Daily Cardiology Research Analysis
Analyzed 203 papers and selected 3 impactful papers.
Summary
Analyzed 203 papers and selected 3 impactful articles.
Selected Articles
1. Multiple 4q25 risk variants impair calcium homeostasis and compromise left atrial function.
Across UK Biobank, three independent 4q25 AF risk alleles increased incident AF in a dose-dependent manner. Human atrial myocyte studies revealed allele-specific calcium abnormalities: reduced ICaL with rs1448818, and increased spontaneous Ca release (higher ITI frequency) with rs2200733/rs10033464 alongside increased RyR2 Ser2808 phosphorylation; cardiac MRI linked rs1448818 and rs2200733 to larger LA volumes and reduced active ejection. These data suggest genotype-tailored strategies targeting ICaL restoration vs suppression of spontaneous Ca release.
Impact: This is a rigorous translational study linking AF risk genetics to human cellular electrophysiology and atrial function, identifying mechanistic heterogeneity with therapeutic implications.
Clinical Implications: Genetic stratification may inform precision AF prevention or therapy: agents restoring L-type Ca current could suit rs1448818 carriers, whereas therapies reducing spontaneous SR Ca release (e.g., RyR2 modulators) may benefit rs2200733/rs10033464 carriers.
Key Findings
- All three 4q25 AF risk alleles increased incident AF in a dose-dependent manner in UK Biobank (n=391,008).
- rs1448818 carriers showed reduced L-type Ca current (ICaL) in human atrial myocytes, while rs2200733/rs10033464 increased transient inward current frequency and RyR2 Ser2808 phosphorylation.
- Cardiac MRI (n=39,391) linked rs1448818 and rs2200733 to larger minimal/maximal LA volumes and lower active LA ejection fraction, with preserved LAAEF in rs10033464.
Methodological Strengths
- Multimodal design integrating population genetics (UK Biobank), human atrial myocyte electrophysiology, and cardiac MRI.
- Allele-specific cellular phenotyping in human tissue from patients without AF, strengthening causal inference.
Limitations
- Observational genetic associations cannot prove causality; experimental validation is limited by moderate ex vivo sample sizes.
- Generalizability may be constrained by ancestry composition of UK Biobank.
Future Directions: Prospective, ancestry-diverse validation and interventional studies testing genotype-tailored calcium-handling therapies are warranted.
AIMS: Single-nucleotide polymorphisms (SNPs) from distinct linkage blocks in the chromosomal region 4q25 (rs1448818, rs2200733 and rs10033464) are associated with increased risk of atrial fibrillation (AF), but their impact on cardiomyocyte and atrial function remain elusive. Here, we tested the hypothesis that these SNPs have differential effects on calcium homeostasis that may afford SNP-specific targets and help explain their impact on atrial function. METHODS AND RESULTS: Analysis of 391,008 individuals from the UK biobank reveale
2. Generalisable artificial intelligence ECG trained on public data for outcome prediction after transcatheter aortic valve replacement.
A transformer AI-ECG trained on 341,151 public ECGs predicted 30-day (AUC 0.85) and 1-year (AUC 0.74) mortality after TAVR from a single pre-procedural ECG. The AI risk score independently associated with adverse outcomes and generalized to a surgical cohort, highlighting feasibility of transparent, shareable AI for risk stratification.
Impact: Demonstrates clinically useful, externally validated prediction using a model trained solely on public data, addressing reproducibility and generalisability barriers to AI adoption.
Clinical Implications: A single pre-procedural ECG can inform TAVR risk stratification, potentially guiding prehabilitation, post-procedure monitoring intensity, and shared decision-making without requiring proprietary datasets.
Key Findings
- Pre-procedural single ECG predicted 30-day and 1-year mortality after TAVR with AUCs 0.85 and 0.74, respectively.
- AI-ECG risk score was independently associated with 1-year mortality, major adverse cardiac events, and major renal events.
- Model trained on 341,151 public ECGs generalized to an independent surgical AVR cohort; salient ECG features included AF, prolonged QT, ST-T abnormalities, and low voltage.
Methodological Strengths
- External prospective validation with clinically relevant endpoints (30-day and 1-year mortality).
- Training exclusively on publicly available ECGs enhancing transparency and reproducibility.
Limitations
- Observational validation without interventional assessment of clinical impact.
- Single-center details not specified for surgical cohort; potential dataset shift and calibration across devices/systems not fully addressed.
Future Directions: Prospective impact studies integrating AI-ECG into TAVR workflows (triage, monitoring, discharge planning) and calibration/threshold optimization across vendors and populations.
BACKGROUND: Artificial intelligence ECG (AI-ECG) models can predict cardiovascular outcomes, but their clinical adoption is limited by restricted access to training data and uncertain generalisability. We developed and externally validated a generalisable AI-ECG model trained exclusively on publicly available data to predict outcomes after transcatheter aortic valve replacement (TAVR). METHODS: A transformer-based AI-ECG model was trained on 341 151 publicly available ECGs with mortality labels. The model was externally validated in a prospective cohort of 439 patients undergoing TAVR and additionally tested in a surgical aortic valve replacement cohort. Model performance for predicting 30-day and 1-year mortality was assessed using area under the receiver operating characteristic curve (AUC) and associations with clinical outcomes were evaluated using multivariable regression. RESULTS: In the TAVR cohort, a single pre-procedural ECG predicted 30-day mortality with an AUC of 0.85 (95% CI 0.78 to 0.92) and 1-year mortality with an AUC of 0.74 (95% CI 0.67 to 0.80). The derived AI-ECG risk score was independently associated with 1-year mortality (adjusted OR 1.70), major adverse cardiac events and major renal events. Predictive performance was consistent in an independent surgical cohort. High-risk ECG features included atrial fibrillation, prolonged QT interval, ST-T abnormalities and low voltage. CONCLUSIONS: An AI-ECG model trained solely on publicly available data provides accurate and generalisable prediction of outcomes after TAVR using a single ECG. This approach demonstrates the feasibility of transparent, shareable AI models for cardiovascular risk stratification and supports a general-to-specific paradigm for clinical deployment.
3. Effect of renin-angiotensin system inhibition on left ventricular mass regression after transcatheter aortic valve replacement: a randomised controlled trial.
In a multicenter PROBE RCT (n=200), adding RAS inhibition after TAVR in patients with HF and LVEF ≥40% produced greater 12-month LV mass index regression and reductions in LV volumes versus standard care, with modest NYHA class benefit and no changes in LVEF or NT-proBNP.
Impact: Provides randomized evidence that common RAS agents can augment structural reverse remodeling after TAVR, informing routine post-procedural pharmacotherapy.
Clinical Implications: RAS inhibition may be considered to enhance LV reverse remodeling after TAVR in patients with HF and LVEF ≥40%, while awaiting outcome-driven trials to confirm effects on morbidity and mortality.
Key Findings
- RAS inhibition yielded greater 12-month LV mass index reduction versus control (adjusted mean difference −12.77 g/m²; p=0.036).
- Consistent decreases in LV end-diastolic and end-systolic volumes were observed with RAS inhibition.
- No significant between-group differences in LVEF or NT-proBNP; modest NYHA class improvement favored RAS inhibition.
Methodological Strengths
- Multicenter randomized, open-label with blinded endpoint (PROBE) design and trial registration.
- Prespecified imaging primary endpoint with modified intention-to-treat analysis.
Limitations
- Surrogate primary endpoint (LVMI) without powered clinical outcomes.
- Heterogeneity across RAS agents; open-label treatment may introduce performance bias.
Future Directions: Larger, outcomes-focused RCTs to test whether augmented reverse remodeling translates into reduced HF hospitalization and mortality, and to define optimal agent/dose and duration.
BACKGROUND: Residual left ventricular (LV) hypertrophy and incomplete reverse remodelling after transcatheter aortic valve replacement (TAVR) are associated with adverse outcomes. Whether renin-angiotensin system inhibitors (RASi) promote reverse remodelling in patients with heart failure and LV ejection fraction (LVEF) ≥40% following TAVR remains uncertain. METHODS: In this multicentre, prospective, randomised, open-label, blinded-endpoint trial, patients aged ≥60 years with symptomatic severe aortic stenosis, LVEF ≥40% and successful TAVR were randomly assigned (1:1) to standard care alone or standard care plus RASi (ACE inhibitor, angiotensin II receptor blocker or angiotensin receptor-neprilysin inhibitor). The primary endpoint was change in LV mass index (LVMI) at 12 months. Secondary endpoints included changes in LV volumes, LVEF, N-terminal pro-B-type natriuretic peptide (NT-proBNP) and functional status. RESULTS: A total of 200 patients were randomised; 194 were included in the modified intention-to-treat analysis (RASi n=95; control n=99). At 12 months, RASi therapy was associated with a greater reduction in LVMI compared with control (adjusted mean difference -12.77 g/m², 95% CI -24.73 to -0.81; p=0.036). Consistent improvements were observed in LV end-diastolic and end-systolic volumes. Functional status (New York Heart Association class) improved modestly in the RASi group. No significant differences were observed in LVEF or NT-proBNP. CONCLUSION: In patients with heart failure and LVEF ≥40% following TAVR, RAS inhibition led to enhanced reverse LV remodelling over 12 months, reflected by greater regression of LV mass and volumes. These findings support the potential role for RASi in modifying post-TAVR myocardial remodelling, although larger trials are required to determine whether these structural benefits translate into improved clinical outcomes. TRIAL REGISTRATION NUMBER: ChiCTR2100042266.