Noseworthy, Peter A.

Abstract Aims Atrial functional mitral regurgitation (AFMR) has been associated with atrial fibrillation (AF) and heart failure with preserved ejection fraction. However, data on incident AFMR are scarce. We aimed to study the incidence, risk factors, and clinical significance of AFMR in AF or sinus rhythm (SR). Methods and results Adults with new diagnosis of AF and adults in SR were identified. Patients with >mild MR at baseline, primary mitral disease, cardiomyopathy, left-sided valve disease, previous cardiac surgery, or with no follow-up echocardiogram were excluded. Diastolic dysfunction (DD) was indicated by ≥2/4 abnormal diastolic function parameters [mitral medial e′, mitral medial E/e′, tricuspid regurgitation velocity, left atrial volume index (LAVI)]. Overall, 1747 patients with AF and 29 623 in SR were included. Incidence rate of >mild AFMR was 2.6 per 100 person-year in new-onset AF and 0.7 per 100 person-year in SR, P < 0.001. AF remained associated with AFMR in a propensity score-matched analysis based on age, sex, and comorbidities between AF and SR [hazard ratio: 3.80 (95% confidence interval 3.04–4.76)]. Independent risk factors associated with incident AFMR were age ≥65 years, female sex, LAVI, and DD in both AF and SR, in addition to rate (vs. rhythm) control in AF. Incident AFMR was independently associated with all-cause death in both groups (both P < 0.001). Conclusions AF conferred a three-fold increase in the risk of incident AFMR. DD, older age, left atrial size, and female sex were independent risk factors in both SR and AF, while rhythm control was protective. AFMR was universally associated with worse mortality.

Background Physiological monitoring systems, like Masimo, used during inpatient hospitalisation, offer a non-invasive approach to capture critical vital signs data. These systems trigger alarms when measurements deviate from preset parameters. However, often non-urgent or potentially false alarms contribute to ‘alarm fatigue,’ a form of sensory overload that can have adverse effects on both patients and healthcare staff. The Joint Commission, in 2021, announced a target to mitigate alarm fatigue-related fatalities through improved alarm management. Yet, no established guidelines are presently available. This study aims to address alarm fatigue at the Mayo Clinic to safeguard patient safety, curb staff burnout and improve the sensitivity of oxygen saturation monitoring to promptly detect emergencies. Methods A quality improvement project was conducted to combat minimise the false alarm burden, with data collected 2 months prior to intervention commencement. The project’s goal was to decrease the total alarm value by 20% from 55%–85% to 35%–75% within 2 months, leveraging quality improvement methodologies. Interventions February to April 2021, we implemented a two-pronged intervention: (1) instituting a protocol to evaluate patients’ continuous monitoring needs and discontinuing it when appropriate, and (2) introducing educational signage for patients and Mayo Clinic staff on monitoring best practices. Results Baseline averages of red alarms (158.6), manual snoozes (37.8) and self-resolves (120.7); the first postintervention phase showed reductions in red alarms (125.5), manual snoozes (17.8) and self-resolves (107.8). Second postintervention phase recorded 138 red alarms, 13 manual snoozes and 125 self-resolves. Baseline comparison demonstrated an average of 16.92% reduction of alarms among both interventions (p value: 0.25). Conclusion Simple interventions like education and communication techniques proved instrumental in lessening the alarm burden for patients and staff. The findings underscore the practical use and efficacy of these methods in any healthcare setting, thus contributing to mitigating the prevalent issue of alarm fatigue.

Introduction Decision aids (DAs) are helpful instruments used to support shared decision making (SDM). Patients with atrial fibrillation (AF) face complex decisions regarding stroke prevention strategies. While a few DAs have been made for AF stroke prevention, an encounter DA (EDA) and patient DA (PDA) have not been created to be used in conjunction with each other before. Design Using iterative user-centered design, we developed 2 DAs for anticoagulation choice and stroke prevention in AF. Prototypes were created, and we elicited feedback from patients and experts via observations of encounters, usability testing, and semistructured interviews. Results User testing was done with 33 experts (in AF and SDM) and 51 patients from 6 institutions. The EDA and PDA underwent 1 and 4 major iterations, respectively. Major differences between the DAs included AF pathophysiology and a preparation to meet with the clinician in the PDA as well as different language throughout. Content areas included personalized stroke risk, differences between anticoagulants, and risks of bleeding. Based on user feedback, developers 1) addressed feelings of isolation with AF, 2) improved navigation options, 3) modified content and flow for users new to AF and those experienced with AF, 4) updated stroke risk pictographs, and 5) added structure to the preparation for decision making in the PDA. Limitations These DAs focus only on anticoagulation for stroke prevention and are online, which may limit participation for those less comfortable with technology. Conclusions Designing complementary DAs for use in tandem or separately is a new method to support SDM between patients and clinicians. Extensive user testing is essential to creating high-quality tools that best meet the needs of those using them. Highlights First-time complementary encounter and patient decision aids have been designed to work together or separately. User feedback led to greater structure and different experiences for patients naïve or experienced with anticoagulants in patient decision aids. Online tools allow for easier dissemination, use in telehealth visits, and updating as new evidence comes out.

Abstract Funding Acknowledgements Type of funding sources: None. Background Atrial functional mitral regurgitation (AFMR) has been associated with atrial fibrillation (AF) and heart failure with preserved ejection fraction. However, data on its incidence, risk factors, and clinical outcomes are scarce. Objective We aimed to study the incidence, risk factors and clinical significance of AFMR, defined as >mild functional MR in the absence of more than mild left ventricle enlargement or dysfunction, in AF and sinus rhythm (SR). Methods Adults with a new diagnosis of AF between 2010–2021 who had a transthoracic echocardiogram (TTE) were identified (n=57,109). Similarly, adults with no diagnosis of AF who had a TTE between 2010–2021 were identified for comparison (n= 364,767). Patients with >mild MR at baseline, primary mitral valve disease, cardiomyopathy, ≥moderate aortic valve disease, current or previous low ejection fraction, enlarged left ventricle, cardiac devices, previous cardiac surgeries, or with no follow up TTE ≥6 months from baseline were excluded. Furthermore, patients with AF had to have a TTE within 1 month of AF diagnosis. The presence of ≥2 of 4 abnormal diastolic function parameters [mitral septal e’ <0.07 m/sec, mitral septal E/e’ >15 in SR and >11 in AF, TR velocity >2.80 m/sec, left atrial volume index >34 ml/m2] was determined. Cox proportional hazards regression was used to determine risk factors associated with incident AFMR and all-cause death. Patients without the event of interest were censored at last follow up. Patients who developed >mild MR of other types (primary or functional ventricular) were censored at that time. Results Overall, 1,747 (median age 67 years; 34% females) patients with AF and 29,623 (median age 59; 51% females) in SR were included. Incident AFMR occurred in 167 patients with AF over median 3.1 (IQR 1.6–5.1) years [incidence rate of 2.6 per 100-person year] and in 780 patients in SR over median 3.1 (IQR 1.5–5.8) years [incidence rate 0.7 per 100-person year] (Figure 1). AF remained associated with AFMR after adjusting to confounding factors [age ≥65, female sex, ≥2 abnormal diastolic function parameters, and low hemoglobin; adjusted HR 3.23 (2.71–3.85)]. Independent risk factors associated with incident AFMR in AF were age ≥65, female sex, persistent AF, and ≥2 abnormal diastolic function parameters, whereas successful rhythm control without recurrence was a protective factor (Figure 2a). Risk factors for AFMR in SR were age ≥65 years, female sex, presence of ≥2 abnormal diastolic function parameters, and low hemoglobin (Figure 2a). Incident AFMR was independently associated with all-cause death in both AF and SR (Figure 2b). Conclusions AF confers more than 3-fold increase in the risk of AFMR. Diastolic dysfunction, older age, and female sex were independent risk factors for incident AFMR in both SR and AF. Paroxysmal AF and successful rhythm control were protective factors against AFMR in AF. AFMR is universally associated with worse mortality.

Introduction: Shared decision-making (SDM) about anticoagulant treatment in patients with atrial fibrillation (AF) is widely recommended but its effectiveness is unclear. We assessed the extent to which using an SDM tool promotes high-quality SDM. Hypothesis: SDM will improve communication quality, knowledge, and decisional conflict and will translate to improved medication adherence and outcomes. Methods: In this multicentre encounter-randomized trial, we included patients with nonvalvular AF considering starting/reviewing anticoagulation, and their clinicians. We compared usual care with or without ANTICOAGULATION CHOICE, an SDM conversation tool for use during the clinical encounter that presents individualized risk estimates and compares anticoagulation options across patient important issues. Results: We enrolled and video-recorded 922 patient-clinician encounters. So far, our data have shown that participants in both arms reported near-optimal communication quality, knowledge, and decisional conflict (JAMA IM-2020). Patients in intervention-arm estimated their stroke risks more accurately and were more involved in decision-making (JAMA IM-2020), clinicians were more satisfied (JAMA IM-2020), and cost conversation were more common (JAMA NO-2021). Use of the intervention had no effect on treatment decisions, encounter duration (JAMA IM-2020), treatment adherence, clinical outcomes (JAHA-epub) or patient-perceived sense of the care plan (PEC-epub). New analyses have shown that compared to eligible white participants, black participants more frequently did not enrol to the study. Enrolment of Black, indigenous, and people of colour benefited most form including and prioritizing practices most likely to care for them. We found no evidence of contamination (tool use, functional, or learned) between study arms. Conclusions: Use of an SDM tool improved some measures of SDM quality, without affecting treatment decisions and their sensibility, encounter duration, patient adherence, or clinical outcomes. Our results should calibrate expectations as to what could be accomplished by implementing SDM tools about anticoagulation in the care of patients with AF

Background: An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm has shown good performance in detecting hypertrophic cardiomyopathy (HCM AI-score). However, its clinical application may be challenging due to the low disease prevalence and potentially high false-positive rate when screening unenriched cohorts. Aims: To identify clinical characteristics associated with false-positive HCM AI-score results to improve screening tool application. Methods: From a cohort of 20,677 patients with a digital 12-lead ECG in routine clinical practice in 2021, we performed a detailed chart review of the 200 patients with highest HCM AI-score to determine their HCM status and collect clinical data. Stepwise forward logistic regression was used to create a clinical variable-based “candidacy for HCM screening” score (CHS score), differentiating true- and likely true-positive from likely false-positive HCM AI-score results. We then validated the CHS score in 200 patients with the highest HCM AI-scores from an independent cohort of 15,147 patients with an ECG in 2022. Results: Among the 200 patients in 2021 (median age 71 years, 48% female), 96 (48%) were likely false-positive HCM AI-score results, 71 (36%) definite or likely true-positive results, and 33 (16%) indeterminate. The CHS score included a history of surgical septal myectomy, coronary artery bypass graft surgery, cardiac valve replacement, and pacemaker, and had an AUC of 0.88 (95% CI 0.83-0.92) for the differentiation of true- versus false-positive AI results. In the 2022 cohort, the CHS score had an AUC of 0.82 (95% CI 0.76 - 0.88). After limiting the cohort to HCM candidates identified by the CHS score, the false-positive results rate was reduced to 27/114 (24%). Conclusions: By applying the CHS score to patients with positive HCM AI-screening results, the false-positive rate decreased from 49% to 24%. Utilizing a clinical model used in tandem with an AI model improved diagnostic yield.

Evaluating the Electrocardiographic Characteristics of Transgender Patients Undergoing Gender-affirming Hormone Therapy with Artificial Intelligence Introduction: An artificial intelligence (AI) electrocardiogram (ECG) algorithm has been demonstrated to identify a patient’s birth designated sex with 90.4% accuracy with an AUC = 0.97. However, the algorithm has not been evaluated in transgender and gender diverse individuals. We aimed to determine how the algorithm output varies among transgender individuals seen at the Mayo Clinic Transgender and Intersex Specialty Care Clinic (TISCC) who are undergoing gender-affirming hormonal treatment (GAHT). Methods: We applied the algorithm (which assessed the probability of “male” between 0 and 1, with < 0.5 considered as “female” and > 0.5 as “male”) to ECGs obtained from patients in the TISCC before and after the initiation of GAHT. GAHT frequently involves the use of testosterone in transgender men, and estrogen and anti-androgen agents in transgender women. Patient characteristics, including the date of the initiation of GAHT, were collected through chart review. Results: Among transgender women, the AI model probability of “male” decreased from 0.84± 0.25 (12/86 classified as males) to 0.59±0.36 (68/173 classified as male) with GAHT (p < 7.8e-10). Among transgender men, the AI model probability of “male” increased from 0.16 ± 0.28 (7/47 classified as male) to 0.41 ±0.38 (22/53 classified as male) with GAHT (p < 2.4e-4). Conclusion: After GAHT initiation, the Mayo Clinic AI ECG algorithm assigns a higher probability of being male among transgender men and a lower probability of being male among transgender women. These findings warrant further investigation into the electrocardiographic differences across genders and the effects of GAHT on the human heart.

Introduction: Over 100 FDA-approved medications, electrolyte perturbations, and many disease states can prolong the QTc beyond its 99 th percentile value resulting in acquired QT prolongation. In contrast, approximately 1 in 2000 people have congenital long QT syndrome (LQTS) hallmarked by pathological QT prolongation secondary to genetic defects in the heart. Hypothesis: An artificial intelligence (AI) deep neural network (DNN) can distinguish patients with LQTS from those with acquired QT prolongation. Methods: The study cohort included all patients with LQTS evaluated in the Windland Smith Rice Genetic Heart Rhythm Clinic and controls from Mayo Clinic’s ECG data vault comprising over 2.5 million patients. For the AI-DNN model, every patient/control with ≥ 1 ECG above age- and sex- specific 99 th percentile values for QTc [> 460 ms for all patients (male/female) < 13 years of age, or > 470 ms for men and > 480 ms for women above this age] was included. An AI-DNN involving a multi-layer c onvolutional n eural n etwork (CNN) was developed to classify patients. LQTS patients were age- and sex- matched to controls at 1:5 ratio. Results: Among the 1,599 patients with genetically confirmed LQTS, 808 had ≥ 1 ECG with QTc above the threshold (2,987 ECGs) compared to 361,069/2.5M controls (14% of Mayo Clinic patients getting an ECG, ‘presumed negative’; 989,313 ECGs). Following age- and sex- matching and splitting, 3,309 (training), 411 (validation) and 887 (testing) control ECGs were used. This model distinguished LQTS from those with acquired QT prolongation with an AUC of 0.896 (accuracy 85%, sensitivity 77%, specificity 88%, PPV 0.58, NPV 0.94). After exclusion of patients with a wide QRS (>150 ms) or pacemaker, the model remained successful in distinguishing the two groups (AUC 0.85, accuracy 78%, sensitivity 78%, specificity 87%, PPV 59%, NPV 94%). Conclusions: For patients with a QTc exceeding its 99 th percentile values, this novel AI-DNN functions as a LQTS mutation detector being able to identify patients with abnormal QT prolongation secondary to a LQTS-causative mutation rather than acquired QT prolongation with a >50% positive predictive value. This algorithm may facilitate screening for this potentially lethal, yet highly treatable, genetic heart disease.

BACKGROUND: Differentiation of wide QRS complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide QRS tachycardia (SWCT) through 12-lead electrocardiogram (ECG) interpretation is one of the most critical yet challenging tasks in clinical practice. Recent research highlights novel automated methods that successfully diagnose WCTs. We sought to directly compare the diagnostic efficacy of these novel automated WCT differentiation methods (i.e., WCT Formula, VT Prediction Model, and WCT Formula II) to traditional ECG interpretation approaches (i.e., Brugada and Vereckei aVR algorithms). METHODS: A collection of paired WCT and baselined ECGs were retrospectively analyzed. Next, an electrophysiologist blindly and prospectively applied the Brugada and Vereckei aVR algorithms. Separately, computerized measurements were used to apply the three automated WCT differentiation methods. The diagnostic performance of each method was then evaluated. RESULTS: 213 WCTs (111 VT and 102 SWCT) from 105 patients were analyzed. The WCT Formula demonstrated superior overall accuracy (84.7% vs. 64.8%), specificity (86.2% vs. 35.3%), positive predictive value (PPV) (85.7% vs. 60.7%), and positive (+) likelihood ratio (LR) (6.06 vs. 1.42) compared to Vereckei aVR algorithm. Similarly, the WCT Formula attained higher specificity (86.2% vs. 61.8%), PPV (85.7% vs. 71.9%), and (+) LR (6.06 vs. 2.36) than the Brugada algorithm. The WCT Formula II demonstrated superior overall accuracy (89.2% vs. 64.8%), specificity (84.3 % vs. 35.3%), PPV (86.7% vs. 60.7%), and positive (+) LR (5.97 vs.1.42) compared to Vereckei aVR algorithm as well as higher specificity (84.3 % vs. 61.8 %), PPV (86.7% vs. 71.9%), and (+) LR (5.97 vs. 2.36) than the Brugada algorithm. The VT prediction model yielded superior specificity (79.4% vs. 35.3%), PPV (79.8 vs. 60.7%) and (+) LR (3.63 vs.1.42) compared to the Vereckei aVR algorithm but did not demonstrate superior diagnostic performance to the Brugada algorithm. CONCLUSIONS: Novel automated WCT differentiation methods achieved similar or superior diagnostic performance metrics compared to traditional ECG interpretation approaches.

Introduction: Deep-learning artificial intelligence (AI) algorithms can identify hypertrophic cardiomyopathy (HCM) from the electrocardiogram (ECG). Preliminary data indicate that AI-ECG may also track treatment response in HCM patients (pts) receiving mavacamten (Mava). In this placebo-controlled analysis, we investigated the performance of AI-ECG in HCM detection and its changes during Mava treatment compared to placebo in the phase 3, EXPLORER-HCM trial. Methods: Pts with non-paced ECGs enrolled in EXPLORER-HCM, evaluating Mava over 30 weeks of treatment, were included. All pts had a 12-lead ECG pre-treatment, at each study visit, and 8 weeks after treatment end. Two independently developed AI-ECG convolutional neural network algorithms from the Mayo Clinic and the University of California, San Francisco (UCSF) were applied to all ECGs suitable for analysis. Serial AI-ECG-predicted scores, which correlate with HCM likelihood (range 0-1), were examined between Mava and placebo groups. Results: A total of 101 Mava and 110 placebo pts were included (mean age 58.6, female 40%). At baseline, the mean Mayo and UCSF AI-ECG scores for HCM were 0.82 and 0.65, with 94% and 88% of pts exceeding the respective diagnostic thresholds of 0.11 (Mayo) and 0.4 (UCSF). During 30 weeks of follow-up in the Mava group, the algorithms showed a mean AI-ECG score reduction of 0.32 (Mayo) and 0.17 (UCSF) in all pts between start and end of treatment. In comparison, AI-ECG scores decreased by 0.05 (Mayo) and increased by 0.02 (UCSF) in the placebo group (Mava vs placebo, t-test p-values <0.001 for both algorithms; Figure ) Conclusion: In the EXPLORER-HCM trial population, two independent deep-learning AI-ECG algorithms correctly identified most HCM pts and demonstrated significant reductions in predicted HCM scores after treatment with Mava when compared to placebo. This relative normalization of the ECG suggests disease-modifying properties of Mava, deserving further investigation.