Pediatric sex estimation using AI-enabled ECG analysis: influence of pubertal development.

AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987-2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0-7 years), peripubertal (8-14 years), and postpubertal (15-18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model's discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy.

Ten-Year Trend of Oral Anticoagulation Use in Postoperative and Nonpostoperative Atrial Fibrillation in Routine Clinical Practice.

BACKGROUND: The study aimed to describe the patterns and trends of initiation, discontinuation, and adherence of oral anticoagulation (OAC) in patients with new-onset postoperative atrial fibrillation (POAF), and compare with patients newly diagnosed with non-POAF. METHODS AND RESULTS: This retrospective cohort study identified patients newly diagnosed with atrial fibrillation or flutter between 2012 and 2021 using administrative claims data from OptumLabs Data Warehouse. The POAF cohort included 118 366 patients newly diagnosed with atrial fibrillation or flutter within 30 days after surgery. The non-POAF cohort included the remaining 315 832 patients who were newly diagnosed with atrial fibrillation or flutter but not within 30 days after a surgery. OAC initiation increased from 28.9% to 44.0% from 2012 to 2021 in POAF, and 37.8% to 59.9% in non-POAF; 12-month medication adherence increased from 47.0% to 61.8% in POAF, and 59.7% to 70.4% in non-POAF. The median time to OAC discontinuation was 177 days for POAF, and 242 days for non-POAF. Patients who saw a cardiologist within 90 days of the first atrial fibrillation or flutter diagnosis, regardless of POAF or non-POAF, were more likely to initiate OAC (odds ratio, 2.92 [95% CI, 2.87-2.98]; P <0.0001), adhere to OAC (odds ratio, 1.08 [95% CI, 1.04-1.13]; P <0.0001), and less likely to discontinue (odds ratio, 0.83 [95% CI, 0.82-0.85]; P <0.0001) than patients who saw a surgeon or other specialties. CONCLUSIONS: The use of and adherence to OAC were higher in non-POAF patients than in POAF patients, but they increased over time in both groups. Patients managed by cardiologists were more likely to use and adhere to OAC, regardless of POAF or non-POAF.

Efficacy of 785-nm Picosecond Titanium Sapphire Laser for Treatment of Brown Nevus of Ota Lesions in FST II-V: A Retrospective Analysis.

BACKGROUND: Nevus of Ota (NOTA) is a dermal melanocytosis acquired in early childhood or pregnancy. Given their color variability, NOTA often require a combination of wavelengths for successful treatment. Quality-switched lasers have consistently shown efficacy in targeting dermal pigment, while picosecond lasers (PSLs) are an emerging technology for pigmentary disorders. OBJECTIVE: To further elucidate its efficacy, the authors conducted a retrospective review of 17 patients with NOTA treated with a 785-nm PSL for brown NOTA lesions between 2021 and 2023. MATERIALS AND METHODS: The primary end point analyzed clinical improvement based on before and after photography reviewed by 3 board-certified dermatologists using a five-point visual analog scale. RESULTS: Seventeen patients of Fitzpatrick skin types (FSTs) II to V, ranging from ages 14 to 38 years, were included in this study. Patients were treated for an average of 3.2 sessions in 2 to 3-month intervals. Visual analog scale scores demonstrated a mean clearance of 51% to 75%. No pigmentary alterations were noted. CONCLUSION: Because NOTA is common in higher FSTs, the authors believe that the 785-nm PSL is an excellent treatment option for brown NOTA in these skin types. This study highlights the need for further investigation to determine optimal treatment parameters for the color-based laser treatment approach for NOTA.

Validation of Non-invasive Detection of Hyperkalemia by Artificial Intelligence Enhanced Electrocardiography in High Acuity Settings.

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-August 2021) and a mixed intensive care unit (ICU) cohort (August 2017-February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within four hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads I and II 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-ECG to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of, 80%, 80%, 3%, 99.8% and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 0.83, 86%, 60%, 15%, 98% and 2.2, respectively, in the ED cohort. The ICU cohort (N=2,636) 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, positive predictive value, negative predictive value and positive likelihood ratio of 82%, 82%, 14%, 99% and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 0.85, 88%, 67%, 29%, 97% and 2.7, respectively in the ICU cohort. CONCLUSION: The AI-ECG algorithm demonstrated a high negative predictive value, suggesting that it is useful for ruling out hyperkalemia, but a low positive predictive value, suggesting that it is insufficient for treating hyperkalemia.

An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension.

BACKGROUND: Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead electrocardiogram (ECG). METHODS: The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage-time data, with patients classified as "PH-likely" or "PH-unlikely" (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%), and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). Performance was also tested on ECGs taken 6-18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites. RESULTS: Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test set at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test set. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis. CONCLUSION: The PH-EDA can detect PH at diagnosis and 6-18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.

Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators.

BACKGROUND: Despite effectiveness of the implantable cardioverter-defibrillator (ICD) in saving patients with life-threatening ventricular arrhythmias (VAs), the temporal occurrence of VA after ICD implantation is unpredictable. OBJECTIVE: The study aimed to apply machine learning (ML) to intracardiac electrograms (IEGMs) recorded by ICDs as a unique biomarker for predicting impending VAs. METHODS: The study included 13,516 patients who received Biotronik ICDs and enrolled in the CERTITUDE registry between January 1, 2010, and December 31, 2020. Database extraction included IEGMs from standard quarterly transmissions and VA event episodes. The processed IEGM data were pulled from device transmissions stored in a centralized Home Monitoring Service Center and reformatted into an analyzable format. Long-range (baseline or first scheduled remote recording), mid-range (scheduled remote recording every 90 days), or short-range predictions (IEGM within 5 seconds before the VA onset) were used to determine whether ML-processed IEGMs predicted impending VA events. Convolutional neural network classifiers using ResNet architecture were employed. RESULTS: Of 13,516 patients (male, 72%; age, 67.5 ± 11.9 years), 301,647 IEGM recordings were collected; 27,845 episodes of sustained ventricular tachycardia or ventricular fibrillation were observed in 4467 patients (33.0%). Neural networks based on convolutional neural networks using ResNet-like architectures on far-field IEGMs yielded an area under the curve of 0.83 with a 95% confidence interval of 0.79-0.87 in the short term, whereas the long-range and mid-range analyses had minimal predictive value for VA events. CONCLUSION: In this study, applying ML to ICD-acquired IEGMs predicted impending ventricular tachycardia or ventricular fibrillation events seconds before they occurred, whereas midterm to long-term predictions were not successful. This could have important implications for future device therapies.

Histological analysis of laser-enabled tissue coring with a novel 2910 nm erbium-doped fluoride glass fiber laser.

OBJECTIVES: There remains an unmet need for a laser-enabled tissue coring device that can effectively improve face and neck skin laxity and rhytides. We investigate a novel 2910 nm erbium-doped fluoride glass fiber laser (2910 nm fiber laser) (UltraClear; Acclaro Medical) for laser-coring of submental tissue. METHODS: Five subjects, Glogau scale III-IV, were treated with a single pulse of the laser-coring mode of the 2910 nm fiber laser in the submentum. A 4 mm punch biopsy was immediately performed. Biopsy specimens were sectioned and stained with hematoxylin and eosin and placed on glass slides. All sections were reviewed, and sections containing the center of the transected core were analyzed for depth and diameter of the ablative microchannel and width of the surrounding zone of coagulation. RESULTS: A total of 15 intact micro-cores were analyzed. Histological analysis revealed an average ± standard deviation microchannel diameter of 242.5 ± 65.2 µm, an average ablative depth of 980 ± 318.8 µm, and an average zone of coagulation of 104 ± 32 µm. CONCLUSIONS: Laser-enabled tissue coring with a novel 2910 nm fiber laser can safely achieve a wider microchannel diameter with ablative depth extending to the mid and deep dermis, which has the potential for collagen contraction and tissue tightening. Laser-coring to this ablation diameter and depth and with the surrounding zone of coagulation was found to be safe without adverse effects of post-inflammatory erythema or scarring in our study.

Cardiac Resynchronization Therapy for Patients With Mild to Moderately Reduced Ejection Fraction and Left Bundle Branch Block.

BACKGROUND: It is unknown whether cardiac resynchronization therapy (CRT) would improve or halt the progression of heart failure (HF) in patients with mild to moderately reduced ejection fraction (HFmmrEF) and left bundle branch block (LBBB). OBJECTIVE: This study aimed to investigate the outcomes of CRT in patients with HFmmrEF and left ventricular conduction delay. METHODS: A prospective, randomized clinical trial sponsored by the National Heart, Lung, and Blood Institute included 76 patients who met the study inclusion criteria (left ventricular ejection fraction [LVEF] of 36%-50% and LBBB). Patients received CRT-pacemaker and were randomized to CRT-OFF (right ventricular pacing 40 beats/min) or CRT-ON (biventricular pacing 60-150 beats/min). At a 6-month follow-up, pacing programming was changed to the opposite settings. New York Heart Association class, N-terminal pro-brain natriuretic peptide levels, and echocardiographic variables were collected at baseline, 6 months, and 12 months. The primary study end point was the left ventricular end-systolic volume (LVESV) change from baseline, and the primary randomized comparison was the comparison of 6-month to 12-month changes between randomized groups. RESULTS: The mean age of the patients was 68.4 ± 9.8 years (male, 71%). Baseline characteristics were similar between the 2 randomized groups (all P > .05). In patients randomized to CRT-OFF first, then CRT-ON, LVESV was reduced from baseline only after CRT-ON (baseline, 116.1 ± 36.5 mL; CRT-ON, 87.6 ± 26.0 mL; P < .0001). The randomized analysis of LVEF showed a significantly better change from 6 to 12 months in the OFF-ON group (P = .003). LVEF was improved by CRT (baseline, 41.3% ±.7%; CRT-ON, 46.0% ± 8.0%; P = .002). In patients randomized to CRT-ON first, then CRT-OFF, LVESV was reduced after both CRT-ON and CRT-OFF (baseline, 109.8 ± 23.5 mL; CRT-ON, 91.7 ± 30.5 mL [P < .0001]; CRT-OFF, 99.3 ± 28.9 mL [P = .012]). However, the LVESV reduction effect became smaller between CRT-ON and CRT-OFF (P = .027). LVEF improved after both CRT-ON and CRT-OFF (baseline, 42.7% ± 4.3%; CRT-ON, 48.5% ± 8.6% [P < .001]; CRT-OFF, 45.9% ± 7.7% [P = .025]). CONCLUSION: CRT for patients with HFmmrEF significantly improves LVEF and ventricular remodeling after 6 months of CRT. The study provides novel evidence that early CRT benefits patients with HFmmrEF with LBBB.

A Modular Communicative Leadless Pacing-Defibrillator System.

BACKGROUND: The subcutaneous implantable cardioverter-defibrillator (ICD) is associated with fewer lead-related complications than a transvenous ICD; however, the subcutaneous ICD cannot provide bradycardia and antitachycardia pacing. Whether a modular pacing-defibrillator system comprising a leadless pacemaker in wireless communication with a subcutaneous ICD to provide antitachycardia and bradycardia pacing is safe remains unknown. METHODS: We conducted a multinational, single-group study that enrolled patients at risk for sudden death from ventricular arrhythmias and followed them for 6 months after implantation of a modular pacemaker-defibrillator system. The safety end point was freedom from leadless pacemaker-related major complications, evaluated against a performance goal of 86%. The two primary performance end points were successful communication between the pacemaker and the ICD (performance goal, 88%) and a pacing threshold of up to 2.0 V at a 0.4-msec pulse width (performance goal, 80%). RESULTS: We enrolled 293 patients, 162 of whom were in the 6-month end-point cohort and 151 of whom completed the 6-month follow-up period. The mean age of the patients was 60 years, 16.7% were women, and the mean (±SD) left ventricular ejection fraction was 33.1±12.6%. The percentage of patients who were free from leadless pacemaker-related major complications was 97.5%, which exceeded the prespecified performance goal. Wireless-device communication was successful in 98.8% of communication tests, which exceeded the prespecified goal. Of 151 patients, 147 (97.4%) had pacing thresholds of 2.0 V or less, which exceeded the prespecified goal. The percentage of episodes of arrhythmia that were successfully terminated by antitachycardia pacing was 61.3%, and there were no episodes for which antitachycardia pacing was not delivered owing to communication failure. Of 162 patients, 8 died (4.9%); none of the deaths were deemed to be related to arrhythmias or the implantation procedure. CONCLUSIONS: The leadless pacemaker in wireless communication with a subcutaneous ICD exceeded performance goals for freedom from major complications related to the leadless pacemaker, for communication between the leadless pacemaker and subcutaneous ICD, and for the percentage of patients with a pacing threshold up to 2.0 V at a 0.4-msec pulse width at 6 months. (Funded by Boston Scientific; MODULAR ATP ClinicalTrials.gov NCT04798768.).

Atrial Fibrillation Substrate and Catheter Ablation Outcomes in MYBPC3- and MYH7-Mediated Hypertrophic Cardiomyopathy.

BACKGROUND: The effects of disease-causing MYBPC3 or MYH7 genetic variants on atrial myopathy, atrial fibrillation (AF) clinical course, and catheter ablation efficacy remain unclear. OBJECTIVES: The aim of this study was to characterize the atrial substrate of patients with MYBPC3- or MYH7-mediated hypertrophic cardiomyopathy (HCM) and its impact on catheter ablation outcomes. METHODS: A retrospective single-center study of patients with HCM who underwent genetic testing and catheter ablation for AF was performed. Patients with MYBPC3- or MYH7-mediated HCM formed the gene-positive cohort; those without disease-causative genetic variants formed the control cohort. High-density electroanatomical mapping was performed using a 3-dimensional mapping system, followed by radiofrequency ablation. RESULTS: Twelve patients were included in the gene-positive cohort (mean age 55.6 ± 9.9 years, 83% men, 50% MYBPC3, 50% MYH7, mean ejection fraction 59.3% ± 13.7%, mean left atrial [LA] volume index 51.7 ± 13.1 mL/m2, mean LA pressure 20.2 ± 5.4 mm Hg) and 15 patients in the control arm (mean age 61.5 ± 12.6 years, 60% men, mean ejection fraction 64.9% ± 5.1%, mean LA volume index 54.1 ± 12.8 mL/m2, mean LA pressure 19.6 ± 5.41 mm Hg). Electroanatomical mapping demonstrated normal voltage in 87.7% ± 5.03% of the LA in the gene-positive cohort and 94.3% ± 3.58% of the LA in the control cohort (P < 0.001). Of the abnormal regions, intermediate scar (0.1-0.5 mV) accounted for 6.33% ± 1.97% in the gene-positive cohort and 3.07% ± 2.46% in the control cohort (P < 0.01). Dense scar (<0.1 mV) accounted for 5.93% ± 3.20% in the gene-positive cohort and 2.61% ± 2.19% in the control cohort (P < 0.01). Freedom from AF at 12 months was similar between the gene-positive (75%) and control (73%) cohorts (P = 0.92), though a greater number of procedures were required in the gene-positive cohort. CONCLUSIONS: Patients with MYBPC3- or MYH7-mediated HCM undergoing AF ablation have appreciably more low-amplitude LA signals, suggestive of fibrosis. However, catheter ablation remains an effective rhythm-control strategy.

Artificial Intelligence Electrocardiography to Predict Atrial Fibrillation in Patients With Chronic Lymphocytic Leukemia.

Background: The use of an artificial intelligence electrocardiography (AI-ECG) algorithm has demonstrated its reliability in predicting the risk of atrial fibrillation (AF) within the general population. Objectives: This study aimed to determine the effectiveness of the AI-ECG score in identifying patients with chronic lymphocytic leukemia (CLL) who are at high risk of developing AF. Methods: We estimated the probability of AF based on AI-ECG among patients with CLL extracted from the Mayo Clinic CLL database. Additionally, we computed the Mayo Clinic CLL AF risk score and determined its ability to predict AF. Results: Among 754 newly diagnosed patients with CLL, 71.4% were male (median age = 69 years). The median baseline AI-ECG score was 0.02 (range = 0-0.93), with a value ≥0.1 indicating high risk. Over a median follow-up of 5.8 years, the estimated 10-year cumulative risk of AF was 26.1%. Patients with an AI-ECG score of ≥0.1 had a significantly higher risk of AF (HR: 3.9; 95% CI: 2.6-5.7; P < 0.001). This heightened risk remained significant (HR: 2.5; 95% CI: 1.6-3.9; P < 0.001) even after adjusting for the Mayo CLL AF risk score, heart failure, chronic kidney disease, and CLL therapy. In a second cohort of CLL patients treated with a Bruton tyrosine kinase inhibitor (n = 220), a pretreatment AI-ECG score ≥0.1 showed a nonsignificant increase in the risk of AF (HR: 1.7; 95% CI: 0.8-3.6; P = 0.19). Conclusions: An AI-ECG algorithm, in conjunction with the Mayo CLL AF risk score, can predict the risk of AF in patients with newly diagnosed CLL. Additional studies are needed to determine the role of AI-ECG in predicting AF risk in CLL patients treated with a Bruton tyrosine kinase inhibitor.

Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.

Aims: Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results: We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion: The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.

Artificial intelligence-based classification of echocardiographic views.

Aims: Augmenting echocardiography with artificial intelligence would allow for automated assessment of routine parameters and identification of disease patterns not easily recognized otherwise. View classification is an essential first step before deep learning can be applied to the echocardiogram. Methods and results: We trained two- and three-dimensional convolutional neural networks (CNNs) using transthoracic echocardiographic (TTE) studies obtained from 909 patients to classify nine view categories (10 269 videos). Transthoracic echocardiographic studies from 229 patients were used in internal validation (2582 videos). Convolutional neural networks were tested on 100 patients with comprehensive TTE studies (where the two examples chosen by CNNs as most likely to represent a view were evaluated) and 408 patients with five view categories obtained via point-of-care ultrasound (POCUS). The overall accuracy of the two-dimensional CNN was 96.8%, and the averaged area under the curve (AUC) was 0.997 on the comprehensive TTE testing set; these numbers were 98.4% and 0.998, respectively, on the POCUS set. For the three-dimensional CNN, the accuracy and AUC were 96.3% and 0.998 for full TTE studies and 95.0% and 0.996 on POCUS videos, respectively. The positive predictive value, which defined correctly identified predicted views, was higher with two-dimensional rather than three-dimensional networks, exceeding 93% in apical, short-axis aortic valve, and parasternal long-axis left ventricle views. Conclusion: An automated view classifier utilizing CNNs was able to classify cardiac views obtained using TTE and POCUS with high accuracy. The view classifier will facilitate the application of deep learning to echocardiography.

Successful prediction of left bundle branch block-induced cardiomyopathy and treatment effect by artificial intelligence-enabled electrocardiogram.

BACKGROUND: Left bundle branch block (LBBB) induced cardiomyopathy is an increasingly recognized disease entity.  However, no clinical testing has been shown to be able to predict such an occurrence. CASE REPORT: A 70-year-old male with a prior history of LBBB with preserved ejection fraction (EF) and no other known cardiovascular conditions presented with presyncope, high-grade AV block, and heart failure with reduced EF (36%). His coronary angiogram was negative for any obstructive disease. No other known etiologies for cardiomyopathy were identified. Artificial intelligence-enabled ECGs performed 6 years prior to clinical presentation consistently predicted a high probability (up to 91%) of low EF. The patient successfully underwent left bundle branch area (LBBA) pacing with correction of the underlying LBBB. Subsequent AI ECGs showed a large drop in the probability of low EF immediately after LBBA pacing to 47% and then to 3% 2 months post procedure. His heart failure symptoms markedly improved and EF normalized to 54% at the same time. CONCLUSIONS: Artificial intelligence-enabled ECGS may help identify patients who are at risk of developing LBBB-induced cardiomyopathy and predict the response to LBBA pacing.

Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series.

Biological age may be a more valuable predictor of morbidity and mortality than a person's chronological age. Mathematical models have been used for decades to predict biological age, but recent developments in artificial intelligence (AI) have led to new capabilities in age estimation. Using deep learning methods to train AI models on hundreds of thousands of electrocardiograms (ECGs) to predict age results in a good, but imperfect, age prediction. The error predicting age using ECG, or the difference between AI-ECG-derived age and chronological age (delta age), may be a surrogate measurement of biological age, as the delta age relates to survival, even after adjusting for chronological age and other covariates associated with total and cardiovascular mortality. The relative affordability, noninvasiveness, and ubiquity of ECGs, combined with ease of access and potential to be integrated with smartphone or wearable technology, presents a potential paradigm shift in assessment of biological age.

Trends in the 30-year span of noninfectious cardiovascular implantable electronic device complications in Olmsted County.

Background: Cardiac implantable electronic devices (CIEDs), such as permanent pacemakers, implantable cardioverter-defibrillators, and cardiac resynchronization therapy devices, alleviate morbidity and mortality in various diseases. There is a paucity of real-world data on CIED complications and trends. Objectives: We sought to describe trends in noninfectious CIED complications over the past 3 decades in Olmsted County. Methods: The Rochester Epidemiology Project is a medical records linkage system comprising records of over 500,000 residents of Olmsted County from 1966 to present. CIED implantations between 1988 and 2018 were determined. Trends in noninfectious complications within 30 days of implantation were analyzed. Results: A total of 157 (6.2%) of 2536 patients who received CIED experienced device complications. A total of 2.7% of the implants had major complications requiring intervention. Lead dislodgement was the most common (2.8%), followed by hematoma (1.7%). Complications went up from 1988 to 2005, and then showed a downtrend until 2018, driven by a decline in hematomas in the last decade (P < .01). Those with complications were more likely to have prosthetic valves. Obesity appeared to have a protective effect in a multivariate regression model. The mean Charlson comorbidity index has trended up over the 30 years. Conclusion: Our study describes a real-world trend of CIED complications over 3 decades. Lead dislodgements and hematomas were the most common complications. Complications have declined over the last decade due to safer practices and a better understanding of anticoagulant management.

Left bundle branch pacing vs ventricular septal pacing for cardiac resynchronization therapy.

Background: The outcomes of left bundle branch pacing (LBBP) and left ventricular septal pacing (LVSP) in patients with heart failure remain to be learned. Objective: The objective of this study was to assess the echocardiographic and clinical outcomes of LBBP, LVSP, and deep septal pacing (DSP). Methods: This retrospective study included patients who met the criteria for cardiac resynchronization therapy (CRT) and underwent attempted LBBP in 5 Mayo centers. Clinical, electrocardiographic, and echocardiographic data were collected at baseline and follow-up. Results: A total of 91 consecutive patients were included in the study. A total of 52 patients had LBBP, 25 had LVSP, and 14 had DSP. The median follow-up duration was 307 (interquartile range 208, 508) days. There was significant left ventricular ejection fraction (LVEF) improvement in the LBBP and LVSP groups (from 35.9 ± 8.5% to 46.9 ± 10.0%, P < .001 in the LBBP group; from 33.1 ± 7.5% to 41.8 ± 10.8%, P < .001 in the LVSP group) but not in the DSP group. A unipolar paced right bundle branch block morphology during the procedure in lead V1 was associated with higher odds of CRT response. There was no significant difference in heart failure hospitalization and all-cause deaths between the LBBP and LVSP groups. The rate of heart failure hospitalization and all-cause deaths were increased in the DSP group compared with the LBBP group (hazard ratio 5.10, 95% confidence interval 1.14-22.78, P = .033; and hazard ratio 7.83, 95% confidence interval 1.38-44.32, P = .020, respectively). Conclusion: In patients undergoing CRT, LVSP had comparable CRT outcomes compared with LBBP.

Electrocardiography-based Artificial Intelligence Algorithms Aid in Prediction of Long-term Mortality After Kidney Transplantation.

BACKGROUND: Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. METHODS: We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms' outputs based on a single preoperative ECG were correlated with patient mortality data. RESULTS: Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00-9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality (P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). CONCLUSIONS: The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.