Friedman, Paul J.

Abstract Intro Energy harvesters that convert biomechanical energy into electrical power could be used for implantable devices, providing a near infinite energy solution. While various energy harvesters with piezoelectric or triboelectric elements have been designed and applied to the cardiac environment, none have been successfully integrated into leadless pacemakers due to their size constraints or insufficient energy generation. Purpose This study aimed to develop a proprietary inertial piezoelectric energy harvester (PEH) capable of generating the energy required for a self-sustaining leadless pacemaker from the beating of the heart. Method Human heart dynamics were tested using ultra-fast MRI scans of 20 patients, which were analyzed for displacement of the right ventricular (RV) apex, mid- to low septum, and the right atrial (RA) walls. The data was used to design the PEH, which was evaluated in thirteen (13) male swine. In addition, epicardial MEMS-based sensors recorded the acceleration, angular positions, and gravity in 3 directions. The sensors were placed at the apex of the RV, LV Apex, LV free wall, and the RA using adhesive means. The data collected from the animals was used to calculate the power produced by the PEH in the different regions of interests and to create a bench model to optimize the PEH design further. The PEH was then tested in-vivo in an ovine model. Results The heart contractility creates harmonic acceleration in the heart tissue, transmitting this vibration into the PEH, resulting in the harvester acceleration of 0.75 g (±0.25g). When the PEH is subject to vibrations, the PEH oscillates using kinetic forces exerted on the piezoelectric beam through a proof mass, transforming these mechanical forces into electric energy. The electrical energy produced using this PEH is 6µW (± 2µW), which is sufficient to power a leadless pacemaker, providing pacing therapy of 100% pacing, at 60 BPM, with heart impedance of 600 (±200), and a pacing voltage reaching 2V, with sufficient excess energy to maintain charge of a secondary battery used for communication and advanced pacemaker functions. This was then tested in a pre-clinical setup, using an ovine model (results to be presented in the future). Conclusion This work demonstrates that a proprietary inertial PEH can use the contraction energy of the heart to generate electrical energy. The design of the PEH was based on the analysis of human heart kinetics using MRI scans and in-vivo recordings from pigs’ hearts. This design of an effective energy harvester can produce enough energy to power a leadless pacemaker. The design can fit in a small capsule that can be used in a leadless pacemaker that will be implanted in the RV providing sensing and pacing as well as daily communication enabling advanced pacemaker functions. The PEH is currently being tested in an ovine model, demonstrating its safety and performance.

Abstract Background AI-enhanced electrocardiogram (AI-ECG) is a cost-effective tool for left ventricular dysfunction screening. However, its cost-effectiveness for other forms of structural heart disease (SHD) is unknown. While AI-ECG is inexpensive, the relatively low positive predictive value (PPV) of many models can lead to high costs from unnecessary follow-up tests like transthoracic echocardiography (TTE). Therefore, strategies to improve the yield of AI-ECG-based screening are needed. Purpose To evaluate the cost savings of a stepwise approach to SHD screening using AI-ECG followed by point-of-care ultrasound (POCUS) compared to AI-ECG alone. Methods 286 adult outpatients undergoing AI-ECG were randomly selected to undergo same-day POCUS. All participants were required to have a recent TTE, our gold standard for SHD diagnosis, for comparison purposes. We evaluated four SHDs: aortic stenosis, cardiac amyloidosis, hypertrophic cardiomyopathy, and left ventricular dysfunction (defined as left ventricular ejection fraction <50%). The costs of AI-ECG ($75/test) and TTE ($1,305/test) were sourced from Healthcare Bluebook, while the cost of POCUS ($100/test) was independently estimated. We compared the costs of screening with AI-ECG alone to stepwise screening with AI-ECG followed by POCUS. Costs were analyzed for two paradigms: screening for all forms of SHD in parallel and screening for each SHD individually. Results AI-ECG alone identified potential SHD in 125 out of 286 patients. When compared to TTE, only 39 out of 125 were true positives (31% PPV). Among the AI-ECG-positive patients, POCUS demonstrated findings of SHD in 52 out of 125 cases. When compared to TTE, this stepwise approach yielded 32 true positives and 20 false positives (62% PPV). In the parallel screening paradigm, the cost per patient diagnosed with SHD was $4,733 with AI-ECG alone, which decreased to $3,182 with stepwise screening (33% cost savings). For the individual SHD screening paradigm, cost reductions were as follows: aortic stenosis ($18,724 to $6,315, 66% savings), cardiac amyloidosis ($21,023 to $12,230, 42% savings), hypertrophic cardiomyopathy ($9,883 to $6,175, 38% savings), and left ventricular dysfunction ($4,019 to $3,582, 11% savings). Conclusions Stepwise SDH screening with AI-ECG followed by POCUS markedly reduces costs compared to AI-ECG alone. The greatest cost savings for individual SHD screening were observed for aortic stenosis and cardiac amyloidosis, conditions where AI-ECG models have lower PPVs. Parallel screening for multiple SHDs is also less costly than screening for any individual SHD alone. Therefore, a parallel screening approach may maximize SHD detection while minimizing screening costs. Stepwise Screening Approach for SHD SDH Screening Costs

Background: Many medications, electrolyte perturbations, and several diseases can prolong the QTc beyond its 99 th percentile value resulting in acquired QT prolongation (AQTP). In contrast, approximately 1 in 2,000 people have congenital long QT syndrome (LQTS) hallmarked by pathological QT prolongation. Recently, we developed an AI-ECG model to distinguish these two groups, however this model does not elucidate which ECG segments distinguish LQTS from AQTP. Methods: Using the cohort from our previous study, all patients with LQTS evaluated in the Mayo Clinic Genetic Heart Rhythm Clinic and controls were included. Next, every patient/control with ≥1 ECG above age- and sex-specific 99 th percentile for QTc was selected and matched at 1:100 ratio. Subsequently, 5 ML algorithms (logistic regression, Naïve Bayes, random forest, Gradient boosting and XGBoost) were trained using 52 features representing segments of the 12-lead ECG (e.g. wave peak times, amplitudes, areas under wave, duration; 52x12 features). Training and testing sets were spilt at 80:20 ratio and 5-fold cross validation was performed to prevent overfitting. Following analysis, the top 100 features were re-analyzed to refine results. Results: Among >1,600 patients with LQTS, 690 had ≥ 1 ECG with a QTc above the established threshold compared to 28,186 controls. Following age- and sex-matching and splitting, 22,410 (training), and 5,776 (testing) ECGs were used. Of the 5 ML algorithms, XGBoost demonstrated best performance (AUC 0.913, accuracy 0.836, sensitivity 0.857, specificity 0.835) to distinguish LQTS from AQTP. Following filtering for the top 100 features, performance of the algorithm remained high with AUC of 0.912 (accuracy 0.84, sensitivity 0.841, specificity 0.84). The features with the greatest impact on classification output included time from T wave onset to T wave peak (leads V1, V4, V4), QRS interval duration (leads V1, V3, V4, aVR), and amplitude at end of ST segment (aVF). Conclusions: Distinguishing monogenetically driven LQTS from multi-factorial AQTP is critical in clinical practice for proper management, mitigation of offending QT-prolonging factors, and treatment. Building on our previously developed AI-ECG-LQTS ‘mutation detector’, this model identifies some of the elements within the ‘black box’ that distinguish an ECG and QTc stemming from a patient with LQTS compared to a similar prolonged QTc value that is arising due to non-genetic, acquired QT-prolonging factors.

Background: Left ventricular diastolic dysfunction (LVDD) predicts mortality in cardiac intensive care unit (CICU) patients. A novel artificial intelligence enhanced electrocardiogram (AIECG) algorithm can predict LVDD and mortality in general populations but has not been examined in the cardiac intensive care unit (CICU). We aim to assess if LVDD by AI-ECG is associated with in-hospital and one-year mortality in CICU patients. Methods: In this retrospective cohort study, we included consecutive unique adults admitted to the Mayo Clinic CICU from 2007 to 2018 with an admission AIECG, which AI assigned LVDD grade (0 to 3). Medial mitral E/e’ ratio >15 on transthoracic echocardiogram (TTE) defined elevated filling pressures. AIECG and TTE assessment of LVDD were used to assign patients to four groups based on TTE as gold standard: true and false negative, and true and false positive. In-hospital mortality was evaluated using multivariable logistic regression. One-year survival was evaluated using Kaplan-Meier curves and multivariable Cox proportional-hazards. Results: We included 11,868 patients (median age 69.5 years, 37.7% females); 48% had heart failure and 44% had acute coronary syndromes. AIECG LVDD grades were: grade 0 (normal), 33%; grade 1, 7%; grade 2, 39%; grade 3, 21%. In-hospital (adjusted OR) and one-year (adjusted HR) mortality increased in each higher AIECG LVDD grade (Figure A/B), before and after adjustment including TTE measurements. Patients with grade 2-3 LVDD by AIECG and medial mitral E/e’ ratio >15 by TTE (true positive) had the highest in-hospital (adjusted OR 2.5 [1.7-3.9]) and one-year (adjusted HR 1.9 [1.5-2.5]) mortality (Figure C/D), and mortality was elevated similarly in patients with either grade 2-3 LVDD by AIECG (false positive) or medial mitral E/e’ ratio >15 by TTE (false negative). Conclusions: The AIECG LVDD grade was strongly associated with in-hospital and one-year mortality in CICU patients, even after adjusting for clinical variables and TTE measurements. Patients with concordant AIECG and TTE for elevated filling pressures (true positive) were at highest risk.

Introduction: Graves’ disease (GD) is known to cause menstrual changes and has been linked to infertility in women. This can be due to the increased production of androstenedione and testosterone induced by the excess thyroid hormone. However, screening for sex hormone levels in all women diagnosed with GD to identify patients more likely to have infertility is not cost-effective and not clinically indicated. AI-augmented ECG can identify the signature of female with an AUC of 0.97. In this model, AI- ECG gives a probability number between 0-100% of being female, with higher numbers indicating high probability of being female. Hypothesis: We hypothesized that this inexpensive AI ECG tool is reflective of hormonal levels and the resulting changes in menstrual cycle and fertility associated with GD in women. Methods: Patients diagnosed with GD (2009-2019) at our institution were included. Three intervals were identified: 1) Before GD: >2 months before GD diagnosis 2) During GD: within 2 months before GD and before attaining euthyroidism 3) After GD: after attaining euthyroidism. The matched-pairs t-test was used to compare probability of being a female; patients acted as their own controls. A subset analysis was done on females <45 years at GD diagnosis to study the association between female probability during GD and menstrual changes (defined as shorter, lighter, or newly irregular menstrual cycles). Results: 154 females with ECGs both before and during GD were identified. AI ECG female probability decreased with GD diagnosis (85% vs. 77%, P<.001). On the other hand, 137 females with GD had ECGs during and after GD. Female probability increased after attaining euthyroidism (76% vs 83%, P=.005). Moreover, 72 females <45 years were identified; 30 (42%) developed menstrual changes and had significantly lower AI-ECG female probability [77 (58-94)% vs 98 (92-100)%, P<.001] Conclusions: AI-enabled ECG signature of female gender in women was altered by GD and was associated with menstrual changes, likely reflecting changes in sex hormone levels. Future studies to confirm its role in screening for infertility in women with GD are needed.

Background: Long QT syndrome (LQTS) is a potentially lethal yet highly treatable channelopathy underscored by marked heterogeneity that necessitates evidence-based and individualized therapeutic approaches. One such therapy is the implantable cardioverter defibrillator (ICD). Here, we sought to determine the percentage of patients (pts) with LQTS that received an ICD in our LQTS clinic and its short term outcomes. Methods: An IRB-approved, retrospective analysis of the electronic medical records of 167 LQT1, 113 LQT2, and 29 LQT3 pts evaluated in our LQTS Clinic with subset analysis of those implanted with an ICD in our institution from January 2000 to June 2007. Results: Forty-eight pts (15% of total, 33 females, 14 LQT1, 20 LQT2, and 14 LQT3) received an ICD. The average age at implant was 30 ± 12 years (range 8 – 52 years). Nearly two-thirds received an ICD as primary prevention. The QTc exceeded 500 ms in 18 (37.5%). With an average follow-up of 42 ± 32 months, 9 (19%) have had an appropriate, VF-terminating therapy including 3 with LQT1 and 6 with LQT2. Notably, 5 of the 6 LQT2 females have already received an appropriate ICD therapy whereas none of the LQT3 pts have received one. Secondary prevention indications (p = 0.006), LQT2 genotype (p = 0.03), QTc > 500 ms (p = 0.001), and surprisingly a negative family history (p = 0.0005) predicted those ICD pts most likely to receive an appropriate therapy. Importantly, none of the non-ICD treated pts (> 250) have died a LQT-related death during the same follow-up. Conversely, 9 pts have experienced an inappropriate shock including 6 with LQT3 and males were more likely to receive inappropriate therapies (p = 0.002). Conclusions: The majority of pts with LQTS can be treated effectively without an ICD. In this study, potentially life-saving therapies have been rendered so far at a 5– 6%/year rate. Inappropriate shocks occurred with similar frequency. Secondary prevention, genotype, and QTc predicted those ICD recipients most likely to receive appropriate therapy. Although LQT3 status is commonly associated with an ICD recommendation, so far none of the LQT3 positive ICD recipients have needed their device. Instead, the greatest “save” rate has occurred among the LQT2 positive females deemed at high risk.