Daily Cardiology Research Analysis
Three impactful cardiology studies stood out: a pooled cohort showed CT-derived fractional flow reserve (CT-FFR) adds strong, incremental prognostic value for long-term outcomes; an imaging study used unsupervised clustering of cardiac magnetic resonance–derived haemodynamic force curves to phenotype non-ischaemic LV cardiomyopathy beyond EF/LGE; and a nationwide EHR study detailed causes of death in atrial fibrillation, highlighting markedly elevated sudden cardiac death risk. Together, these w
Summary
Three impactful cardiology studies stood out: a pooled cohort showed CT-derived fractional flow reserve (CT-FFR) adds strong, incremental prognostic value for long-term outcomes; an imaging study used unsupervised clustering of cardiac magnetic resonance–derived haemodynamic force curves to phenotype non-ischaemic LV cardiomyopathy beyond EF/LGE; and a nationwide EHR study detailed causes of death in atrial fibrillation, highlighting markedly elevated sudden cardiac death risk. Together, these works advance risk stratification, precision phenotyping, and population-level understanding.
Research Themes
- AI-enabled noninvasive coronary physiology and long-term prognosis
- Advanced CMR phenotyping using haemodynamic force dynamics
- Population-level mortality patterns and sudden cardiac death in atrial fibrillation
Selected Articles
1. Prognostic Significance of Computed Tomography-Derived Fractional Flow Reserve for Long-Term Outcomes in Individuals With Coronary Artery Disease.
In 2,566 CAD patients followed for a median of ~6 years, deep learning–derived CT-FFR ≤0.80 was strongly associated with MACE (HR ~5.05) and improved prediction beyond clinical and CT anatomic models. Incorporating CT-FFR yielded incremental prognostic value for long-term outcomes.
Impact: This study supports CT-FFR as a clinically meaningful, noninvasive physiologic metric that independently predicts long-term events, strengthening the rationale for integrating AI-enabled physiology into routine CAD assessment.
Clinical Implications: Use CT-FFR alongside clinical and anatomic CT to refine long-term risk stratification and guide management (e.g., intensifying preventive therapy or functional testing) in patients with intermediate stenoses.
Key Findings
- CT-FFR ≤0.80 independently predicted major adverse cardiovascular events over a median ~6-year follow-up (multivariable HR ≈ 5.05).
- Adding CT-FFR to clinical and CCTA anatomic models improved long-term outcome prediction.
- Among 2,566 patients, 9.2% experienced MACE; model performance increased stepwise from clinical to anatomic to anatomic+CT-FFR.
Methodological Strengths
- Large pooled individual patient dataset with long follow-up (median ~6 years).
- Comparative modeling framework demonstrating incremental value of CT-FFR beyond clinical and anatomic variables.
Limitations
- Retrospective pooled design with potential selection and measurement biases.
- Event rates and generalizability may vary across contributing cohorts and CT-FFR algorithms.
Future Directions: Prospective, multicenter studies comparing CT-FFR-guided management against standard care with hard outcome endpoints; head-to-head evaluation of AI-CT-FFR methods and integration with plaque characterization.
BACKGROUND: Data on the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) for long-term outcomes are limited. METHODS AND RESULTS: A retrospective pooled analysis of individual patient data was performed. Deep-learning-based CT-FFR was calculated. All patients enrolled were followed-up for at least 5 years. The primary outcome was major adverse cardiovascular events. The secondary outcome was death or nonfatal myocardial infarction. Predictive abilities for outcomes were compared among 3 models (model 1, constructed using clinical variables; model 2, model 1+coronary computed tomography angiography-derived anatomical parameters; and model 3, model 2+CT-FFR). A total of 2566 patients (median age, 60 [53-65] years; 56.0% men) with coronary artery disease were included. During a median follow-up time of 2197 (2127-2386) days, 237 patients (9.2%) experienced major adverse cardiovascular events. In multivariable-adjusted Cox models, CT-FFR≤0.80 (hazard ratio [HR], 5.05 [95% CI, 3.64-7.01]; CONCLUSIONS: CT-FFR provides strong and incremental prognostic information for predicting long-term outcomes. The combined models incorporating CT-FFR exhibit modest improvement of prediction abilities, which may aid in risk stratification and decision-making.
2. Unsupervised clustering of intra-ventricular haemodynamic forces for the phenotyping of left ventricular function in non-ischaemic left ventricular cardiomyopathy.
Using cine CMR-derived haemodynamic force curves, unsupervised clustering identified three NILVC phenotypes with stepwise risk increases. Clusters 2 and 3 predicted adverse outcomes independent of EF, LV size, and LGE over a median 40 months.
Impact: Introduces a dynamic, physics-based imaging biomarker that extends beyond static EF/LGE to refine NILVC phenotyping and prognosis, potentially informing personalized monitoring.
Clinical Implications: CMR HDF profiling may help identify NILVC patients at higher risk despite similar EF/LGE, guiding closer follow-up, earlier HF therapy escalation, or arrhythmia surveillance.
Key Findings
- Unsupervised clustering of longitudinal and transversal HDF curves identified three phenotypes with stepwise worsening atrial and ventricular function.
- Clusters 2 and 3 were associated with significantly higher risk of the composite endpoint (CV death, HF hospitalization, ventricular arrhythmias) independent of EF, LV size, and LGE.
- Median follow-up was 40 months with 60 events among 279 NILVC patients.
Methodological Strengths
- Novel use of dynamic time warping and partitioning around medoids for full-curve HDF clustering.
- Adjustment for established prognostic markers (EF, LV size, LGE) demonstrating incremental prognostic value.
Limitations
- Single-center retrospective cohort without external validation; potential overfitting of clusters.
- HDF derivation depends on image quality and standardization; clinical integration pathways require prospective testing.
Future Directions: Prospective multicenter validation of HDF-based phenotyping, integration with strain/fibrosis biomarkers, and interventional trials to test phenotype-guided therapy.
AIMS: Cardiac magnetic resonance (CMR) is essential for diagnosing cardiomyopathy, serving as the gold standard for assessing heart chamber volumes and tissue characterization. Haemodynamic forces (HDFs) analysis, a novel approach using standard cine CMR images, estimates energy exchange between the left ventricular (LV) wall and blood. While prior research has focused on peak or mean longitudinal HDF values, this study aims to investigate whether unsupervised clustering of HDF curves can identify clinically significant patterns and stratify cardiovascular (CV) risk in non-ischaemic LV cardiomyopathy (NILVC). METHODS AND RESULTS: A retrospective cohort of 279 patients with NILVC who underwent cardiac CMR at Vall d'Hebron University Hospital (Barcelona) was examined. Unsupervised clustering of longitudinal and transversal HDF curves was performed using dynamic time warping for dissimilarity measurement and the partitioning around medoids algorithm. Outcomes were defined as a composite of CV mortality, heart failure hospitalization, and ventricular arrhythmias. The median age was 65 (57.0; 74.0) years, with 27.2% females and 35.5% showing late gadolinium enhancement (LGE). Unsupervised clustering identified three distinct clusters, delineating risk groups with worsening LA and LV function, indicating a stepwise increase in CV risk profile. Over a median follow-up of 40 months, 60 patients experienced the composite outcome. After adjusting for LGE, LV ejection fraction (EF), and LV size, Clusters 2 and 3 demonstrated a significantly higher risk of adverse events (both P < 0.05) compared with Cluster 1. CONCLUSION: Analysing both longitudinal and transversal HDFs throughout the cardiac cycle enables the identification of distinct phenotypes with prognostic value beyond EF and LGE in NILVC patients.
3. Causes of death in patients with atrial fibrillation in the UK: a nationwide electronic health record study.
In a nationwide matched cohort of 214,222 AF patients, sudden cardiac death occurred in 6.5% versus 2.0% in matched non-AF (OR 3.38). Circulatory diseases dominated mortality, with sex-specific differences: higher cardiovascular/respiratory mortality in women and more SCD in men.
Impact: This large-scale EHR study quantifies cause-of-death patterns in AF and underscores elevated SCD risk, informing risk stratification, surveillance, and preventive strategies at population scale.
Clinical Implications: AF care pathways may merit enhanced SCD risk assessment (e.g., structural remodeling, ventricular ectopy) and targeted prevention, with attention to sex-specific mortality patterns.
Key Findings
- Among 214,222 AF patients, SCD occurred in 6.50% versus 2.01% in matched non-AF, OR 3.38 (95% CI 3.27–3.50).
- Circulatory system diseases were the leading causes of death in AF; women had higher cardiovascular and respiratory mortality but fewer neoplasm deaths.
- Median follow-up 2.7 years; study leveraged linked nationwide primary/secondary care data with matched controls.
Methodological Strengths
- Nationwide, matched cohort linking primary and secondary care with large sample size.
- Systematic cause-of-death adjudication using ICD-10 and chapter-level categorization.
Limitations
- Observational design with residual confounding; cause-of-death misclassification possible.
- Median follow-up relatively short; limited granularity on arrhythmic substrate and interventions.
Future Directions: Integrate EHR with imaging/ECG biomarkers to refine SCD risk in AF; prospective validation of SCD risk tools and targeted prevention strategies.
AIMS: Causes of death remain largely unexplored in the atrial fibrillation (AF) population. We aimed to (i) thoroughly assess causes of death in patients with AF, especially those associated with sudden cardiac death (SCD) and (ii) evaluate the potential association between AF and SCD. METHODS AND RESULTS: Linked primary and secondary care United Kingdom Clinical Practice Research Datalink dataset comprising 6 529 382 individuals aged ≥18. We identified 214 222 patients with newly diagnosed AF, and an equivalent number of non-AF patients matched for age, sex and primary care practice. The underlying primary cause of death for each patient was assessed in the form of International Classification of Diseases Tenth Revision (ICD-10) codes and also as part of broader disease categories (i.e. ICD-10 chapters). FINDINGS: Over a median follow-up of 2.7 (interquartile range: 0.7-6.0) years, 124 781 (58.25%) patients with AF died. Sudden cardiac death occurred in 13 923 patients with AF [6.50% patients with AF vs. 2.01% non-AF patients; odds ratio (OR) = 3.38, 95% confidence interval (CI): 3.27-3.50, INTERPRETATION: Conditions of the circulatory system are the main driver of mortality in the AF population. Females with AF experience higher cardiovascular and respiratory mortality but die less frequently of neoplasms. The risk of SCD is higher in the AF population, occurring more frequently in males.