Feasibility of remote monitoring for fatal coronary heart disease using Apple Watch ECGs

Single-lead ECGs with wearable technology: diagnostic accuracy in patients with cardiovascular disease

The past, present, and future of the implantable cardioverter defibrillator

POSTER PRESENTATIONS

The decline in coronary heart disease: Determining the paternity of success

Objective To investigate the electrocardiogram analysis of electronic colonoscopy on patients with coronary artery heart disease,and to evaluate the safety of colonoscopy on patients with coronary artery heart disease.Methods Sixty patients who underwent colonoscopy from Jun.2012 to Jun.2013 were divided into experimental group (patients with coronary artery heart disease,Heart function class Ⅰ-Ⅲ) and control group (patients without coronary artery heart disease).The changes of electrocardiography during colonoscopy and before performance were compared between two groups through dynamic electrocardiogram.Results Heart rate of the two groups were no statistically significant difference before colonoscopy process (t =0.537,P ＞ 0.05).During the inspection process,there was heart rate increase at different degree in two groups.The heart rate in patients of experiments group was increased from (73.20 ± 7.91) times/min to (88.67 ± 7.79) times/min,which waas more than that in control group (from (73.40 ±6.44) times/min to (74.88 ±7.82) times/min),and the difference between the two groups was significant(t =4.462,P ＜ 0.05).During colonoscopy inspection,the arrhythmia rate arrhythmia in experiment and control group were 46.67％ (14/30),20.00％ (6/30),and the difference was statistically significant (x2 =4.8,P ＜0.05).Meanwhile,ST-T change rates in experimental group and control group were 26.7％ (8/30) and 10.0％ (3/30) (x2 =45.72,P ＜ 0.05).The rate of subjective discomfort the two groups were 40％,30％ (x2 =0.659,P ＞ 0.05).Conclusion During the inspection process of colonoscopy,patients with coronary heart disease are more susceptible to increase heart rate,cardiac arrhythmia,ST-T change than those without coronary heart disease.However,no serious electrocardiographic changes.It is relatively safe to get colonoscopy in patients with coronary heart disease. Key words: Electronic colonoscopy; Coronary heart disease; Electrocardiogram

Impact of clinical expertise and choice of wearable device on accuracy to detect atrial fibrillation via single-lead ECGs

Background: Wearable devices can capture 1-lead ECGs but are primarily used for detecting rhythm disorders. AI deployed on these ECGs could enable scalable detection of structural heart diseases (SHDs). However, wearable ECGs are noisier than clinical ECGs, and it is essential to develop and validate noise-resilient models that reliably detect SHD on real-world wearable devices. Aim: We first developed and externally validated a noise-adapted AI-ECG model to detect SHD from lead I of clinical ECGs. In the prospective WATCH-SHD study, we then assessed its performance in detecting SHD from an Apple Watch-acquired 1-lead ECG. Methods: Using 266,054 ECGs from 110,006 patients at Yale (2015–23), we developed an AI-ECG algorithm to detect SHD from lead I ECGs (resembling Apple Watch 1-lead ECGs) paired with echocardiograms within 30 days. SHD was defined as a composite of LVEF <40%, severe left-sided valvular disease, or severe LVH (IVSd >15 mm + LV diastolic dysfunction). ECGs were augmented with random Gaussian noise during training to improve the model’s robustness against noisy signal acquisition. The model was then externally validated in 44,591 patients across 4 community hospitals and 3,014 participants from the population-based ELSA-Brasil. Subsequently, we prospectively enrolled 600 participants undergoing an outpatient echocardiogram at Yale and obtained a 30-s, 1-lead ECG with an Apple Watch to assess the AI tool’s performance in detecting SHD. ECG acquisition and inference were conducted in real-time using the CarDS-Plus app. Results: The AI model had an AUROC of 0.92 (95% CI, 0.91-0.93) for detecting SHD from lead I of clinical ECGs in the Yale test set and generalized well to the external cohorts, with AUROCs ranging from 0.89 to 0.92. In the prospective WATCH-SHD, 596 of 600 participants (99.3%; median age 62 years [IQR, 46–71]; 52% women) successfully obtained a 1-lead ECG on the Apple Watch, of whom 21 (5.3%) had SHD. This included 15 with LVEF <40%, 5 with severe valvular disease, and 1 with severe LVH. The AI model had an AUROC of 0.88 (0.78–0.98) for detecting SHD from Apple Watch-acquired 1-lead ECG. At the threshold for optimizing Youden’s index, the model’s sensitivity was 86%, specificity 87%, NPV 99%, and PPV 27% for detecting SHD. Conclusions: A noise-adapted AI tool integrated with an automated platform can detect SHD from a 30-s, 1-lead ECG acquired with an Apple Watch. This has the potential to transform SHD screening in communities.

Risk factors for sudden coronary death in the United States.

Current guideline statements for primary and secondary prevention of cardiovascular disease (CVD) rely on estimates of absolute risk of coronary events. For example, the American Heart Association guidelines on primary prevention state that persons with ≥10% risk over 10 years of myocardial infarction (MI) or coronary death should be considered for antiplatelet therapy with aspirin.1 Similarly, the National Cholesterol Education Program Adult Treatment Panel III (ATP III) guidelines2 state that target low-density lipoprotein level should be based on projected absolute risk of future coronary events rather than on presence or absence of specific risk factors. These guidelines state that patients at high risk of MI and coronary death, defined as an absolute 10-year risk of ≥20%, should have a target low-density lipoprotein level <100 mg/dL and should receive statin therapy if needed to achieve this goal. Stroke, however, is not included as one of the outcomes contributing to these absolute risk levels. Included in the group of patients with elevated risk, moreover, are those who already have ischemic heart disease, as well as patients deemed to be “coronary heart disease (CHD) risk equivalents,” indicating those at the same elevated risk as patients with ischemic heart disease. CHD risk equivalents include patients with diabetes mellitus, those with multiple risk factors that put them at elevated risk based on calculation of their Framingham Score, and patients with “other forms of symptomatic atherosclerotic disease.” The latter group is further defined to include those with peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. The category of “risk equivalents” in the ATP III guidelines, however, does not include the vast majority (≈80%3) of ischemic stroke patients without carotid artery disease as cause of their stroke. Ischemic stroke is therefore notably excluded from the list of outcomes contributing to …

Automated atrial fibrillation detection with a smartwatch and smart-ring in individuals with cardiovascular disease

Guidelines for Perioperative Cardiovascular Evaluation for Noncardiac Surgery: An Abridged Version of the Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines

Association of P-Wave Abnormalities With Sudden Cardiac and Cardiovascular Death: The ARIC Study.

ECG-AIR: AN AI PLATFORM FOR REMOTE SMARTWATCH ECG-BASED CARDIOVASCULAR DISEASE DETECTION AND PREDICTION

At least 25% of coronary patients have sudden death or nonfatal myocardial infarction without prior symptoms.1 Therefore, the search for coronary patients with subclinical disease who could potentially benefit from intensive primary prevention efforts is critically important. The American Heart Association’s (AHA) Prevention V Conference, “Beyond Secondary Prevention: Identifying the High Risk Patient for Primary Prevention,” addressed ways to identify more patients who are asymptomatic and clinically free of coronary heart disease (CHD) but at sufficiently high risk for a future coronary event to justify more intensive risk reduction efforts.2 In this report, we amplify on key findings and recommendations of the AHA Prevention V conference, highlight new research since the conference, and propose an approach to the use of office-based testing and additional noninvasive procedures in selected patients to better define their coronary event risk. The recommendations are concordant with the recently released approach to risk assessment and management from the third report of the Adult Treatment Panel of the National Cholesterol Education Program (ATP-III).3 Enthusiasm for primary prevention and risk assessment in asymptomatic people has been spurred by recent advances in prevention research. Lipid-lowering trials demonstrated that primary prevention of coronary events is feasible, evidenced by the West of Scotland Coronary Primary Prevention Study (WOSCOPS) trial4 of hypercholesterolemic men and by the Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS) trial5 in average or typical risk men and women with only moderate lipid abnormalities. Aspirin6 or ACE inhibitors7 can also reduce risk in selected asymptomatic, high-risk people. Emerging coronary risk factors have been described including inflammatory, infectious, and thrombotic markers,8 and there has been a steady flow of reports that focus attention on potential new ways of predicting coronary risk.9 In addition, noninvasive tests for subclinical atherosclerotic disease are available …

ObjectiveTo assess the accuracy of recently commercialized wearable devices in heart rate (HR) measurement during cardiopulmonary exercise test (CPX) under gradual increase in exercise intensity, while wearable devices with HR monitors are reported to be less accurate in different exercise intensities.MethodsCPX was performed for patients with coronary artery disease (CAD). Twelve lead electrocardiograph (ECG) was the gold standard and Apple watch 7 (AW7), Galaxy watch 4 (GW4) and Bio Patch Mobicare 200 (MC200) were applied for comparison. Paired absolute difference (PAD), mean absolute percentage error (MAPE) and intraclass correlation coefficient (ICC) were evaluated for each device.ResultsForty-four participants with CAD were included. All the devices showed MAPE under 2% and ICC above 0.9 in rest, exercise and recovery phases (MC200=0.999, GW4=0.997, AW7=0.998). When comparing exercise and recovery phase, PAD of MC200 and AW7 in recovery phase were significantly bigger than PAD of exercise phase (p<0.05). Although not significant, PAD of GW4 tended to be bigger in recovery phase, too. Also, when stratified by HR 20, ICC of all the devices were highest under HR of 100, and ICC decreased as HR increased. However, except for ICC of GW4 at HR above 160 (=0.867), all ICCs exceeded 0.9 indicating excellent accuracy.ConclusionThe HR measurement of the devices validated in this study shows a high concordance with the ECG device, so CAD patients may benefit from the devices during high-intensity exercise under conditions where HR is measured reliably.