ST-elevation acute myocardial infarction: a critical but difficult electrocardiographic diagnosis

Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals

Cardiovascular diseases (CVDs) have become the number 1 threat to human health. Their numerous complications mean that many countries remain unable to prevent the rapid growth of such diseases, although significant health resources have been invested toward their prevention and management. Electrocardiogram (ECG) is the most important non-invasive physiological signal for CVD screening and diagnosis. For exploring the heartbeat event classification model using single- or multiple-lead ECG signals, we proposed a novel deep learning algorithm and conducted a systemic comparison based on the different methods and databases. This new approach aims to improve accuracy and reduce training time by combining the convolutional neural network (CNN) with the bidirectional long short-term memory (BiLSTM). To our knowledge, this approach has not been investigated to date. In this study, Database I with single-lead ECG and Database II with 12-lead ECG were used to explore a practical and viable heartbeat event classification model. An evolutionary neural system approach (Method I) and a deep learning approach (Method II) that combines CNN with BiLSTM network were compared and evaluated in processing heartbeat event classification. Overall, Method I achieved slightly better performance than Method II. However, Method I took, on average, 28.3 h to train the model, whereas Method II needed only 1 h. Method II achieved an accuracy of 80, 82.6, and 85% compared with the China Physiological Signal Challenge 2018, PhysioNet Challenge 2017, and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia datasets, respectively. These results are impressive compared with the performance of state-of-the-art algorithms used for the same purpose.

DIagnostic accuracy oF electrocardiogram for acute coronary OCClUsion resuLTing in myocardial infarction (DIFOCCULT Study)

Background

Although ST-segment elevation (STE) has been used synonymously with acute coronary occlusion (ACO), current STE criteria miss nearly one-third of ACO and result in a substantial amount of false catheterization laboratory activations. As many other electrocardiographic (ECG) findings can reliably indicate ACO, we sought whether a new ACO/non-ACO myocardial infarction (MI) paradigm would result in better identification of the patients who need acute reperfusion therapy.

Methods

A total of 3000 patients were enrolled in STEMI, non-STEMI and control groups. All ECGs were reviewed by two cardiologists, blinded to any outcomes, for the current STEMI criteria and other subtle signs. A combined ACO endpoint was composed of peak troponin level, troponin rise within the first 24 h and angiographic appearance. The dead or alive status was checked from hospital records and from the electronic national database.

Results

In non-STEMI group, 28.2% of the patients were re-classified by the ECG reviewers as having ACO. This subgroup had a higher frequency of ACO, myocardial damage, and both in-hospital and long-term mortality compared to non-STEMI group. A prospective ACOMI/non-ACOMI approach to the ECG had superior diagnostic accuracy compared to the STE/non-STEMI approach in the prediction of ACO and long-term mortality. In Cox-regression analysis early intervention in patients with non-ACO-predicting ECGs was associated with a higher long-term mortality.

Conclusions

We believe that it is time for a new paradigm shift from the STEMI/non-STEMI to the ACOMI/non-ACOMI in the acute management of MI. (DIFOCCULT study; ClinicalTrials.gov number, NCT04022668.).

Electrocardiogram differentiation of benign early repolarization versus acute myocardial infarction by emergency physicians and cardiologists

OBJECTIVES

ST-segment elevation (STE) related to benign early repolarization (BER), a common normal variant, can be difficult to distinguish from acute myocardial infarction (AMI). The authors compared the electrocardiogram (ECG) interpretations of these two entities by emergency physicians (EPs) and cardiologists.

METHODS

Twenty-five cases (13 BER, 12 AMI) of patients presenting to the emergency department with chest pain were identified. Criteria for BER required four of the following: 1) widespread STE (precordial greater than limb leads), 2) J-point elevation, 3) concavity of initial up-sloping portion of ST segment, 4) notching or irregular contour of J point, and 5) prominent, concordant T waves. Additional BER criteria were 1) stable ECG pattern, 2) negative cardiac injury markers, and 3) normal cardiac stress test or angiography. AMI criteria were 1) regional STE, 2) positive cardiac injury markers, and 3) identification of culprit coronary artery by angiography in less than eight hours of presentation. The 25 ECGs were distributed to 12 EPs and 12 cardiologists (four in academic medicine, four in community practice, and four in community academics [health maintenance organization] in each physician group). The physicians were informed of the patients' age, gender, and race, and they then interpreted the ECGs as BER or AMI. Undercalls (AMI misdiagnosed as BER) and overcalls (BER misdiagnosed as AMI) were calculated for each physician group.

RESULTS

Cardiologists correctly interpreted 90% of ECGs, and EPs correctly interpreted 81% of ECGs. The proportion of undercalls (missed AMI/total AMI) was 2.8% for cardiologists (95% confidence interval [CI] = 0.09% to 5.5%) compared with 9.7% for EPs (95% CI = 4.8% to 14.6%) (p = 0.02). The proportion of overcalls (missed BER/total BER) was 17.3% for cardiologists (95% CI = 11.4% to 23.3%) versus 27.6% for EPs (95% CI = 20.6% to 34.6%) (p = 0.03). The mean number of years in practice was 19.8 for cardiologists (95% CI = 19 to 20.5) and 11 years for EPs (95% CI = 10.5 to 12.0) (p < 0.001).

CONCLUSIONS

Although correct interpretation was high in both groups, cardiologists, who had significantly more years of practice, had fewer misinterpretations than EPs in distinguishing BER from AMI electrocardiographically.