Harmon, David M

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.

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.

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.

Introduction Mayo Clinic Connect is an online community of over 100,000 members who support each other through sharing lived experience when facing and managing new diagnoses. The community is moderated by Mayo Clinic staff and volunteer patient mentors. Methods Mayo Clinic breast clinic patients undergoing evaluation received a binder of support resources including a brochure about Mayo Clinic Connect at visits between January and May of 2019. Surveys were distributed at subsequent visits between May and December of 2019 to assess patient awareness about the online resource, participation frequency, purpose of use, and benefits for members, as well as reasons for not joining (non-members). The primary aim was to assess patient resilience, coping, and self-management after joining the online community. Results Nine hundred surveys were distributed, and 102 participants completed surveys between May and December 2019. Forty-five percent ( n = 46) had heard about Mayo Clinic Connect; 34% ( n = 15) through a brochure. The remainder heard about the community from a Mayo Clinic provider (43%; n = 19) or other resources (22%, n = 10; no response n = 2). Twenty percent ( n = 20) of survey participants registered as Breast Cancer group members, and most of this subgroup (55%; n = 11) reported understanding diagnosis, treatment plans, and finding peer support as reasons for joining. Seventy-five percent of Mayo Clinic Connect participants ( n = 15) reported the community met or exceeded expectations. Conclusion This pilot study reveals the potential positive impact of introducing an online peer support group into clinical care plans for patients coping with a new and anxiety-provoking cancer diagnosis.

Abstract Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.