Khand, Yugant

Cardiac allograft vasculopathy (CAV) remains a leading cause of graft failure after heart transplantation, yet an early, non-invasive risk stratification tool is not available. Artificial intelligence electrocardiography (AI-ECG) can estimate biological heart age, and it has been associated with cardiovascular risk. Its deviation with chronological age, called delta age, and its utility in the prediction of CAV has never been explored. We conducted a retrospective single-center study for heart transplant recipients bridged with Impella 5.5 (Abiomed, Danvers, MA, USA) at Mayo Clinic Florida (2020–2023). ΔAge was calculated at discharge and 1-year post-transplant. The primary outcome was confirmed CAV at 1 year. Univariate and multivariable logistic regression models were used to assess the independent association between the ΔAge and the development of CAV, with discrimination evaluated by AUC and internally validated via bootstrap resampling (2000 iterations). Fifty patients were reviewed. CAV developed in 13 (26%) patients in 3 years. CAV+ patients had a significantly higher ΔAge at discharge compared to CAV- patients (median −1.0 vs. −11.0 years, p = 0.0023) and at 1 year (median +7.0 vs. −4.0 years, p = 0.0080). On univariate analysis, ΔAge at discharge was a significant predictor of the development of CAV (OR 1.084 per year, AUC 0.788). On multivariable adjustment, ΔAge at discharge remained an independent predictor (adjusted OR 1.138, 95% CI 1.023–1.265, p = 0.018, and model AUC 0.859). ΔAge at discharge is a novel and potential predictor of the development of CAV within 3 years. These findings support the integration of AI-ECG into post-transplant surveillance as a scalable, low-cost tool.

Abstract Background Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have demonstrated cardiovascular benefits in type 2 diabetes mellitus (T2DM); however, their association with post-stroke outcomes following acute ischemic stroke (AIS) remains uncertain. Methods We conducted a retrospective propensity score-matched (PSM) cohort study using the TriNetX Global Collaborative Network. Adults with T2DM and AIS between January 2020 and January 2025 were included. Pre-stroke GLP-1 RA users were compared with non-users. Primary outcomes were all-cause mortality, intracerebral hemorrhage (ICH), ICD-10-coded stroke severity (NIHSS categories), and transient ischemic attack (TIA). Results After PSM, GLP-1 RA use was associated with significantly lower all-cause mortality at all time points (RR 0.44–0.52; HR 0.43–0.45; all p<0.001). ICH risk was reduced within 1–3 days (RR 0.683; 95% CI 0.496–0.940; p=0.019) and within 7 days (RR 0.575; 95% CI 0.516–0.641; p<0.001). A severity-dependent gradient was observed across NIHSS categories, with risk reductions ranging from 37% for minor strokes (NIHSS 0–5; RR 0.626) to 67% for very severe strokes (NIHSS >20; RR 0.327; all p<0.001). TIA risk was 29% lower (RR 0.712; 95% CI 0.668–0.759; p<0.001). E-value analysis demonstrated that an unmeasured confounder would need to be associated with both GLP-1 RA use and 30-day mortality by a risk ratio of at least 3.95 to fully explain the observed association, and by a risk ratio of at least 3.53 to shift the confidence interval to include the null. Conclusions In this large real-world cohort, pre-stroke GLP-1 RA use was associated with lower post-stroke mortality, reduced ICH, and a severity-dependent reduction in ICD-10-coded stroke severity among patients with T2DM. Residual confounding cannot be excluded, and these findings warrant confirmation in prospective randomized trials.