ECG and Exercise Testing

Cluster RCT: AI-ECG Shows Potential to Reduce Door-to-Balloon Time and Cardiac Deaths in STEMI Patients – NEJM AI

10 Aug, 2024 | 21:57h | UTC

Study Design and Population: This open-label, cluster randomized controlled trial assessed the impact of AI-powered electrocardiogram (AI-ECG) on reducing treatment delays for ST-elevation myocardial infarction (STEMI). The study involved 43,234 patients, with an average age of 60 years, at Tri-Service General Hospital in Taiwan. Patients were randomized 1:1 into an intervention group (AI-ECG-assisted STEMI detection) or a control group receiving standard care.

Main Findings: AI-ECG significantly reduced the median door-to-balloon time for emergency department patients (82.0 vs. 96.0 minutes, P=0.002) and the ECG-to-balloon time across all settings (78.0 vs. 83.6 minutes, P=0.011). While the AI-ECG intervention did not significantly affect all-cause mortality or new-onset heart failure, it led to a notable reduction in cardiac death rates (85 vs. 116 cases; odds ratio, 0.73; P=0.029).

Implications for Practice: AI-ECG can expedite the critical time to treatment for STEMI patients, potentially reducing cardiac death. Although overall mortality remained unchanged, the reduction in cardiac deaths suggests that AI-ECG could be a valuable tool in emergency and inpatient settings to improve outcomes for STEMI patients.

Reference: Lin C. et al. (2024). Artificial Intelligence–Powered Rapid Identification of ST-Elevation Myocardial Infarction via Electrocardiogram (ARISE) — A Pragmatic Randomized Controlled Trial. NEJM AI, 1(7). DOI: 10.1056/AIoa2400190.

RCT: Ponatinib shows superior MRD-negative complete remission rates compared to Imatinib in newly diagnosed Philadelphia chromosome–positive acute lymphoblastic leukemia – JAMA

25 May, 2024 | 19:03h | UTC

This global phase 3 randomized clinical trial investigated the efficacy and safety of ponatinib versus imatinib in adults with newly diagnosed Philadelphia chromosome–positive acute lymphoblastic leukemia (Ph+ ALL). The study, conducted across 77 sites, enrolled 245 patients who were randomized in a 2:1 ratio to receive either ponatinib (30 mg/d) or imatinib (600 mg/d) alongside reduced-intensity chemotherapy. The primary endpoint of the trial was minimal residual disease–negative (MRD-negative) complete remission, assessed at the end of cycle 3. Results showed that ponatinib achieved a significantly higher MRD-negative complete remission rate of 34.4% compared to 16.7% with imatinib. Additionally, the safety profile between the two drugs was comparable, with arterial occlusive events being rare and similar across groups. These findings suggest ponatinib as a potentially preferable frontline therapy in this patient population due to its superior efficacy in achieving MRD-negative status without compromising safety.

Reference (link to abstract – $ for full-text):

Jabbour E, Kantarjian HM, Aldoss I, et al. (2024). Ponatinib vs Imatinib in Frontline Philadelphia Chromosome–Positive Acute Lymphoblastic Leukemia: A Randomized Clinical Trial. JAMA. Published online May 9, 2024. doi:10.1001/jama.2024.4783

Cohort Study | Applying machine learning to ECG may improve identification of occlusion MI in individuals with chest pain

18 Jul, 2023 | 13:32h | UTC

Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction – Nature Medicine

News Release: New AI tool beats standard approaches for detecting heart attacks – University of Pittsburgh

Podcast | EKG pearls in children

11 Jul, 2023 | 13:50h | UTC

#88: The EKG with Mike Fah-EY – The Cribsiders

Machine learning proves effective in predicting left ventricular hypertrophy through ECG analysis, study shows

13 Jun, 2023 | 13:57h | UTC

Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using machine learning – European Heart Journal – Digital Health

Commentary on Twitter

Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using #MachineLearning https://t.co/3pnVQtkPH5 #EHJDigital #WhyCMR @BruiningNico @rafavidalperez @GerdHindricks @rbcasado pic.twitter.com/jbjuBJVIHW

— European Society of Cardiology Journals (@ESC_Journals) June 2, 2023

ACC/AHA Multimodality appropriate use criteria for the detection and risk assessment of chronic coronary disease

29 May, 2023 | 11:18h | UTC

ACC/AHA/ASE/ASNC/ASPC/HFSA/HRS/SCAI/SCCT/SCMR/STS 2023 Multimodality Appropriate Use Criteria for the Detection and Risk Assessment of Chronic Coronary Disease – Journal of the American College of Cardiology

Commentaries:

New Multimodality AUC Address Detection and Risk Assessment of Chronic Coronary Disease – American College of Cardiology

2023 Multimodality Appropriate Use Criteria for Chronic Coronary Disease: Key Points – American College of Cardiology

Commentary on Twitter

New multimodality AUC released by @ACCinTouch, key specialty & subspecialty societies, provides guidance on the appropriate use of stress testing & anatomic diagnostic procedures for risk assessment & evaluation of known or suspected CCD. https://t.co/56ewQWqW13 #JACC pic.twitter.com/WIqPyEDwfC

— JACC Journals (@JACCJournals) May 25, 2023

Position Paper | Accuracy and reliability of ECG monitoring in the detection of Afib in cryptogenic stroke patients

31 Jan, 2023 | 13:53h | UTC

ESC Working Group on e-Cardiology Position Paper: accuracy and reliability of electrocardiogram monitoring in the detection of atrial fibrillation in cryptogenic stroke patients: In collaboration with the Council on Stroke, the European Heart Rhythm Association, and the Digital Health Committee – European Heart Journal – Digital Health

Commentary on Twitter

.@escardio Working Group on e-Cardiology Position Paper: accuracy and reliability of #ECG monitoring in the detection of #AFib in cryptogenic stroke patients https://t.co/MKAAeYwvqn #EHJDigital @BruiningNico @rafavidalperez @pauldub67 @rbcasado @EHRAPresident pic.twitter.com/7Jso8clTVv

— European Society of Cardiology Journals (@ESC_Journals) June 13, 2022

Commentary from the author on Twitter (thread – click for more)

Out today in @JACCJournals, our ValveNet study shows a ML model can detect mod-severe left sided valve disease from ECG. This model is currently running live in our health system daily. Now let me tell you everything wrong with it! A brief ? https://t.co/rjtZxxTLt9 pic.twitter.com/oZKDDnZqYU

— Pierre Elias, MD (@PierreEliasMD) August 1, 2022

Navigating a Complicated World | Commentary of the new AHA/ACC Chest Pain Guidelines.

12 Jul, 2022 | 12:40h | UTC

Navigating a Complicated World: The American Heart Association/American College of Cardiology/American College of Chest Physicians/Society of Academic Emergency Medicine/Society of Cardiovascular Computed Tomography/Society of Cardiovascular Magnetic Resonance Chest Pain Guidelines – CHEST

Original Guideline: 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines – Journal of the American College of Cardiology

News Release: First-Ever Guideline Focused Solely on Chest Pain Evaluation, Diagnosis Released

Guideline Hub: Guideline for the Evaluation and Diagnosis of Chest Pain

Key Points: 2021 AHA/ACC Chest Pain Guideline – American College of Cardiology

Commentary: New Chest Pain Guidelines Out at Last: Most Tests Have a Role – TCTMD

Guideline Synopsis | Evaluation and diagnosis of chest pain.

4 Jul, 2022 | 12:49h | UTC

Evaluation and Diagnosis of Chest Pain – JAMA (free for a limited period)

Original Guideline:

2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines – Journal of the American College of Cardiology