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Background Left ventricular diastolic dysfunction (LVDD) predicts mortality in patients in cardiac intensive care units. An artificial intelligence enhanced ECG (AIECG) algorithm can predict LVDD and mortality in general populations but has not been examined in cardiac intensive care units. Methods This historical cohort study included consecutive adults admitted to Mayo Clinic cardiac intensive care unit from 2007 to 2018 with an admission AIECG. The AIECG assigned the LVDD grade (0–3). Medial mitral E/e' ratio >15 on transthoracic echocardiogram (TTE) defined elevated filling pressures. In‐hospital and 1‐year mortality was evaluated, before and after multivariable adjustment. Results We included 11 868 patients (median age 69.5 years, 37.7% female); 48% had heart failure and 44% had acute coronary syndromes. AIECG LVDD grade was 0 (normal), 33%; 1, 7%; 2, 39%; and 3, 21%. In‐hospital and 1‐year mortality increased in each higher AIECG LVDD grade. After adjustment, each higher AIECG LVDD grade was associated with higher in‐hospital (adjusted odds ratio [OR], 1.22 [95% CI, 1.13–1.32]) and 1‐year mortality (adjusted hazard ratio [HR], 1.23 [95% CI, 1.19–1.29]); this persisted after adjustment for TTE measurements. Patients with grade 2 or 3 LVDD by AIECG and medial mitral E/e' ratio >15 by TTE had the highest in‐hospital (adjusted OR, 2.54 [95% CI, 1.69–3.88]) and 1‐year (adjusted HR, 2.03 [95% CI, 1.65–2.48]) mortality, whereas patients meeting either of these criteria had similar, elevated mortality. Conclusions The AIECG LVDD grade was strongly associated with in‐hospital and 1‐year mortality in patients in cardiac intensive care units, even after adjusting for clinical variables and TTE measurements. Patients with concordant AIECG and TTE for elevated filling pressures were at highest risk.

Abstract Background and Aims Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronological age, termed electrocardiographic aging (ECG aging), is associated with atrial fibrillation (AF) risk. Methods An AI-ECG age prediction model was developed using a large-scale dataset (1 533 042 ECGs from 689 639 participants) and validated with six independent and multi-national datasets (737 133 ECGs from 330 794 participants). The AI-ECG age gap was calculated across two South Korean cohorts [mean (standard deviation) follow-up: 4.1 (4.3) years for 111 483 participants and 6.1 (3.8) years for 37 517 participants], one UK cohort [3.0 (1.6) years; 40 973 participants], and one US cohort [12.9 (8.6) years; 90 639 participants]. Participants were classified into two groups: normal group (age gap < 7 years) and ECG-aged group (age gap ≥ 7 years). The predictive capability of ECG aging for new- and early-onset AF risk was assessed. Results The mean AI-ECG ages were 51.9 (16.2), 47.4 (12.5), 68.4 (7.8), and 56.7 (14.6) years with age gaps of .0 (6.8), −.1 (6.0), 4.7 (8.7), and −1.4 (8.9) years in the two South Korean, UK, and US cohorts, respectively. In the ECG-aged group, increased risks of new-onset AF were observed with hazard ratios (95% confidence intervals) of 2.50 (2.24–2.78), 1.89 (1.46–2.43), 1.90 (1.55–2.33), and 1.76 (1.67–1.86) in the two South Korean, UK, and US cohorts, respectively. For early-onset AF, odds ratios were 2.89 (2.47–3.37), 1.94 (1.39–2.70), 1.58 (1.06–2.35), and 1.79 (1.62–1.97) in these cohorts compared with the normal group. Conclusions The AI-derived ECG aging was associated with the risk of new- and early-onset AF, suggesting its potential utility to identify individuals for AF prevention across diverse populations.

Background: Our team recently developed an Artificial Intelligence model that enables the identification of increased left ventricular filling pressures using a single-lead electrocardiogram (AI-ECG) with excellent performance (AUC 0.91). However, its ability to predict incident heart failure (HF) in the community is yet to be evaluated. Hypothesis: We tested the hypothesis that the output from the AI-ECG will predict the long-term incidence of heart failure (HF) and HF with preserved ejection fraction (HFpEF), and would refine the HF predictive capabilities of the AHA Predicting Risk of Cardiovascular Disease EVENTs (PREVENT TM ) Equations. Methods: Leveraging the resources of the Rochester Epidemiology Project (REP) we analyzed data from patients who sought primary care in Olmsted County, MN, between 1997-2003 with passive follow-up until April 2024. We included those with a digital ECG at baseline and excluded those who were part of the original algorithm development or history of heart failure. The diagnosis of heart failure was ascertained by using ICD codes (ICD9-428,ICD10-I.50) and a word search for the terms related to heart failure. HFpEF was ascertained using the Ejection Fraction (EF) data closest to the heart failure diagnosis date, using EF more than 40% as the criterion. We tested the association between AI-ECG predicted probabilities and new-onset HF and HFpEF using Cox proportional hazard models. These models were also adjusted for cardiovascular risk factors and were stratified to evaluate the effect of the AI-ECG output on PREVENT TM -predicted HF at 10 years. Results: We included 20,276 subjects, mean age 55±14.03, 52% women, 92% non-Hispanic white. After 18.2±8.5 years of follow-up, 4,008 (19.76%) were diagnosed with HF and 3,812 (18.8%) HFpEF. Risks of HF and HFpEF significantly increased in patients with increased left ventricular filling pressures by AI-ECG, independent of risk factors, all p for trend <0.001, see Fig A-B , respectively. AI-ECG enhanced the prediction capabilities of the PREVENT TM score across all risk groups, see Fig-C. Conclusions: AI-ECG-Derived increased left ventricular filling pressure is associated with new-onset HF and HFpEF in the community and was associated with incidental heart failure at any PREVENT TM risk category. Those results suggest a potential role of AI ECG in predicting heart failure.

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.

Abstract Background The determination of left ventricular diastolic dysfunction (LVDD) and filling pressure in patients with significant (&gt;moderate) mitral regurgitation (MR) poses a complex challenge. We recently validated an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to detect LVDD and estimate LV filling pressures. Methods This retrospective study sought to examine the risk of all-cause mortality across AI-ECG LVDD grades. Patients from the AI-ECG LVDD study test cohort who underwent transthoracic echocardiography confirming significant MR and an ECG within fourteen days of each other at Mayo Clinic between 09/2001 and 06/2022 were included. LVDD status was determined based on the index ECG, and patients were categorized into groups representing normal diastolic function (NDF) or LVDD grades 1 (G1), 2 (G2), or 3 (G3). Results Of 4,019 patients with significant MR (mean age 69.8, 49.0% women), 1,175 (29.2%), 1,881 (46.8%), and 963 (24.0%) were classified by AI-ECG as NDF/G1-LVDD, G2-LVDD, and G3-LVDD, respectively. The median mitral effective regurgitant orifice area was 26 mm² (interquartile range 20-36 mm²). Over a median follow-up of 3.5 years, 1,636 (40.7%) patients died. In multivariable survival analysis adjusted for multiple risk factors, a worse AI-ECG LVDD grade was independently associated with an increased death risk [G2DD: adjusted hazard ratio (aHR) 1.98 (95% confidence interval (95% CI) 1.63-2.40), G3DD: aHR 2.59 (95% CI 2.09-3.20)]. These findings were consistent when accounting for mitral valve intervention and among patients with &gt;moderate MR or after mitral valve intervention. Conclusion In patients with significant MR, the grading of LVDD by AI-ECG is independently associated with all-cause mortality.

Germline mutations modulate the risk of developing schizophrenia (SCZ). Much less is known about the role of mosaic somatic mutations in the context of SCZ. Deep (239×) whole-genome sequencing (WGS) of brain neurons from 61 SCZ cases and 25 controls postmortem identified mutations occurring during prenatal neurogenesis. SCZ cases showed increased somatic variants in open chromatin, with increased mosaic CpG transversions (CpG>GpG) and T>G mutations at transcription factor binding sites (TFBSs) overlapping open chromatin, a result not seen in controls. Some of these variants alter gene expression, including SCZ risk genes and genes involved in neurodevelopment. Although these mutational processes can reflect a difference in factors indirectly involved in disease, increased somatic mutations at developmental TFBSs could also potentially contribute to SCZ.

Abstract Background and Aims Incidence and types of secondary tricuspid regurgitation (TR) are not well defined in atrial fibrillation (AFib) and sinus rhythm (SR). Atrial secondary TR (A-STR) is associated with pre-existing AFib; however, close to 50% of patients with A-STR do not have AFib. The aim of this study was to assess incidence, types, and outcomes of ≥ moderate TR in AFib vs. SR. Methods Adults with and without new-onset AFib without structural heart disease or ≥ moderate TR at baseline were followed for the development of ≥ moderate TR. Tricuspid regurgitation types were pacemaker, left-sided valve disease, left ventricular (LV) dysfunction, pulmonary hypertension (PH), isolated ventricular, and A-STR. Results Among 1359 patients with AFib and 20 438 in SR, 109 and 378 patients developed ≥ moderate TR, respectively. The individual types of TR occurred more frequently in AFib related to the higher pacemaker implantation rates (1.12 vs. 0.19 per 100 person-years, P &lt; .001), larger right atrial size (median 78 vs. 53 mL, P &lt; .001), and higher pulmonary pressures (median 30 vs. 28 mmHg, P &lt; .001). The most common TR types irrespective of rhythm were LV dysfunction-TR and A-STR. Among patients in SR, those with A-STR were older, predominantly women with more diastolic abnormalities and higher pulmonary pressures. All types of secondary TR were associated with all-cause mortality, highest in PH-TR and LV dysfunction-TR. Conclusions New-onset AFib vs. SR conferred a higher risk of the individual TR types related to sequelae of AFib and higher pacemaker implantation rates, although the distribution of TR types was similar. Secondary TR was universally associated with increased mortality.

Abstract 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.

Background: Enlargement or dilation of the left ventricle (LV) is associated with increased cardiovascular morbidity and mortality, and is an established precursor of ventricular dysfunction and heart failure in almost all major cardiovascular conditions. Although there are existing artificial intelligence frameworks that make use of deep learning to detect LV enlargement, many entail LV segmentation, which can be error-prone especially for point-of-care ultrasound (POCUS) images. Goal: To develop a fully end-to-end deep learning framework that takes as input either transthoracic echocardiographic (TTE) or POCUS 2D image clips, and classifies directly (i.e., without first segmenting the LV) whether a patient has an enlarged LV. Methods: Using a sample of retrospective TTE data enriched for LV enlargement (out of 13,868 patients total, 3,351 had an enlarged LV defined using sex-specific guideline-directed criteria), we trained a classifier that takes as input apical 2-chamber and parasternal long axis videos, and provides as output a binary label denoting whether a patient has an enlarged LV. Then, we evaluated the trained model using a holdout testing dataset of retrospective TTE data (2,087 patients) as well as data from a prospective cohort of 428 patients from whom TTE and POCUS were collected simultaneously. Results: For the retrospective TTE testing dataset, the AUC was 0.851 (95% confidence interval (CI) of 0.830-871). For the prospective cohort, the model performed comparably for both TTE and POCUS video clips (TTE: AUC of 0.865, 95% CI of 0.823-0.906; POCUS: AUC of 0.840, 95% CI of 0.797-0.883). Conclusions: The current deep learning model performed comparably well for both TTE and POCUS echocardiograms. We regard these results as a proof-of-concept of the potential utility of this model in helping point-of-care clinicians make decisions regarding whether patients should receive further evaluation for LV enlargement and associated conditions.

Background: Recently, our team developed an end-to-end artificial intelligence (AI) framework that can successfully estimate left ventricular ejection fraction (LVEF) from echocardiogram videos. However, this framework requires specific views (A2C and PLAX), and cannot estimate LVEF for studies from which these views are missing. Goal: To develop a deep learning model to estimate LVEF from any type and number of echocardiogram video(s). Methods: We built a deep learning model for B-mode echocardiogram videos of the left ventricle (A2C, A3C, A4C, or PLAX views), using retrospective transthoracic echocardiography (TTE) data. The model was evaluated with two test datasets: one with random valid (i.e., showing the left ventricle) views, and one with preselected views (both having 433 patients). Later, the model was tested with a prospective patient cohort that had both TTE and point-of-care ultrasound (POCUS) data collected simultaneously (393 patients). Results: On the retrospective TTE test set, we observed excellent performance (five random valid views per study: RMSE of 6.87% and Pearson correlation (PC) of 0.82 on LVEF estimation, and an AUC of 0.96 (95% CI: of 0.94-0.98) on low LVEF classification (<=40%); preselected views: RMSE of 6.75%, PC of 0.83, and an AUC of 0.96 (95% CI: 0.94-0.98)). For both TTE and POCUS data from the prospective cohort, we also observed strong performance (TTE: RMSE of 5.62%, PC of 0.87, and an AUC of 0.97; POCUS: RMSE of 6.12%, PC of 0.84, and an AUC of 0.96). For the prospective test sets as well as for the retrospective TTE test set with preselected views, results reflect the estimated LVEF average of A2C, A3C, A4C, and PLAX videos from each patient. Conclusions: For both TTE and POCUS data, the current model showed strong performance which was comparable to that of our previously established view-specific model. Alongside this comparable performance, the current model surpasses our previous model in terms of flexibility, as it isn't dependent on view classification and allows a broader selection in terms of the number and types of videos per study or patient. This innovative approach could prove particularly beneficial in point-of-care situations in which it may not be feasible to obtain high-quality, view-specific videos.