Harmon, David

Abstract Aims Rheumatic heart disease (RHD) remains the most prevalent acquired cardiovascular disease among young adults in developing countries, and there is a critical need for an efficient, affordable, and available screening strategy to detect RHD in these resource-limited areas. The 12-lead electrocardiogram (ECG) is a ubiquitous, inexpensive diagnostic tool, and application of artificial intelligence to the ECG (AI-ECG) has allowed for screening of various cardiovascular pathologies. Methods and results Patients aged >18 years with severe RHD with at least one transthoracic echocardiogram and one standard 12-lead ECG performed at Groote Schuur Hospital, Cape Town, within 180 days of each other were eligible. A total of 920 RHD patients were identified, with 2199 corresponding controls from the same medical centre. Patients were allocated to derivation (80%), validation (10%), and testing (10%) groups, respectively. The AI-ECG to detect RHD from a standard 12-lead ECG was built using the Keras framework under Tensorflow backend. In the test group, the AI-ECG for RHD exhibited strong algorithm performance with an AUC of 0.890 (95% Confidence Interval 0.848–0.940) and an accuracy of 83.8%. The algorithm remained robust between RHD presentation: mitral valve alone (AUC 0.913) and mixed mitral and aortic valve disease (AUC 0.858). There were minor performance differences in the subgroup analysis of patient sex, age, and ECG characteristics. Conclusion In this study, an AI-ECG algorithm was successfully developed to screen for advanced RHD from a standard 12-lead ECG, and the AI-ECG performance remained acceptable across most subgroups, including RHD manifestation, patient sex, age <70 years, and ECG characteristics.

Background Valvular heart disease contributes to symptomatic heart failure, but its prevalence and prognostic significance in advanced heart failure is not well understood. Methods This retrospective population‐based cohort study included adult residents of Olmsted County, Minnesota, with advanced heart failure (2007–2018). Patients were classified by echocardiographic regurgitation and stenosis of aortic, mitral, tricuspid, and pulmonic valves at the time of advanced heart failure. We examined the associations of aortic stenosis, mitral regurgitation, and tricuspid regurgitation with risks of cardiovascular and all‐cause death through 2023 using Cox proportional hazards models. Results There were 936 patients with advanced heart failure (mean age, 76.9 years; 44.5% women, 43.4% with heart failure with preserved ejection fraction). Moderate or greater regurgitation was observed in 2.8% (n=26) of aortic, 26% (n=244) of mitral, 42% (n=389) of tricuspid, and 4.3% (n=40) of pulmonic valves, while moderate or greater stenosis was observed in 9.7% (n=91) of aortic and 1.8% (n=17) of mitral valves. No patients had clinically significant tricuspid or pulmonic stenosis. After adjustment for age, sex, left ventricular ejection fraction, right ventricular function, serum sodium, Charlson comorbidity index, body mass index, and systolic blood pressure, moderate and severe aortic stenosis were associated with increased cardiovascular death compared with patients without aortic stenosis (hazard ratio [HR], 1.42 [95% CI, 1.01–2.01]; and HR, 1.55 [95% CI, 1.02–2.36]), respectively. Conclusions Tricuspid regurgitation, mitral regurgitation, and aortic stenosis were the most observed valvular pathologies of moderate or greater severity in advanced heart failure. Only moderate or greater aortic stenosis was independently associated with cardiovascular death in advanced heart failure.

ABSTRACT Background Valve‐in‐valve transcatheter aortic valve replacement (ViV‐TAVR) is increasingly used to treat structural valve degeneration. Permanent pacemaker implantation (PPI) following TAVR is associated with increased morbidity and mortality, yet predictors specific to ViV‐TAVR remain poorly defined. Objective To identify clinical and procedural predictors of PPI in patients undergoing ViV‐TAVR. Methods We conducted a retrospective observational study of consecutive ViV‐TAVR cases at the Mayo Clinic from 2012 to 2022. Clinical, ECG, procedural data and fluoroscopic measurements were analyzed, and propensity score matching was used to adjust for confounding. Predictors of PPI were identified using multivariable logistic regression. Results Among 261 patients, 24 (9.2%) underwent PPI. Balloon‐expandable valves were used in 66.7% and self‐expanding valves in 33.3%. Predictors of PPI included Preprocedural bifascicular block (OR 12.15, 95% CI: 2.52–68.89, p = 0.003), greater septal implantation depth (OR 1.29, 95% CI: 1.04–1.65, p = 0.019), and post‐procedural new left (OR 23.56, 95% CI: 4.91–48.52, p = 0.0004) and right bundle branch blocks (OR 24.4, 95% CI: 3.82–30.51, p = 0.001). Aortic regurgitation as the mechanism of prosthetic valve failure was associated with reduced PPI odds (OR 0.28, 95% CI: 0.10–0.76, p = 0.014). Pacing requirement—defined as > 90% ventricular pacing—increased from 25% at 3 months to 56.25% at 12 months. At a median follow‐up of 962.5 days (IQR 481.5–1555.3), adjusted mortality was significantly higher in the PPI group (aOR 9.4, 95% CI: 1.9–60.4, p = 0.028). Conclusion Bifascicular block, greater septal depth, and new bundle branch blocks predict PPI post‐ViV‐TAVR, while aortic regurgitation appears protective. Risk stratification and procedural planning are critical.

Background Valve-in-valve transcatheter aortic valve replacement (ViV-TAVR) is increasingly used to treat structural valve degeneration. Permanent pacemaker implantation (PPI) following TAVR is associated with increased morbidity and mortality, yet predictors specific to ViV-TAVR remain poorly defined. Objective To identify clinical and procedural predictors of PPI in patients undergoing ViV-TAVR. Methods We conducted a retrospective observational study of consecutive ViV-TAVR cases at the Mayo Clinic from 2012 to 2022. Clinical, ECG, procedural data and fluoroscopic measurements were analyzed, and propensity score matching was used to adjust for confounding. Predictors of PPI were identified using multivariable logistic regression. Results Among 261 patients, 24 (9.2%) required PPI. Balloon-expandable valves were used in 66.7% and self-expanding valves in 33.3%. Predictors of PPI included pre-procedural bifascicular block (OR 12.15, 95% CI: 2.52–68.89, p=0.003), greater septal implantation depth (OR 1.29, 95% CI: 1.04–1.65, p=0.019), and post-procedural new left (OR 23.56, 95% CI: 4.91–48.52, p=0.0004) and right bundle branch blocks (OR 24.4, 95% CI: 3.82–30.51, p=0.001). Aortic regurgitation as the mechanism of prosthetic valve failure was associated with reduced PPI odds (OR 0.28, 95% CI: 0.10–0.76, p=0.014). Pacing requirement—defined as >90% ventricular pacing—increased from 25% at 3 months to 56.25% at 12 months. At a median follow-up of 962.5 days (IQR 481.5–1555.3), adjusted mortality was significantly higher in the PPI group (aOR 9.4, 95% CI: 1.9–60.4, p=0.028). Conclusion Bifascicular block, greater septal depth, and new bundle branch blocks predict PPI post-ViV-TAVR, while aortic regurgitation appears protective. Risk stratification and procedural planning are critical.

Key Points Measuring blood potassium has always required access to blood. The surface electrocardiogram, analyzed using an artificial intelligence algorithm, can detect hyperkalemia bloodlessly. The artificial intelligence-analyzed electrocardiogram exhibited a high negative predictive value but substantially lower positive predictive value. Background Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings. Methods An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within 4 hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads 1 and 2 of the 12 lead ECGs to screen for hyperkalemia (potassium >6.0 mEq/L). Results The ED cohort ( N =40,128) had a mean age of 60 years, 48% were male, and 1% ( N =351) had hyperkalemia. The area under the curve (AUC) of the AI-enhanced ECG (AI-ECG) to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive likelihood ratio (LR+) of 80%, 80%, 3%, 99.8%, and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.83, 86%, 60%, 15%, 98%, and 2.2, respectively, in the ED cohort. The ICU cohort ( N =2636) had a mean age of 65 years, 60% were male, and 3% ( N =87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, PPV, NPV, and LR+ of 82%, 82%, 14%, 99%, and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.85, 88%, 67%, 29%, 97%, and 2.7, respectively in the ICU cohort. Conclusions The AI-ECG algorithm demonstrated a high NPV, suggesting that it is useful for ruling out hyperkalemia, but a low PPV, suggesting that it is insufficient for treating hyperkalemia.

Introduction: Hyperkalemia has been associated with increased mortality in cardiac intensive care unit (CICU) patients. An artificial intelligence (AI) augmented electrocardiogram (ECG) can predict hyperkalemia, and other AI-ECG algorithms have predicted mortality in CICU patients. Hypothesis: We hypothesized that the AI-ECG hyperkalemia algorithm could stratify mortality risk beyond the serum potassium measurement. Methods: We included 11 234 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who had both a 12 lead ECG and blood potassium (K) level obtained at admission with K>=5 mEq/L defining hyperkalemia. ECGs underwent AI evaluation for probability of hyperkalemia (probability >=0.5 defined as positive). Hospital mortality was analyzed using logistic regression and survival to one year was estimated using Kaplan-Meier and Cox analysis. Results: The mean age was 70 years, 37.8% were females, and 92.4% were white. Chronic kidney disease was present in 20%. The mean K value was 4.2 mEq/L, and 13% (n=1460) had hyperkalemia. AI-ECG predicted hyperkalemia in 34% (n=3820). In-hospital mortality was higher in patients with laboratory and/or AI-ECG hyperkalemia, and these patients had lower survival out to 1 year. Mortality was lowest in patients with normal K and negative AI-ECG and highest in patients with both laboratory-confirmed hyperkalemia and AI-ECG predicted hyperkalemia. Patients with isolated laboratory hyperkalemia (false-negative AI-ECG) had higher mortality than those with isolated AI-ECG hyperkalemia (false-positive AI-ECG). Conclusions: AI-ECG probability of hyperkalemia, even with a normal laboratory K, was associated with higher short-term and long-term mortality in CICU patients. The laboratory K value and AI-ECG probability of hyperkalemia are complementary risk factors. The AI algorithm can detect prognostically important ECG abnormalities which may reflect the effects of K levels on the myocardium.

AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.

Abstract Aims Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm’s long-term efficacy and potential bias in the absence of retraining. Methods and results Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90–0.92) with minimal performance difference between sexes. Patients with a ‘normal sinus rhythm’ electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). Conclusion The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features.