Daily Cardiology Research Analysis
Three studies advanced cardiovascular care today: an international multicentre study showed AI can extract volumetric body composition biomarkers from routine CT attenuation scans to independently predict mortality; a French nationwide cohort linked cardiologist follow-up intensity to lower one-year mortality in heart failure with practical, risk-based visit targets; and a registered meta-analysis suggested digoxin may significantly reduce angiodysplasia-related GI bleeding in CF-LVAD patients.
Summary
Three studies advanced cardiovascular care today: an international multicentre study showed AI can extract volumetric body composition biomarkers from routine CT attenuation scans to independently predict mortality; a French nationwide cohort linked cardiologist follow-up intensity to lower one-year mortality in heart failure with practical, risk-based visit targets; and a registered meta-analysis suggested digoxin may significantly reduce angiodysplasia-related GI bleeding in CF-LVAD patients.
Research Themes
- AI-enabled imaging biomarkers for cardiovascular risk stratification
- Health services optimization in heart failure follow-up
- Therapeutic repurposing to prevent LVAD-related gastrointestinal bleeding
Selected Articles
1. AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study.
In 9,918 patients from four centres, an AI pipeline quantified six chest body composition tissues from routine CT attenuation correction scans and independently predicted all-cause mortality. Higher VAT, EAT, and IMAT attenuation increased risk, whereas higher bone attenuation and skeletal muscle volume index reduced risk, after adjustment for established factors and other body composition measures.
Impact: This work unlocks prognostic biomarkers from routinely acquired CTAC scans without extra imaging or radiation, offering scalable risk stratification. It integrates multi-tissue volumetrics and attenuation with rapid, automated processing across centres.
Clinical Implications: Health systems can incorporate automated CTAC body composition metrics into reports to enhance mortality risk stratification after nuclear perfusion imaging, potentially guiding targeted interventions (e.g., sarcopenia management, cardiometabolic risk reduction).
Key Findings
- AI segmentation of CTAC scans quantified bone, skeletal muscle, SAT, IMAT, VAT, and EAT between T5–T11 in <2 minutes per scan without user interaction.
- High VAT attenuation (adjusted HR 2.39, 95% CI 1.92–2.96) and high EAT (1.55, 1.26–1.90) and IMAT attenuation (1.30, 1.06–1.60) were independently associated with higher all-cause mortality.
- High bone attenuation (HR 0.77, 0.62–0.95) and higher skeletal muscle volume index (HR 0.56, 0.44–0.71) were associated with lower mortality.
- Findings were adjusted for established risk factors and 18 other body composition measures, demonstrating independent prognostic value.
Methodological Strengths
- Large multicentre cohort with 9,918 patients and standardized AI processing across four sites
- Comprehensive adjustment for established risk factors and multiple body composition measures
Limitations
- Retrospective design with median 2.48-year follow-up limits causal inference
- CTAC scans cover T5–T11 only; generalizability to other populations and imaging protocols requires validation
Future Directions: Prospective validation with predefined thresholds, integration into clinical reporting pipelines, and trials testing interventions guided by these biomarkers (e.g., sarcopenia/ectopic fat modification).
BACKGROUND: CT attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only used for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and to evaluate these measures for all-cause mortality risk stratification. METHODS: We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry at four sites (Yale
2. Cardiologist follow-up and improved outcomes of heart failure: a French nationwide cohort.
Among 655,919 French HF patients, simple stratification by prior HF hospitalization and loop diuretic use strongly predicted one-year mortality. Notably, a single cardiology visit in the prior year was associated with a 6–9% absolute mortality reduction (NNC 11–16), and optimal visit frequency scaled with severity (1, 2–3, and 4 visits across strata).
Impact: This nationwide analysis provides actionable, risk-based targets for cardiology follow-up frequency, revealing large, scalable mortality benefits in routine care.
Clinical Implications: Health systems should implement risk-stratified scheduling ensuring at least annual cardiology visits for low-risk HF and more frequent visits (2–4 annually) for higher-risk strata to reduce avoidable deaths.
Key Findings
- One-year all-cause mortality was 15.9% overall, ranging from 8.0% (NoHFH/LD-) to 25.0% (HFH ≤1y).
- Compared with NoHFH/LD-, mortality risk increased stepwise: NoHFH/LD+ HR 1.61, HFH >1y HR 1.83, HFH ≤1y HR 2.32 (P<.0001).
- Forty percent of patients had no cardiology visit during 2020 across all strata.
- A single cardiology visit in 2019 was associated with a 6–9% absolute reduction in 1-year mortality in 2020 (NNC 11–16). Optimal annual visits: 1 (NoHFH/LD-), 2–3 (NoHFH/LD+ and HFH >1y), 4 (HFH ≤1y).
Methodological Strengths
- Nationwide cohort with extremely large sample size and simple, reproducible stratification
- Use of survival models with clinically interpretable metrics (absolute risk reduction, NNC)
Limitations
- Observational design with potential residual confounding and reliance on administrative data
- Study period includes 2020, potentially influenced by COVID-19 service disruptions
Future Directions: Pragmatic trials to test stratified follow-up intensity, integration with telemedicine and multidisciplinary HF programs, and evaluation of cost-effectiveness and equity.
BACKGROUND AND AIMS: Outpatient cardiology follow-up is the cornerstone of heart failure (HF) management, requiring adaptation based on patient severity. However, risk stratification using administrative data is scarce, and the association between follow-up and prognosis according to patient risk has yet to be described at a population level. This study aimed to describe prognosis and management across different strata using simple criteria, including diuretic use and prior HF hospitalization (HFH). METHODS: This nationwide cohort included all French patients reported as having HF
3. Impact of Digoxin Utilization on Gastrointestinal Bleeding in Patients With Continuous-Flow Left Ventricular Assist Devices: A Systematic Review and Meta-Analysis.
Across 14,917 CF-LVAD patients in four studies, digoxin use was associated with a nonsignificant trend toward lower overall GIB but a significant reduction in GI angiodysplasia-related GIB (HR 0.33). Findings align with a mechanistic rationale of HIF-1α inhibition.
Impact: This meta-analysis highlights a potentially modifiable and common complication in CF-LVAD care with a widely available drug, motivating randomized trials.
Clinical Implications: Clinicians may consider digoxin in CF-LVAD patients at high risk for GIAD-related bleeding while awaiting randomized evidence, balancing benefits with known digoxin toxicities and patient-specific factors.
Key Findings
- Meta-analysis of four studies (n=14,917) comparing digoxin users (n=2,742) vs nonusers (n=12,175).
- Overall GIB risk showed a nonsignificant reduction with digoxin (HR 0.70; 95% CI 0.49–1.01).
- GI angiodysplasia-related GIB was significantly reduced with digoxin (HR 0.33; 95% CI 0.13–0.82).
- CF-LVAD types included axial (HeartMate II, 78%) and centrifugal (HeartMate 3/HeartWare, 22%).
Methodological Strengths
- PROSPERO-registered meta-analysis with a large aggregate sample size
- Mechanism-informed hypothesis (HIF-1α inhibition) and focused outcome (GIAD-related GIB)
Limitations
- Only four observational studies; susceptibility to residual confounding and heterogeneity
- Lack of randomized data; follow-up durations varied across studies
Future Directions: Conduct randomized controlled trials of digoxin for prevention of GIAD-related bleeding in CF-LVAD, assess dose-response and device-specific effects, and incorporate mechanistic biomarkers (e.g., HIF-1α).
Continuous-flow left ventricular assist devices (CF-LVADs) improve quality of life and survival in patients with advanced heart failure but are frequently complicated by gastrointestinal bleeding (GIB). Reduced pulsatile flow may induce mucosal hypoxia, upregulating factors such as hypoxia-inducible factor (HIF)-1α and triggering neo-angiogenesis, leading to the development of gastrointestinal angiodysplasias (GIADs), a common cause of GIB. Digoxin inhibits HIF-1α and may prevent GIAD development, although its impact on the incidence of GIB remains uncertain. This meta-analysis (PROSPERO ID: CRD42024626222) evaluated the association between digoxin use and GIB occurrence (primary outcome) in patients with CF-LVADs. Research articles including adults with CF-LVADs, comparing digoxin users versus nonusers were included. Overall, four studies were included (n = 14,917; age 55 ± 13 years, 21% female) with 2,742 patients in the digoxin group and 12,175 in the no-digoxin group.