Daily Cardiology Research Analysis

Heart failure after acute myocardial infarction (post-MI HF) has become a major global health problem. Accurate risk prediction is essential for optimising management and preventing post-MI HF. However, existing models rely mainly on resting-state clinical examinations and inadequately reflect the complex pathophysiology of post-MI HF. We aimed to develop and validate a multimodal machine learning (ML) model incorporating cardiopulmonary exercise testing (CPET) data to predict post-MI HF risk and to quantify CPET's incremental value.This study included 3172 acute myocardial infarction (AMI) patients who underwent CPET at three hospitals from 2018 to 2023. The primary outcome was post-MI HF within 1 year. Thirteen ML algorithms were used to select clinical and CPET variables and to construct multimodal prediction models. The incremental predictive value of CPET was evaluated by the area under the curve (AUC), integrated discrimination improvement index (IDI), and net reclassification improvement index (NRI).After screening, 2221 patients were included, of whom 221 (10.0%) developed post-MI HF. The optimal multimodal ML model achieved an AUC of 0.987 (95% CI: 0.982-0.992) in training set and 0.929 (95% CI: 0.903-0.955) in external validation set. Ablation analyses showed that CPET significantly improved discrimination (AUC: 0.890 vs. 0.929, P=0.003), calibration (IDI=0.135 [95% CI: 0.082-0.189], P<0.001), and reclassification (NRI=0.154 [95% CI: 0.073-0.234], P<0.001). The model effectively stratified low- and high-risk patients (3.1% vs. 54.7%, P<0.001). The multimodal ML model accurately predicted post-MI HF and highlighted the additive value of CPET in risk stratification. The web-based risk calculator derived from this model may support early identification of high-risk patients and facilitate personalised management.

BACKGROUND: Cardiac amyloidosis (CA) is an under-recognized cause of heart failure. Its prevalence in screening studies, and the extent to which selective confirmatory testing affects prevalence estimates, remain uncertain across clinical settings. METHODS: We systematically searched PubMed/MEDLINE and EMBASE up to 10 August 2025. Two reviewers independently screened studies and extracted data. We grouped studies by clinical setting and combined prevalence estimates using random-effects meta-analysis. For each study, we calculated CA prevalence in 1) the whole enrolled cohort and 2) the subgroup who underwent confirmatory testing. RESULTS: Eighty-three studies were included. Pooled CA prevalence (whole cohort; tested subgroup) was highest in left ventricular hypertrophy/hypertrophic cardiomyopathy [LVH/HCM] (15.1%; 35.4%), followed by heart failure [HF]-mainly HF with preserved or mildly reduced ejection fraction (HFpEF/HFmrEF)-(12.6%; 13.6%) and aortic stenosis [AS] (9.6%; 11.6%). Orthopedic cohorts were lower overall (4.1%) but higher in tested subgroups (12.4%); "no specific red flags" showed 1.8% vs. 7.8%; non-cardiac bone scintigraphy was 0.49% in both denominators. Across settings, transthyretin CA predominated over light-chain CA. Several studies approached systematic screening in the general elderly; however, they were few and still applied referral criteria to second-level examinations. CONCLUSION: This meta-analysis shows that CA is relatively frequent in HFpEF/HFmrEF, severe AS, and LVH/HCM. To obtain reliable population estimates, future studies should test either all eligible participants or a predefined random sample, rather than only those with suspected disease. In clinical practice, screening strategies should clearly define which higher-risk individuals are referred for second-level tests, balancing diagnostic yield with feasibility.

BACKGROUND: While guideline-directed medical therapy (GDMT) is recommended for left ventricular assist device (LVAD) recipients, real-world evidence supporting its clinical impact remains limited. This study evaluated the association between GDMT prescription and clinical outcomes in LVAD patients. METHODS: This international retrospective multicenter study included 875 LVAD patients from 22 centers. Patients were categorized based on the number of GDMT (ACE-I/ARBs, beta-blockers, MRAs) prescribed. Primary outcome was 6-month all-cause mortality. Secondary outcome was late ventricular arrhythmias (VAs) (>30 days post-implant). Multivariable Cox regression and ordinal logistic regression analyses were performed. RESULTS: Overall, only 261 patients (29.8%) received triple GDMT, while 97 (11.1%) received no GDMT. After multivariable adjustment, the number of prescribed GDMTs was independently associated with improved survival, with aHRs for all-cause mortality of 0.51 (0.33-0.75, p<0.01) for triple therapy, 0.39 (0.26-0.59, p<0.01) for dual therapy, and 0.45 (0.30-0.67, p<0.01) for single therapy, all compared with no GDMT. Similarly, ACE-I/ARB were associated with a lower risk of late VAs (aHR 0.65 [0.50-0.84], p<0.01). Female sex, diabetes, early VAs, and higher bilirubin levels were associated with lower GDMT prescription rates. Major LVEDD improvement (≥10mm reduction) increased progressively from 51.5% without GDMT to 66.1% with triple therapy. CONCLUSION: In this large international study, the use of GDMT in LVAD patients was associated with improved survival, with benefits observed even with single-agent therapy. Despite these benefits, only 30% of patients received optimal triple therapy, highlighting the need for improved implementation strategies and standardized protocols in this population.