Prospective longitudinal evaluation of AI-ECG in newly diagnosed heart failure (PLANE-HF)

Abstract Background * In the United Kingdom, there are one million people suffering from heart failure (HF), with more than 60,000 new cases annually (1). * Monitoring for declining left ventricular ejection fraction (LVEF) could optimise treatment approaches and avert healthcare episodes. * Easy home-based tracking of cardiac function would be transformative. * Previous studies have validated the capability of artificial intelligence-enabled electrocardiogram (AI-ECG) to correlated with changes in LVEF (2). * We hypothesize that longitudinal changes in AI-ECG probability score is associated with changes in the left ventricular (LV) function. Method * A prospective multicentre longitudinal study recruiting newly diagnosed HF with reduced ejection fraction (HFrEF) cases to investigate application of AI-ECG using longitudinal single-lead ECG recordings via patient-administered smart ECG-stethoscope examination – Figure 1. * A total of 36 patients were included in the analysis, with invitation to return for repeat echocardiography at 6-8 weeks. * Adjusted logistic regression was performed to analyze changes in LVEF by 10% as the outcome variable, investigating the relationship with the trajectory of AI-ECG probability scores. This study was approved by the UK Health Research Authority (reference 22/LO/0701). Result * Out of 36 patients, 19 patients had ischaemic and 17 had a non-ischaemic HF. * The mean age was 56.3 years, 97.2% patients were male and 46.6% were White. * 328 longitudinal single-lead ECGs were captured and used in the analysis. * 12 (33.3%) patients had an increase of their LVEF by 10%; preceding AI-ECGs was predictive of this recovery (OR 1.36; 95% CI, 1.05 – 1.65, p value = 0.016 – Table 1) Clinical Importance: * Individualised predictions of deterioration and improvement * Proactive medication adjustment * Enhanced triage for echocardiography Conclusion * AI-ECG designed to identify left ventricular systolic dysfunction (LVSD) can predict changes in LVEF% among newly diagnosed HFrEF patients. * Such results can place AI-ECG as a remote monitoring tool to track improvement or deterioration in patients’ LVEF%, which facilitate a proactive management and prevention strategies.