Mangold, K E

Abstract Background Ventricular systolic dysfunction (LVSD) is the main predictor of mortality in Chagas disease (ChD). Although LVSD can be treated with affordable medications that enhance both symptoms and survival, its diagnosis necessitates advanced imaging tests, which are often unavailable in resource-limited settings. Recently, artificial intelligence (AI) models applied to an electrocardiogram (ECG) have shown promise in detecting LVSD and assessing disease progression, acting as digital biomarkers and offering the potential for screening tools. Purpose This study aims to evaluate the performance of an AI-ECG model in detecting LVSD and predicting mortality in patients with ChD. Specifically, we will fine-tune the AI-ECG model to improve LVSD identification and update the previously published mortality score (JAHA) by replacing NT-proBNP with AI-ECG to assess mortality risk. Methods This study was conducted with patients from the SaMi-Trop project, a prospective cohort of individuals seropositive for ChD from 21 municipalities in Minas Gerais, an endemic region of Brazil. The baseline assessment occurred in 2013–2014, with follow-ups in 2015–2016 (FU1) and 2022–2023 (FU2). The AI-ECG model was fine-tuned using a dataset with an 8% low ejection fraction prevalence. The model was trained with a binary cross-entropy loss function, a batch size of 128, and an Adam optimizer with a learning rate of 0.001. The area under the curve (AUC) was calculated for validation. The fine-tuned model, parsed into 2-second ECG windows, was applied to two waves of the SaMi-Trop cohort: Baseline (4,701 ECGs from 1,906 patients) and FU1 (2,324 ECGs from 1,059 patients). The association between AI-detected LVSD (AI-LVSD) and mortality was assessed using Cox proportional hazard models. Results The fine-tuned AI-LVSD model achieved an AUC of 0.88 (95% CI: 0.78–0.97) in detecting LVSD. When updating the JAHA mortality score, AI-LVSD replaced NT-proBNP with a stronger association with mortality risk, yielding hazard ratios (HR) of 4.5 (95% CI: 3,0–6.8) for two-year mortality and 4.3 (95% CI: 3.4–5.5) for overall mortality. Additionally, when incorporated into a simplified hazard model alongside age and NYHA class, AI-LVSD emerged as a strong biomarker for mortality risk, with hazard ratios of 5.4 (95% CI: 3.7–7.8) for two-year mortality and 4.9 (95% CI: 4.0–6.1) for overall mortality (Figure 1). Conclusion The AI-ECG model accurately identified LVSD for screening purposes and effectively replaced NT-proBNP in a simplified mortality risk model for Chagas disease. This tool can potentially enhance early detection and risk stratification, particularly in remote areas with limited access to advanced diagnostic resources.Figure 01.Cox proportional hazard model

Abstract Background Artificial intelligence (AI) models trained on 12-lead ECGs effectively detect left ventricular systolic dysfunction (LVSD; left ventricular ejection fraction [LVEF] <=40%). Continuous ECG monitoring via Holter recordings provides an opportunity for opportunistic screening for structural heart disease beyond rhythm disorders. We hypothesized that a lead-invariant version of the 12-lead AI model would enable a Holter monitor to screen for both arrhythmias and ventricular dysfunction. Methods We retrospectively analyzed continuous Holter ECGs from 17,665 patients who underwent a Holter and transthoracic echocardiogram (TTE) within 30 days of each other at Mayo Clinic. From each Holter, a random 20-minute of valid (non-flatline/lead disconnect) ECG segment was extracted and analyzed for LVSD detection using the adapted lead-invariant AI model. To evaluate stability, we examined model performance across different time points of the day, presenting results as area under the receiver operating characteristic curve (AUC) over time. Moreover, we illustrated the model’s robustness to noisy data by comparing its performance on raw ECG signals with that on bandpass-filtered inputs. Results Among 17,665 patients (mean age 59 years, 48.57% female), 4.96% had an LVEF <=40%. The AI model demonstrated strong predictive performance (20-minute segment AUC 0.90, mean prediction of 24-hour AUC 0.92). Analysis of results over time (Figure) revealed temporal patterns in predictive accuracy, with specific time periods showing greater stability. Despite modest variability, model performance remained consistently high throughout the day, confirming robustness across different physiological states. The predictions remained robust with noisy input. We did not observe performance improvement when the baseline wander and high frequency noise are removed by the bandpass filter. Conclusion Applying a 12-lead AI ECG model with a lead-invariant framework to a continuous Holter ECG enables effective screening for left ventricular dysfunction. This suggests that AI-based analysis of Holter-monitors can facilitate opportunistic screening of ventricular dysfunction and may enable assessment of an arrhythmia’s impact on LVEF, as well as the relationship between arrhythmia burden and LVEF.Figure 1.Mean prediction AUC of the day Figure 2.AUC for different time point