Clinical Implications of the ISCHEMIA Trial: Invasive vs Conservative Approach in Stable Coronary Disease

Choudhary, Jamal; Chiu, Sarah; Bhugra, Priyanka; Bikdeli, Behnood; Supariwala, Azhar; Jauhar, Rajiv; Chatterjee, Saurav

doi:10.1007/s11886-021-01470-x

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…According to the World Health Organization, cardiovascular diseases (CVDs) are the leading cause of death globally (Choudhary et al 2021, Ng 2016). An estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths (WHO 2021).…”

Section: Introductionmentioning

confidence: 99%

Advanced repeated structuring and learning procedure to detect acute myocardial ischemia in serial 12-lead ECGs

et al. 2023

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Acute myocardial ischemia in the setting of acute coronary syndrome (ACS) may lead to myocardial infarction. Therefore, timely decisions, already in the pre-hospital phase, are crucial to preserving cardiac function as much as possible. Serial electrocardiography, a comparison of the acute electrocardiogram (AECG) with a previously recorded (reference) ECG (RECG) of the same patient, aids in identifying ischemia-induced electrocardiographic changes by correcting for interindividual ECG variability. Recently, the combination of deep learning and serial electrocardiography provided promising results in detecting emerging cardiac diseases; thus, the aim of our current study is the application of our novel Advanced Repeated Structuring & Learning Procedure (AdvRS&LP), specifically designed for acute myocardial ischemia detection in the pre-hospital phase by using serial ECG features. Data belong to the SUBTRACT study, which includes 1425 ECG pairs, 194 (14%) ACS patients, and 1035 (73%) controls. Each ECG pair was characterized by 28 serial features that, with sex and age, constituted the inputs of the Advanced Repeated Structuring & Learning Procedure (AdvRS&LP), an automatic constructive procedure for creating supervised neural networks (NN). We created 100 NNs to compensate for statistical fluctuations due to random data divisions of a limited dataset. We compared the performance of the obtained NNs to a logistic regression (LR) procedure and the Glasgow program (Uni-G) in terms of area-under-the-curve (AUC) of the receiver-operating-characteristic (ROC) curve, sensitivity (SE), and specificity (SP). NNs (median AUC=83%, median SE=77%, and median SP=89%) presented statistically (P value lower than 0.05) higher testing performance than those presented by LR (median AUC=80%, median SE=67%, and median SP=81%) and by Uni-G algorithm (median SE=72% and median SP=82%). In conclusion, the positive results underscore the value of serial comparison of ECG in ischemia detection and, NNs created by AdvRS&LP seems to be considered as realible tools in terms of generalization and clinical applicability.

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Section: Introductionmentioning

confidence: 99%