atherosclerosis group (coronary artery <50% stenosis) and normal groups (without stenosis). Between-group differences were evaluated and the sensitivity and specificity of cardiovascular risk score were evaluated. Results Compared to the normal and atherosclerosis groups, patients in the CHD group were older, had higher body mass index, and more likely to be smoking and obese, and had dyslipidemia, hypertension and diabetes, and had higher cardiovascular risk score (4.05 ± 2.15 vs 2.94 ± 1.90 vs 2.54 ± 1.59). Patients in the CHD group were more likely to have cardiovascular risk scores ≥2 (90.2% CHD group vs 74.2% atherosclerosis group vs 76.1% normal group, P < 0.05). The area under the ROC was 0.673, with 95% confidence interval was 0.623–0.722 (P < 0.001), and the sensitivity and specificity were highest when the cardiovascular risk score was 4, indicating that the value of cardiovascular risk score of 4 was a good cutoff point for CHD diagnosis. Conclusion Using cardiovascular risk score can improve CHD diagnosis which may help to reduce health disparities between rural and urban area.

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“…This issue can be solved using visualization techniques such as Grad-CAM [ 32 ], which can display image areas that contribute to classification. In addition, it is important to present scores based on ECG indices as proposed in the previous research [ 7 , 8 , 9 , 33 ]. Such scores can provide important information regarding fast triage and prognostic effects for MI patients.…”

Section: Discussionmentioning

confidence: 99%

“…To date, many research groups have attempted to predict the prognosis from ECG progression in patients with MI [ 7 , 8 , 9 ]. In these studies, the prognostic prediction was performed using parameter values calculated from ECG signals.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Convolutional Neural Network Model to Detect and Localize Myocardial Infarction Using 12-Lead ECG Images without Preprocessing

et al. 2022

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In recent years, many studies have proposed automatic detection and localization techniques for myocardial infarction (MI) using the 12-lead electrocardiogram (ECG). Most of them applied preprocessing to the ECG signals, e.g., noise removal, trend removal, beat segmentation, and feature selection, followed by model construction and classification based on machine-learning algorithms. The selection and implementation of preprocessing methods require specialized knowledge and experience to handle ECG data. In this paper, we propose an end-to-end convolutional neural network model that detects and localizes MI without such complicated multistep preprocessing. The proposed model executes comprehensive learning for the waveform features of unpreprocessed raw ECG images captured from 12-lead ECG signals. We evaluated the classification performance of the proposed model in two experimental settings: ten-fold cross-validation where ECG images were split randomly, and two-fold cross-validation where ECG images were split into one patient and the other patients. The experimental results demonstrate that the proposed model obtained MI detection accuracies of 99.82% and 93.93% and MI localization accuracies of 99.28% and 69.27% in the first and second settings, respectively. The performance of the proposed method is higher than or comparable to that of existing state-of-the-art methods. Thus, the proposed model is expected to be an effective MI diagnosis tool that can be used in intensive care units and as wearable technology.

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In-Hospital Prognostic Value of Electrocardiographic Parameters Other Than ST-Segment Changes in Acute Myocardial Infarction: Literature Review and Future Perspectives

Cited by 9 publications

References 67 publications

A patient with acute myocardial infarction with electrocardiogram Aslanger’s pattern

A patient with acute myocardial infarction with electrocardiogram Aslanger’s pattern

Utilization of Risk Scores for Coronary Heart Disease Diagnosis in Rural China

End-to-End Convolutional Neural Network Model to Detect and Localize Myocardial Infarction Using 12-Lead ECG Images without Preprocessing

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