Citations

INTRODUCTION: The SPEC-AI Nigeria trial (NCT05438576) was designed to use artificial intelligence (AI) to screen for pregnancy-related cardiomyopathy in the peripartum period. Using data from this study, we evaluated the utility of AI-predicted delta age (adjusted AI-predicted age − chronological age) as a surrogate for biological age. METHODS: We included 1,187 pregnant and postpartum women enrolled between August 2022 and September 2023 with follow-up through May 2024. Standard 12-lead electrocardiograms (ECGs) were recorded at study entry to generate AI age predictions. Artificial intelligence-predicted age was adjusted using estimated reference ranges obtained from a community-dwelling cohort of 25,144 individuals with AI-ECG age estimated and documented chronological age. Logistic and Cox-proportional hazards regression were used to examine associations with comorbid cardiovascular conditions and maternal mortality, respectively. RESULTS: Adjusted AI-predicted age was significantly higher among women with any cardiovascular condition, peripartum cardiomyopathy, or who died within 18 months (7, 14, and 23 years older, respectively) compared to those without these conditions who had values similar to the normal reference ranges (adjusted AI-predicted age difference less than 1 year). Artificial intelligence-predicted delta age greater than the 75th percentile was associated with an odds ratio (OR) of 2.06 for any cardiovascular condition, OR of 4.98 for left ventricular systolic dysfunction, and a hazard ratio of 32.81 for all-cause mortality; all values of P<.001. CONCLUSIONS/IMPLICATIONS: Artificial intelligence-ECG-derived biological age appears to be a potentially useful measure of cardiovascular health status and risk among pregnant and postpartum women. However, its role in monitoring maternal health requires further exploration.

Background: Deciding to use oral anticoagulation (OAC) for stroke prevention in patients with atrial fibrillation (AF) is complex. For many, the decision between treatment and non-treatment requires a primary tradeoff between the risks of potential stroke and increased bleeding. Hypothesis: We hypothesized that patient values and empiric stroke risk measured by CHA 2 DS 2 -VASc score would influence OAC decisions. Methods: We conducted a secondary analysis of a randomized clinical trial where eligible clinicians and patients were independently randomized to one of two decision aids (DAs) – a patient decision aid and/or an encounter decision aid. Eligible patients were adults with AF with CHA 2 DS 2 -VASc≥1 for men or ≥2 for women. Patients newly prescribed (OAC-naive subgroup) or already taking OAC (OAC-experienced subgroup) were included. We assessed patient values using a 5-point Likert scale between stroke prevention and bleeding risk after SDM during a clinical encounter. We report preliminary results using descriptive statistics and adjusted analysis to investigate the effects of CHA 2 DS 2 -VASc and expressed values on OAC use. Results: 1005 patients were included in the analysis. 828 (82%) patients valued stroke prevention over bleeding risk. Among the OAC-naive group, 175 (63%) valued stroke prevention more than bleed avoidance, 58 (20%) valued bleed avoidance more than stroke prevention, and 47 (17%) were neutral. In the unadjusted analysis, patients were more likely to value stroke prevention if they were OAC-experienced (p<0.001), older (p<0.001), female (p< 0.001), had patient-reported bleeding (p=0.003), or if CHA 2 DS 2 -VASc ≥ 2 if male, ≥3 if female (p<0.001) Table 1 . After adjustments, there were significant associations for OAC use among patients who valued stroke prevention (OR 5.35, 95%CI 3.13-9.13, p<0.001) or had higher CHA 2 DS 2 -VASc (OR 2.03, 95%CI 1.26-3.27, p=0.004). Patients who valued bleed avoidance had an opposing association not to use OAC (OR 0.23, 95%CI 0.11-0.48, p<0.001). Conclusions: Patients with elevated risk (CHA 2 DS 2 -VASc score, age, and female), and those who reported a prior bleeding event valued stroke prevention more than bleeding avoidance. Both empiric stroke risk and patient values were associated with the decision to use OAC. While the tradeoff between stroke prevention and bleeding risk is only one aspect of the decision-making process, these results indicate variability in patient values may influence OAC use.

Abstract Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date—the electrocardiogram (ECG). The application of artificial intelligence‐enabled ECG (AI‐ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI‐ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417‐3424, 2022.

Abstract Aims Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and understanding AI-related research in EP is lacking. This scientific statement aims to develop and apply a checklist for AI-related research reporting in EP to enhance transparency, reproducibility, and understandability in the field. Methods and results An AI checklist specific to EP was developed with expert input from the writing group and voted on using a modified Delphi process, leading to the development of a 29-item checklist. The checklist was subsequently applied to assess reporting practices to identify areas where improvements could be made and provide an overview of the state of the art in AI-related EP research in three domains from May 2021 until May 2024: atrial fibrillation (AF) management, sudden cardiac death (SCD), and EP lab applications. The EHRA AI checklist was applied to 31 studies in AF management, 18 studies in SCD, and 6 studies in EP lab applications. Results differed between the different domains, but in no domain reporting of a specific item exceeded 55% of included papers. Key areas such as trial registration, participant details, data handling, and training performance were underreported (&lt;20%). The checklist application highlighted areas where reporting practices could be improved to promote clearer, more comprehensive AI research in EP. Conclusion The EHRA AI checklist provides a structured framework for reporting AI research in EP. Its use can improve understanding but also enhance the reproducibility and transparency of AI studies, fostering more robust and reliable integration of AI into clinical EP practice.

INTRODUCTION: Building on prior results, we hypothesized that an electrocardiogram (ECG)-enabled machine learning (ML) model could be used to detect advanced chronic liver disease (CLD). METHODS: A cohort with CLD and 12-lead ECGs was matched with controls from electronic health records. A ML model was trained as a binary classifier. RESULTS: There are 12,930 patients with CLD and 64,577 controls in the cohort. The model's discriminative ability to classify CLD showed an area under the receiver-operating characteristic curve 0.858 (95% confidence interval: 0.850–0.866), and at the chosen threshold, CLD ECGs had 12 times higher odds of being classified as CLD (diagnostic odds ratio 12.33, 95% confidence interval: 11.16–13.63). DISCUSSION: An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.

Hypertension is the leading modifiable risk factor for atrial fibrillation (AF) and is estimated to be present in >70% of AF patients. This Frontiers Review was prepared by 29 expert members of the AF-SCREEN International Collaboration to summarize existing evidence and knowledge gaps on links between hypertension, AF, and their cardiovascular sequelae; simultaneous screening for hypertension and AF; and the prevention of AF through antihypertensive therapy. Hypertension and AF are inextricably connected. Both are easily diagnosed, often silent, and frequently treated inadequately. Together, they additively increase the risk of ischemic stroke, heart failure, and many types of dementia, resulting in greater all-cause mortality, considerable disease burden, and increased health care expenditures. Automated upper arm cuff blood pressure devices with implemented technology can be used to simultaneously detect both hypertension and AF. However, positive screening for AF with an oscillometric blood pressure monitor still requires ECG confirmation. The current evidence suggests that high-risk individuals aged ≥65 years or with treatment-resistant hypertension could benefit from AF screening. Since antihypertensive therapy effectively lowers AF risk, particularly in individuals with left ventricular dysfunction, hypertension should be the key target for AF prediction and prevention rather than merely a comorbidity of AF. Nevertheless, several important gaps in knowledge need to be filled over the next years, including the ideal method and selection of patients for simultaneous screening of hypertension and AF and the optimal antihypertensive drug class and blood pressure targets for AF prevention.

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.

Migraine with aura(MwA) is associated with an increased risk of stroke and adverse vascular outcomes compared to those with migraine without aura (MwoA). AI-ECG prediction models developed at our institution can evaluate the probability of atrial fibrillation (AF) and estimate a patient’s age based on a normal sinus rhythm (NSR) ECG. Delta age, AI-ECG estimated age minus chronological age, indicates endothelial dysfunction and is associated with cardiovascular mortality. We aim to assess how AI-ECG prediction model outputs, specifically the AF probability and delta age, are associated with adverse vascular outcomes in patients with migraine. Adult patients diagnosed with MwA and MwoA from 2000-2020 with at least one digital, standard 12-lead ECG were identified. The first ECG that showed NSR is used as the index ECG. Patients with any adverse vascular outcomes before the index ECG were excluded. Adverse vascular outcomes used as endpoints include acute ischemic stroke, acute myocardial infarction, deep vein thrombosis/pulmonary embolism, AF, and carotid artery dissection. A total of 31301 patients (13783 MwA, 17518 MwoA) were included in our analysis. The mean age at the time of the index ECG was 44.3 (14.5), and the average follow-up time was 71.6 (69.8) months. Based on the index ECG, the mean AF probability was significantly higher in MwA compared to MwoA (2.39 vs 2.06, p< 0.001), while the delta age was lower in MwA compared to MwoA (3.7 vs 4.2, p <.001). The prevalences of all vascular outcomes are summarized in Table 1. Multivariate Cox regression models showed that a higher AF prediction model output (every 10% increase), as well as a higher delta age (every 10-year increase), are associated with an increased risk of composite vascular outcomes (HR1.20, 95%CI 1.17-1.23, p<.001, and HR1.19, 95%CI 1.14-1.23, p < .001) [Table 2]. The Kaplan-Meier curve of adverse vascular outcome-free survival by AF prediction model output, using 1% as the cutoff (HR 1.49, 95% CI 1.41, 1.57, p<.001), and by delta age, using 2-year as the cutoff (HR 1.14, 95% CI 1.08, 1.21, p <. 001) are presented in Figure 1. Having aura, older age, male, coronary artery disease, congestive heart failure, diabetes, hypertension, and tobacco use were also associated with a higher risk of adverse vascular outcomes. Our results demonstrated the potential of using AI-ECG AF prediction model output and delta age to identify migraine patients at risk for adverse vascular outcomes.

Abstract Background Moderate-severe mitral annulus calcification (MAC) and left ventricular (LV) diastolic dysfunction are both linked to increased risks of heart failure and mortality. Assessing LV diastolic function (LVDF) in moderate-severe-MAC by echocardiography is frequently challenging and unreliable. We recently validated an AI-ECG model for LVDF. We aimed to determine if this tool can predict long-term mortality in patients with moderate-severe-MAC and whether it can help establish MAC-specific echocardiographic LVDF parameter cutoffs. Methods This retrospective study included all patients from the AI-ECG LVDF study test cohort who underwent comprehensive transthoracic echocardiography confirming moderate-severe-MAC and ECG within fourteen days of each other at Mayo Clinic between 09/2001 and 06/2022. AI-ECG LVDF was determined based on the index ECG. Patients were categorized based on AI-ECG into groups representing normal/grade-1 LVDF, grade-2 LVDF, or grade-3 LVDG (DF-1, DF-2, and DF-3, respectively). Results Of 2,506 patients with moderate-severe-MAC [mean age 77, 64.1% females], 709 (28.3%), 1,510 (60.3%), and 287 (11.5%) were classified as DF-1, DF-2, and DF-3, respectively. Worse LVDF grade was associated with older age, male sex, lower LV ejection fraction, and higher rates of noncardiovascular and cardiovascular comorbidities. Over a median follow-up of 2.8 years, 1,059 (42.3%) patients died. In multivariable survival analysis, DF-2 [adjusted hazard ratio (aHR) 1.43, 95% confidence interval (95%CI) 1.21-1.68] and DF-3 (aHR 1.54, 95%CI 1.22-1.93) were independently associated with a higher risk of death (figure). Findings were also consistent after adjusting to mitral inflow gradients. When comparing LVDF echocardiographic parameters in controls without MAC matched by age, sex, left ventricular ejection fraction, and AI-ECG DF grade, significant differences were observed. Patients with moderate to severe MAC generally exhibited lower e' velocities, higher TR velocities, elevated E/e' ratios, and increased LAVi. Interestingly, the E/A ratio between MAC patients and their matched controls across AI-ECG LVDF grades remained consistent. Conclusion AI-ECG LVDF predicts mortality risk in patients with moderate-severe-MAC, regardless of mitral inflow gradients. Utilizing AI-ECG LVDF might aid in the risk stratification of patients with significant MAC.