López-Jiménez, Francisco

Abstract Aims Social isolation (SI) is associated with a higher risk of cardiovascular disease (CVD). One mechanism linking SI and CVD is accelerated biological ageing, which can be assessed using artificial intelligence-enabled electrocardiography (AI-ECG). We investigated whether longitudinal changes in SI status are associated with changes in biological ageing patterns. Methods and results A retrospective longitudinal cohort study including 17 663 individuals without significant CVD who completed ≥2 Berkman and Syme Social Network Index and had paired ECGs within 1 year of each questionnaire. Longitudinal SI status was defined by comparing baseline and follow-up SI status. Ageing trends were assessed using Δage-gap, defined as the change in the gap between AI-ECG-predicted age and chronological age from baseline to follow-up. Linear regression models predicting Δage-gap and survival analyses with Kaplan–Meier and multivariable Cox regression for all-cause mortality were performed. Persistently connected individuals were older [63.1 (52.1, 70.6) years] and had a higher prevalence of hyperlipidaemia and hypertension, yet demonstrated more favourable ageing trajectories. Similarly, those who became connected showed a non-significant attenuation of accelerated ageing. In multivariable linear regression, being persistently isolated [β = +0.412 years, P < 0.001] or becoming isolated [β = +0.438 years, P = 0.012] was associated with accelerated ageing compared to being persistently connected. In survival analyses, becoming isolated (aHR = 1.94, 95% CI = 1.53–2.46, P < 0.001) and being persistently isolated (aHR = 1.66, 95% CI = 1.40–1.96, P < 0.001) were associated with significantly higher all-cause mortality, independent of chronological age and comorbidities. Conclusion Persistent social connection is associated with slower biological ageing and lower mortality. The effects of SI on ageing and survival appear to be potentially reversible, calling for a prospective study to evaluate interventions aimed at improving both the social and biological burden of SI.

Background: Sleep regularity is emerging as an independent determinant of health. Multiple studies suggest a link between increased variability in sleep duration and timing and adverse cardiovascular, metabolic, and cognitive outcomes. There is, however, a lack of data on the implications of sleep regularity for aerobic capacity and physical endurance, particularly in patients with established cardiovascular disease. This study seeks to investigate the influence of irregularity in sleep duration on functional capacity in patients with coronary artery disease (CAD) undergoing cardiac rehabilitation (CR). Methods: We recruited a sample of patients with CAD who were referred to CR at Mayo Clinic Rochester. Participants completed a 6-minute walk test (6MWT) upon enrollment into CR. 6MWT variables included distance walked (meters), predicted distance walked (percentage) and estimated metabolic equivalents (METs). 24-hour objective sleep quality and quantity were assessed over a 7-day period by wrist actigraphy. Average standard deviation (SD) of sleep duration was used to define sleep regularity, with SD >60 minutes indicating irregular sleep. Chi-square test, independent samples t-test, and linear regression were used for statistical analysis. Results: Twenty-eight participants with valid actigraphy and 6MWT were included in this analysis (eight females; mean age 67.4 ± 11.6 years; body mass index 31.1 ± 5.8 kg/m 2 ). The average total sleep time was 7.1 ± 1.2 hours, and 10 participants (35.7%) exhibited sleep duration SD >60 minutes. Demographic, clinical and sleep characteristics were comparable between the regular and irregular sleep groups. Compared to patients with regular sleep, those with irregular sleep had poorer 6MWT, with shorter distance walked (406.2 ± 81.2 meters vs 338.8 ± 80.7 meters; p = 0.045), lower % of predicted distance walked (87.1 ± 18.7% vs 69.2 ± 11.4%; p = 0.011), and lower METS (2.99 ± 0.36 vs 2.63 ± 0.38; p = 0.022). Irregular sleep remained significantly associated with performance at the 6MWT after taking into account age and sex, with patients walking 14.4 meters fewer with every 10-minute increase in sleep variability (95% CI -28.1, -0.7; p 0.041). Conclusions: Increased day-to-day fluctuation in sleep duration is associated with reduced exercise capacity in patients with CAD undergoing CR. These findings add to the mounting body of evidence linking irregular sleep patterns to heightened cardiovascular risk, and support the integration of sleep monitoring in the CR practice to enhance risk stratification and outcome prediction. Funding Source: Sleep Number Corp. This abstract was presented at the American Physiology Summit 2026 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

Abstract Aims Heart failure is prevalent; however, there is no cost-effective screening option. Against formal echocardiography, we assessed the diagnostic performance of focused cardiac ultrasound (FoCUS), performed by novice users guided and interpreted by artificial intelligence (AI) for the assessment of left ventricular ejection fraction (LVEF). Methods and results We prospectively enrolled 496 adults referred for diagnostic echocardiography. Novice operators (without clinical or imaging experience) underwent a 4-h imaging workshop, then performed FoCUS using a point-of-care device (Philips Lumify) utilizing real-time AI-image guidance (UltraSight). Images were assessed by AI-image interpretation software (Mayo Clinic), and two expert blinded echocardiologists. Median age was 67, 39% female, and body mass index range 17–56 kg/m2. Forty-one subjects (8.3%) exhibited moderate or greater LV dilatation and 28 (5.6%) had LVEF <40%. The median scan time was 4 min (IQR 3–5). Adequate views were achieved in 95.0% and 97.4% of subjects for AI interpretation and expert analysis, respectively. A two-step diagnostic screening process for low EF in which only abnormal AI reads or uninterpretable scans were reviewed by experts (15.1%) had a sensitivity 96.2%, specificity 95.4%, PPV 54.3%, and NPV 99.8%. A subsequent prospective validation study of 344 subjects demonstrated similar results (sensitivity 100%, specificity 99.4%). Conclusion In this early feasibility investigation, AI-guidance technology embedded on a handheld device, enabled novice users without clinical or imaging experience to acquire sufficient quality images to accurately assess LVEF with minimal training. The technology evaluated in this study may hold promise for AI-guidance and interpretation to facilitate low-cost screening for cardiac dysfunction.

Abstract Aims Early detection of structural heart disease (SHD) improves patient outcomes. However, population-based screening is not recommended due to the lack of accurate and cost-effective tools. We evaluated the costs of artificial intelligence–enabled electrocardiogram (AI-ECG) alone vs. AI-ECG followed by handheld cardiac ultrasound (HCU) for SHD screening. Methods and results We performed a model-based cost analysis using data from 286 adult patients who underwent ECG and same-day HCU performed by a novice operator. Transthoracic echocardiogram (TTE) was the reference standard. We compared two screening strategies: (i) AI-ECG alone and (ii) a stepwise approach (AI-ECG followed by HCU). We assessed costs per diagnosis of aortic stenosis (AS), increased left ventricular wall thickness (ILVWT), and left ventricular systolic dysfunction (LVSD). Sensitivity analyses were conducted for varying disease prevalence. The stepwise approach decreased the cost per diagnosis of AS from $6386 (AI-ECG alone) to $2746 (57.0% savings), ILVWT from $4448 to $2895 (34.9% savings), and LVSD from $1469 to $1296 (11.8% savings). Overall, the cost per diagnosis for all SHDs combined decreased from $1940 to $1570 (19.1% savings). Sensitivity analysis demonstrated that cost savings were inversely proportional to disease prevalence. Nevertheless, stepwise screening remained cost-saving compared with AI-ECG alone until prevalence exceeded ∼55.9% for AS, 28.9% for ILVWT, 20.7% for LVSD, and 40.8% for all SHDs combined. Conclusion A stepwise screening strategy incorporating HCU after a positive AI-ECG reduces the immediate costs of SHD detection by minimizing unnecessary TTEs. This approach may enhance the feasibility of population-based SHD screening, particularly in lower-prevalence settings.

ABSTRACT Background Acute heart failure (AHF) exhibits marked heterogeneity in diastolic hemodynamics, yet comprehensive echocardiographic assessment of diastolic function (DF) and filling pressure (FP) is often infeasible. We evaluated whether artificial intelligence–enabled electrocardiography (AI-ECG) could provide scalable DF grading and FP estimation in hospitalized AHF patients. Methods We retrospectively studied adults hospitalized for AHF across Mayo Clinic sites (2013–2023) who received ≥1 dose of intravenous loop diuretic and had paired 12-lead ECG and TTE. The previously validated AI-ECG DF model was applied without retraining to generate four DF grades and a continuous FP probability. Clinical outcomes were all-cause mortality and heart failure rehospitalization. Associations with clinical severity markers and echocardiographic indices were examined. Kaplan–Meier survival analysis and adjusted multivariable Cox proportional hazards models were performed. Exploratory analyses examine the kinetics of change in FP probability and impact on mortality. Results Among 11,513 patients (median age 75 years, 39% female), AI-ECG DF grading was feasible in 100%, whereas echocardiographic DF was indeterminate in 44% of clinically eligible patients. In 2,582 patients with determinate echocardiographic DF, AI-ECG FP probability discriminated TTE Grade 2–3 dysfunction with AUC 0.85 (95% CI 0.83 – 0.86). Higher AI-ECG DF grades were associated with higher comorbidity burden, worse NYHA class, elevated NT-proBNP, higher MAGGIC scores, elevated PCWP, and more advanced structural remodeling. After multivariable adjustment, AI-ECG DF remained independently associated with mortality (hazard ratio [HR] 1.25, 95% CI 1.16–1.35 for Grade 2; HR 1.44, 95% CI 1.33–1.56 for Grade 3 versus Normal/Grade 1). Combining AI-ECG DF with MAGGIC scores yielded ordered risk gradients, with highest mortality in patients with both high MAGGIC and Grade 2–3 DF. Among patients with serial ECGs, improvement in FP probability was independently associated with lower mortality (HR 0.85, 95% CI 0.79–0.91), whereas worsening did not show a consistent adverse gradient beyond baseline DF. Conclusions In a large, geographically diverse AHF cohort, AI-ECG DF grading was universally feasible, correlated with established hemodynamic severity markers, and provided independent prognostic information beyond established risk factors, supporting its role as a pragmatic, scalable diastolic biomarker in AHF. CLINICAL PERSPECTIVE What Is New? In 11,513 hospitalized acute heart failure (HF) patients, artificial intelligence-enabled electrocardiography provided diastolic function grading in 100% of patients from a single 12-lead ECG without requiring additional clinical variables, compared with 56% feasibility for guideline-based echocardiography grading. AI-ECG diastolic function grades correlated with established marker of severity (NYHA functional class, NT-proBNP, MAGGIC risk scores, and pulmonary capillary wedge pressure) and remained independently associated with both mortality and HF rehospitalization after multivariable adjustment. Serial AI-ECG measurements identified post-discharge filling pressure trajectories, with improvement independently associated with 15% lower mortality, a first demonstration that longitudinal ECG assessment can track post-discharge hemodynamic recovery. What Are the Clinical Implications? AI-ECG transforms the universally obtained 12-lead ECG into an actionable hemodynamic biomarker that addresses the critical gap when echocardiographic diastolic function assessment is indeterminate or unavailable in acute HF patients. Despite markedly different hemodynamic severity and long-term outcomes across AI-ECG diastolic function grades, hospitalization length of stay did not differ, suggesting advanced diastolic dysfunction represents occult risk not easily recognized during routine acute care and highlighting the need for improved post-discharge risk stratification. The continuous filling pressure probability metric enables longitudinal monitoring of post-discharge hemodynamic status using serial routine ECGs, potentially identifying patients requiring intensified follow-up or specialist referral.

Abstract Objective To investigate the ability of artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output and delta age, defined as AI‐estimated age minus chronological age, to predict adverse vascular outcomes in patients with migraine. Background Migraine, especially migraine with aura, is associated with an increased risk of cardiovascular events. Using normal sinus rhythm ECG, our institution has developed AI‐ECG models that predict the probability of AF and estimate patients' age, and from that, delta age (estimated age minus actual age) has been shown to reflect cardiovascular health and physiologic aging. Methods We conducted a retrospective cohort study that included adult patients with migraine who had at least one digital, standard 12‐lead ECG from 2000 to 2020 at any Mayo Clinic site. The first normal sinus rhythm ECG was defined as the index ECG, and those with adverse vascular events or AF prior to, or within 3 days after, index ECG were excluded. AI‐ECG AF prediction model output and delta age were obtained from the index ECG. The primary outcome was a composite endpoint of adverse vascular events that included acute myocardial infarction, acute ischemic stroke, venous thromboembolism, and death. New‐onset AF was also recorded as a secondary outcome. Results A total of 29,928 patients were included. The mean age was 44.3 ± 14.5 years, and the median follow‐up time was 54 months. During the study period, 4662 (15.6%) patients had adverse vascular events, and 1384 (4.6%) patients developed new‐onset AF. A higher AF prediction model output was associated with a higher risk of adverse vascular outcomes (adjusted hazard ratio [aHR]:1.15 [95% confidence interval: 1.12 to 1.18] for every 10% increase in prediction model output), as was a larger delta age (aHR:1.16 [1.12 to 1.21] for every 10‐year increase in delta age). Moreover, AI‐ECG AF prediction model outputs of ≥1% (aHR: 1.45 [1.36 to 1.54]) and delta age ≥−1 years (aHR: 1.09 [1.02 to 1.16]) were found to be the optimal cutoffs to dichotomize patients into high‐risk and low‐risk groups for adverse vascular events. Higher AI‐ECG AF prediction model output at baseline also predicted a higher likelihood of having new‐onset AF during the study period (aHR: 1.31 [1.26 to 1.37]), and the development of new‐onset AF was associated with a higher risk of adverse vascular events (aHR: 2.43 [2.17 to 2.73]). Conclusions For patients with migraine, a higher AI‐ECG AF prediction model output and delta age are predictive of adverse vascular outcomes. A higher AF prediction model output is also predictive of future AF development. Furthermore, we identified the cutoffs for both AI‐ECG models that can distinguish patients between high‐ and low‐risk groups. Our results showed that AI‐ECG models could inform clinical practice and identify patients with migraine at risk for future adverse vascular events.

Abstract Aims Artificial intelligence electrocardiography (AI-ECG) algorithms are emerging tools for identifying individuals at risk of atrial fibrillation (AF). We evaluated the predictive performance of a validated AI-ECG algorithm for incident AF in UK Biobank participants with AF risk factors, irrespective of prevalent cardiovascular disease, and its incremental value when added to clinical predictors. Methods and results The AI-ECG tool was applied to sinus rhythm ECGs from UK-Biobank participants with risk factors for AF but no AF. Model performance was evaluated using time-dependent ROC-AUC and Harrell’s C-index. Multivariable Cox regression was used to identify clinical risk factors associated with incident AF and to quantify the contribution of AI-ECG. A total of 21 842 participants (56% male) were included. The median follow-up time was 3.7 years (IQR 0.5–5.4) The ECG-AI tool achieved a ROC-AUC of 0.73 (95% CI 0.68–0.78) at 1 and 0.69 (95% CI 0.66–0.72) at 3 years. A multivariable Cox regression model using clinical parameters achieved a ROC-AUC of 0.71 (95% CI 0.66–0.75) at 1 and 0.71 (95% CI 0.68–0.74) at 3 years. By adding ECG-AI to the clinical Cox regression model, the ROC-AUC increased to 0.75 (95%CI 0.71–0.80) at 1 and 0.74 (95% CI 0.71–0.77) at 3 years. AI-ECG showed a hazard ratio of 1.23 per decile increase (95% CI 1.18–1.27). Conclusion An AI-ECG algorithm improved the prediction of incident AF when added to a clinical parameter-based model over a median follow-up time of 3.7 years among individuals with comorbidities predisposing to AF who may benefit from targeted screening and preventive strategies.

Abstract Background Normal weight obesity (NWO) has been linked to metabolic syndrome (MetS), cardiovascular disease (CVD) and mortality, but has not been comprehensively studied in the Hispanic population. Purpose Our study aims to examine the CVD risk factors associated with NWO and to compare the development of CVD risk factors between NWO and normal weight non-obese (NWNO) Hispanic adults over 6 years. Methods This is a prospective study using data from the Hispanic Community Health Study (HCHS). Interviews and clinical assessments were collected from 2008–2011 (visit 1) to 2014–2017 (visit 2). NWO was defined as a body mass index (BMI) between 18.5–24.9 kg/m2 and a body fat percentage of ≥25 % in men and ≥35% in women, estimated using the Tanita scale. We identified 1,000 HCHS participants as having a normal weight at visit 1 and not missing data on covariates of interest including hypertension (HTN), hyperlipidemia (HLD), MetS, and insulin resistance (Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) ≥2.5). Participants were categorized into NWO (n=104) or NWNO (n=896) groups. Univariate logistic regression models were used to estimate the odds ratio (OR) and 95% confidence intervals (CI) of the association between health risk factors and NWO. Multivariate logistic regression models were used to estimate the odds of developing CVD risk factors over 6 years, using propensity score matching. Results The prevalence of NWO among our sample of Hispanic adults was 10.4% and the 6-year incidence of NWO at visit 2 was 3.5% (n=31) from NWNO participants at visit 1. Higher BMI (OR=5.19; 95% CI=3.15–9.27, p<0.001) and a higher mean overnight heart rate (OR=1.34; 95% CI=1.01–1.79, p=0.043) were statistically significantly associated with NWO at visit 1. Furthermore, HTN (OR=2.88; 95% CI=1.31–6.71, p=0.011), HLD (OR=2.12; 95% CI=1.21–3.77, p=0.009), and MetS (OR=3.48; 95% CI=1.62–8.09, p=0.002) were statistically significantly associated with NWO at visit 1. Participants with NWO at visit 1 had higher odds of developing insulin resistance (OR=1.79; 95% CI=1.07–3.04, p=0.029) and MetS (OR=2.03; 95% CI=1.15–3.66, p=0.016) at visit 2 compared to NWNO participants at visit 1, after age and sex matching. Conclusion Among Hispanic adults, the prevalence of NWO was associated with a higher BMI, higher mean overnight heart rate, treated HTN, HLD, and MetS. Hispanic adults with NWO were more likely to develop insulin resistance and MetS over 6 years compared to NWNO Hispanic adults. Overall, NWO among Hispanic adults was associated with CVD risk factors which places this population at a higher risk for CVD morbidity and mortality.

Abstract Introduction Atrial fibrillation (AF) is a major contributor to stroke risk, yet many cases remain undiagnosed due to its silent nature. Deep learning-based electrocardiogram (DL-ECG) analysis can streamline AF risk stratification, providing a more reliable and accessible method to stratify risk using only a standard resting ECG. However, this tool has been validated only on AF detected clinically or with short ECG event monitors. Purpose This study aimed to validate the predictive accuracy of a DL-ECG model for identifying AF in normal sinus rhythm (NSR) ECGs among patients undergoing continuous cardiac monitoring with implantable loop recorders (ILRs). Methods We conducted a historical cohort study across three major U.S. academic medical centers, enrolling patients with an ILR and no prior diagnosis or documented ECG evidence of AF. Pre-implantation 10-second, 12-lead ECGs in NSR were analyzed using the DL-ECG model. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with the CHARGE-AF score. Kaplan-Meier survival analysis and Cox proportional hazards models assessed the association between DL-ECG risk categories and incident AF, defined as any episode lasting ≥6 minutes. Results We included 2,523 patients (mean age 66 ± 17 years, 52% male). AF was detected in 658 (26%) over a median follow-up of 19 months (IQR 8-37), Table 1. The DL-ECG model classified 1,634 patients (65%) as at low-risk and 889 (35%) as at high-risk. AF developed in 242 (14.8%) of the low-risk group and 416 (46.8%) of the high-risk group, yielding an AUC of 0.73 (95% CI 0.70-0.75), surpassing the CHARGE-AF AUC of 0.69 (95% CI 0.66-0.71). A combined DL-ECG and CHARGE-AF model yielded an AUC of 0.74 (95% CI 0.72-0.76), Figure 1 presents the AUC, Net Reclassification Index, and DeLong test comparisons among the three models. High-risk patients had a significantly higher incidence of AF, which remained significant after adjusting for CHARGE-AF variables, Figure 2. AF incidence increased as DL-ECG quartiles increased, with 61% of patients at the top quartile developing AF during follow-up, Figure 3. False negatives (patients initially classified as low-risk but in whom later AF was detected) had a longer median time to AF detection (127 days [IQR 28-425] vs. 101 days [IQR 20-318] for true positives, p = 0.01), and where found to have at least one positive ECG during follow-up in 36%, compared to 32% of true negatives, indicating eventual risk reclassification. Conclusions Among patients undergoing ILR monitoring, the DL-ECG model demonstrated superior predictive performance compared to CHARGE-AF, offering a promising and accessible alternative for early detection of silent AF. This approach could aid early AF detection, particularly in resource-limited settings where access to specialized rhythm monitoring services is limited.Table 1 and Figure 1 Figure 2 and 3