Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.

BACKGROUND: Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2-mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)-enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. METHODS: Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the "gold standard". The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. RESULTS: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (-1.76±23.14 ms). Similarly, within the prospective, genetic heart disease-enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (-0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN's ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. CONCLUSIONS: Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.

Impact of hormone therapy on Medicare spending in the Women's Health Initiative randomized clinical trials.

BACKGROUND: Randomized trials can compare economic as well as clinical outcomes, but economic data are difficult to collect. Linking clinical trial data with Medicare claims could provide novel information on health care utilization and cost. METHODS: We linked data from Medicare claims of women ≥65 years old who had Medicare fee-for-service coverage with their clinical data from the Women's Health Initiative trials of conjugated equine estrogens plus medroxyprogesterone acetate (CEE+MPA) versus placebo and of CEE-alone versus placebo. The primary outcome was total Medicare spending during the intervention phase of the trial, and the secondary outcomes were spending on diseases hypothesized a priori to be sensitive to the effects of hormone therapy. RESULTS: In the CEE+MPA trial, 4,557 participants ≥65 years old were included. Women randomly assigned to CEE+MPA had 4% higher mean Medicare spending overall ($45,690 vs $43,920, P = .08) but 0.5% lower spending for hormone-sensitive diseases ($3,526 vs $3,547, P = .07), with 73% higher spending for coronary heart disease (P = .045) and 122% higher spending for pulmonary embolism (P = .026). In the CEE-alone trial, 3,107 participants were included. Total spending among women randomly assigned to CEE was 3.3% higher ($75,411 vs $72,997, P = .16), and 1.7% higher spending for hormone-sensitive diseases ($5,213 vs $5,127, P = .57), but with 39% lower spending for hip fracture (p<0.03). CONCLUSIONS: Menopausal hormone therapy increased spending for some diseases, but decreased spending for others. These offsetting effects led to modest (3%-4%), nonsignificant increases in overall spending among women aged 65 years and older.

Late diagnosis of abdominal aortic aneurysms substantiates underutilization of abdominal aortic aneurysm screening for Medicare beneficiaries.

OBJECTIVE: Abdominal aortic aneurysm (AAA) screening remains largely underutilized in the U.S., and it is likely that the proportion of patients with aneurysms requiring prompt treatment is much higher compared with well-screened populations. The goals of this study were to determine the proportion of AAAs that required prompt repair after diagnostic abdominal imaging for U.S. Medicare beneficiaries and to identify patient and hospital factors contributing to early vs late diagnosis of AAA. METHODS: Data were extracted from Medicare claims records for patients at least 65 years old with complete coverage for 2 years who underwent intact AAA repair from 2006 to 2009. Preoperative ultrasound and computed tomography was tabulated from 2002 to repair. We defined early diagnosis of AAA as a patient with a time interval of greater than 6 months between the first imaging examination and the index procedure, and late diagnosis as patients who underwent the index procedure within 6 months of the first imaging examination. RESULTS: Of 17,626 patients who underwent AAA repair, 14,948 met inclusion criteria. Mean age was 77.5 ± 6.1 years. Early diagnosis was identified for 60.6% of patients receiving AAA repair, whereas 39.4% were repaired after a late diagnosis. Early diagnosis rates increased from 2006 to 2009 (59.8% to 63.4%; P < .0001) and were more common for intact repair compared with repair after rupture (62.9% vs 35.1%; P < .0001) and for women compared with men (66.3% vs 59.0%; P < .0001). On multivariate analysis, repair of intact vs ruptured AAAs (odds ratio, 3.1; 95% confidence interval, 2.7-3.6) and female sex (odds ratio, 1.4; 95% confidence interval, 1.3-1.5) remained the strongest predictors of surveillance. Although intact repairs were more likely to be diagnosed early, over one-third of patients undergoing repair for ruptured AAAs received diagnostic abdominal imaging greater than 6 months prior to surgery. CONCLUSIONS: Despite advances in screening practices, significant missed opportunities remain in the U.S. Medicare population for improving AAA care. It remains common for AAAs to be diagnosed when they are already at risk for rupture. In addition, a significant proportion of patients with early imaging rupture prior to repair. Our findings suggest that improved mechanisms for observational management are needed to ensure optimal preoperative care for patients with AAAs.

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram.

Importance: For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition. Objective: To evaluate the performance of a deep-learning model in detection of hyperkalemia from the ECG in patients with CKD. Design, Setting, and Participants: A deep convolutional neural network (DNN) was trained using 1 576 581 ECGs from 449 380 patients seen at Mayo Clinic, Rochester, Minnesota, from 1994 to 2017. The DNN was trained using 2 (leads I and II) or 4 (leads I, II, V3, and V5) ECG leads to detect serum potassium levels of 5.5 mEq/L or less (to convert to millimoles per liter, multiply by 1) and was validated using retrospective data from the Mayo Clinic in Minnesota, Florida, and Arizona. The validation included 61 965 patients with stage 3 or greater CKD. Each patient had a serum potassium count drawn within 4 hours after their ECG was recorded. Data were analyzed between April 12, 2018, and June 25, 2018. Exposures: Use of a deep-learning model. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity, with serum potassium level as the reference standard. The model was evaluated at 2 operating points, 1 for equal specificity and sensitivity and another for high (90%) sensitivity. Results: Of the total 1 638 546 ECGs, 908 000 (55%) were from men. The prevalence of hyperkalemia in the 3 validation data sets ranged from 2.6% (n = 1282 of 50 099; Minnesota) to 4.8% (n = 287 of 6011; Florida). Using ECG leads I and II, the AUC of the deep-learning model was 0.883 (95% CI, 0.873-0.893) for Minnesota, 0.860 (95% CI, 0.837-0.883) for Florida, and 0.853 (95% CI, 0.830-0.877) for Arizona. Using a 90% sensitivity operating point, the sensitivity was 90.2% (95% CI, 88.4%-91.7%) and specificity was 63.2% (95% CI, 62.7%-63.6%) for Minnesota; the sensitivity was 91.3% (95% CI, 87.4%-94.3%) and specificity was 54.7% (95% CI, 53.4%-56.0%) for Florida; and the sensitivity was 88.9% (95% CI, 84.5%-92.4%) and specificity was 55.0% (95% CI, 53.7%-56.3%) for Arizona. Conclusions and Relevance: In this study, using only 2 ECG leads, a deep-learning model detected hyperkalemia in patients with renal disease with an AUC of 0.853 to 0.883. The application of artificial intelligence to the ECG may enable screening for hyperkalemia. Prospective studies are warranted.

Functional Testing or Coronary Computed Tomography Angiography in Patients With Stable Coronary Artery Disease.

BACKGROUND: The choice of either anatomical or functional noninvasive testing to evaluate suspected coronary artery disease might affect subsequent clinical management and outcomes. OBJECTIVES: This study analyzed the association of initial noninvasive cardiac testing in outpatients with stable symptoms, with subsequent use of medications, invasive procedures, and clinical outcomes. METHODS: We studied patients enrolled in a Danish nationwide register who underwent initial noninvasive cardiac testing with either coronary computed tomography angiography (CTA) or functional testing (exercise electrocardiography or nuclear stress testing) from 2009 to 2015. Further use of noninvasive testing, invasive procedures, medications, and medical costs within 120 days were evaluated. Risks of long-term mortality and myocardial infarction (MI) were analyzed using adjusted Cox proportional hazard models. RESULTS: A total of 86,705 patients underwent either functional testing (n = 53,744, mean age 57.4 years, 49% males) or coronary CTA (n = 32,961, mean age 57.4 years, 45% males), and were followed for a median of 3.6 years. Compared with functional testing, there was significantly higher use of statins (15.9% vs. 9.1%), aspirin (12.7% vs. 8.5%), invasive coronary angiography (14.7% vs. 10.1%), and percutaneous coronary intervention (3.8% vs. 2.1%); all p < 0.001 after coronary CTA. The mean costs of subsequent testing, invasive procedures, and medications were higher after coronary CTA ($995 vs. $718; p < 0.001). Unadjusted rates of mortality (2.1% vs. 4.0%) and MI hospitalization (0.8% vs. 1.5%) were lower after coronary CTA than functional testing (both p < 0.001). After adjustment, coronary CTA was associated with a comparable all-cause mortality (hazard ratio: 0.96; 95% confidence interval: 0.88 to 1.05), and a lower risk of MI (hazard ratio: 0.71; 95% confidence interval: 0.61 to 0.82). CONCLUSIONS: In stable patients undergoing initial evaluation for suspected coronary artery disease, coronary CTA was associated with greater use of statins, aspirin, and invasive procedures, and higher costs than functional testing. Coronary CTA was associated with a lower risk of MI, but a similar risk of all-cause mortality.