Daily Cardiology Research Analysis
Analyzed 74 papers and selected 3 impactful papers.
Summary
A multinational randomized trial showed that omitting defibrillation testing after S-ICD implantation using the PRAETORIAN score is noninferior and reduces procedural risk. Prospective multi-ethnic metabolomics identified distinct HFpEF vs HFrEF metabolic signatures that improve prognostication. An AI-derived, age-adjusted breast arterial calcification percentile nomogram independently predicted cardiovascular events and reclassified risk beyond ASCVD scores in over 21,000 women.
Research Themes
- Procedure optimization in electrophysiology (PRAETORIAN-guided omission of DF testing)
- Metabolomics-based risk stratification in heart failure phenotypes
- Opportunistic cardiovascular risk assessment from mammography (BAC percentiles)
Selected Articles
1. Subcutaneous Defibrillator Implantation With or Without Defibrillation Test: The Primary Results of the Randomized PRAETORIAN-DFT Trial.
In a 37-center randomized trial (n=965), PRAETORIAN score–guided omission of defibrillation testing after S-ICD implantation was noninferior for first-shock efficacy (1.7% vs 2.3% failed first shocks) and did not increase mortality. Testing-related complications occurred in 1.7% of those undergoing DF testing, supporting a safer, streamlined post-implant protocol.
Impact: This pragmatic RCT provides high-level evidence to safely omit DF testing using a validated radiographic score, with immediate implications for procedural risk reduction and workflow efficiency in S-ICD programs.
Clinical Implications: Centers can adopt PRAETORIAN score–guided omission of DF testing after S-ICD implantation to reduce anesthesia time and procedural risk without compromising defibrillation efficacy, while standardizing implant quality checks via chest X-rays.
Key Findings
- Failed first shock rates were 1.7% (no-DF) vs 2.3% (DF), meeting noninferiority (p<0.001).
- No differences in all-cause mortality (HR 0.9) or arrhythmic death (HR 0.4).
- DF testing–related complications occurred in 1.7% of tested patients.
- Postoperative S-ICD revisions for positioning were identical (n=2 per group).
Methodological Strengths
- Multinational randomized design with long median follow-up (41 months).
- Pre-specified noninferiority margin and clinically meaningful surrogate endpoint.
Limitations
- Primary endpoint is a surrogate (failed first shock) rather than mortality.
- Industry funding; applicability may vary across implantation techniques and operators.
Future Directions: Evaluate health-economic impact, broader real-world implementation, and integration of automated PRAETORIAN scoring in clinical workflows; assess long-term arrhythmic outcomes.
BACKGROUND: To improve survival in patients at risk of sudden cardiac death, subcutaneous ICDs (S-ICDs) require optimal implant positioning for effective shocks. Defibrillation (DF) testing is recommended but carries serious risks. The PRAETORIAN score predicts defibrillation outcomes based on chest X-rays. The PRAETORIAN-DFT trial evaluated whether omission of DF testing guided by the PRAETORIAN score is non-inferior for first-shock efficacy. METHODS: In this multinational trial, S-ICD patients from 37 centers were randomized to DF testing or no DF testing. In the No-DF testing group, the PRAETORIAN score was evaluated before discharge. The primary endpoint was failed first shock for spontaneous ventricular arrhythmias, as a surrogate for defibrillation success, tested for non-inferiority with a 3% absolute risk margin. Secondary endpoints included mortality, potential DF testing-related complications, and S-ICD revisions. RESULTS: The included 965 patients (No-DF testing, n=483;DF testing, n=482) were followed for a median of 41 months. Failed first shock for spontaneous ventricular arrhythmia occurred in 1.7% of the No-DF testing group versus 2.3% of the DF testing group (-0.6%, 95% CI [ -2.6 to 1.4]; p<0.001). There were no significant differences in all-cause mortality (HR 0.9 [95% CI 0.6-1.4]) or arrhythmic death (HR 0.4 [95% CI 0.04-3.4]). Potential DF testing-related complications occurred in 1.7% in the DF testing group. Postoperative S-ICD revisions due to inadequate positioning were identical between groups (n=2 each). CONCLUSIONS: PRAETORIAN score-guided omission of DF testing- after S-ICD implantation did not increase the risk of failed first shocks for spontaneous ventricular arrhythmias and reduced procedural risk without increasing S-ICD revisions. (Funded by Boston Scientific; PRAETORIAN-DFT).
2. Differential metabolomic prediction of outcomes in HFpEF and HFrEF: a prospective international multi-ethnic cohort study.
Across two prospective cohorts (n=1,520), metabolic network indices differentially predicted outcomes in HFpEF (NO/glycemic control protective; purine adverse) vs HFrEF (acylcarnitine adverse; amino acids protective). Adding orotic acid to a strong baseline model improved 2-year mortality discrimination (AUROC +0.01 to +0.05), reinforcing metabolomics for risk stratification and therapeutic targeting.
Impact: This study links mechanistic metabolic pathways to phenotype-specific risk in HF and demonstrates generalizable, incremental prognostic value, informing biomarker panels and potential metabolic interventions.
Clinical Implications: Metabolomics panels could refine risk stratification in HFpEF vs HFrEF and guide phenotype-targeted therapies (e.g., augment NO signaling in HFpEF; address fatty acid oxidation dysregulation in HFrEF).
Key Findings
- Every 20% higher netNO-pEF associated with lower risk in HFpEF (HR 0.64 and 0.40 across PEOPLE/SHOP).
- Higher netPurine-pEF predicted worse HFpEF outcomes (HR 1.71 and 1.96).
- In HFrEF, higher netACa-rEF predicted worse outcomes (HR 1.48 and 1.39), while higher netAA-rEF predicted better outcomes (HR 0.75 and 0.51).
- Adding orotic acid improved 2-year mortality prediction (AUROC 0.82→0.83 in PEOPLE; 0.74→0.79 in SHOP).
Methodological Strengths
- Prospective, multi-ethnic cohorts with external validation across two datasets.
- Advanced analytics (WGCNA, multivariable hazards, win-ratio) with concordant results.
Limitations
- Observational design limits causal inference; residual confounding possible.
- Clinical translation requires standardized, scalable metabolite assays and thresholds.
Future Directions: Prospective interventional studies targeting identified pathways (e.g., NO bioavailability in HFpEF) and integration of metabolomic indices into clinical risk models and trial enrichment strategies.
AIMS: Metabolic dysregulation accompanies HF, yet coordinated biochemical shifts are rarely quantified for prognostication. We investigated whether early metabolic profiles predict morbidity and mortality in HF with preserved (HFpEF) or reduced ejection fraction (HFrEF). METHODS AND RESULTS: In the PEOPLE and SHOP cohorts (n = 1,520), 288 plasma metabolites were quantified by tandem mass spectrometry. Weighted co-expression network analysis produced metabolic indices that were regressed against all-cause mortality and composite outcomes (death or readmission) using multivariable proportional hazards and win-ratio models. In HFpEF, metabolic indices of nitric oxide signalling and glycaemic control (netNO-pEF) and purine metabolism (netPurine-pEF) predicted outcomes. In HFrEF, indices of acylcarnitine (netACa-rEF) and amino acids (netAA-rEF) predicted outcomes. Every 20% higher netNO-pEF was associated with adjusted hazard ratios (HR20%higher) of 0.64 [95% CI 0.49-0.84] in PEOPLE-HFpEF and 0.40 [0.20-0.83] in SHOP-HFpEF; HR20%higher for netPurine-pEF were 1.71 [1.30-2.24] in PEOPLE-HFpEF and 1.96 [1.08-3.55] in SHOP-HFpEF; HR20%higher for netACa-rEF were 1.48 [1.23-1.79] in PEOPLE-HFrEF and 1.39 [1.15-1.68] in SHOP-HFrEF; HR20%higher for netAA-rEF were 0.75 [0.62-0.91] in PEOPLE-HFrEF and 0.51 [0.39-0.67] in SHOP-HFrEF. Adjusted inverse win-ratios were concordant: 1/WR20%higher for netACa-rEF were 1.40 [1.19-1.64] in PEOPLE-HFrEF and 1.17 [1.02-1.34] in SHOP-HFrEF; 1/WR20%higher for netNO-pEF were 0.74 [0.54-1.00] in PEOPLE-HFrEF and 0.55 [0.33-0.89] in SHOP-HFrEF. netNO-pEF was negatively associated with body surface area among HFpEF with concomitant hypertension and diabetes, but only in SHOP-HFpEF. Adding orotic acid, a constituent metabolite of netNO-pEF, to a base model containing five risk indices (MAGGIC score, NT-proBNP, hsTnT, GDF15, and E/e' ratio) incrementally improved 2-year mortality prediction (AUROC: 0.82→0.83 in PEOPLE; 0.74→0.79 in SHOP). CONCLUSION: Differential metabolic signatures tied to metabolic inflammation in HFpEF and impaired energy metabolism in HFrEF enhance risk stratification and point to therapeutic targets.
3. A Novel Breast Arterial Calcification Age-Based Percentile Nomogram for the Incremental Prediction of Incidental Cardiovascular Events.
In 21,514 women without known CVD, AI-quantified age-adjusted BAC percentiles independently predicted MACE over 4.7 years and improved discrimination (C-statistic +0.04) and net reclassification (NRI 5%) beyond ASCVD scores, including in low-risk women.
Impact: Transforms routine mammography into an opportunistic cardiovascular risk assessment, with scalable AI quantification and age-adjusted interpretation that meaningfully reclassifies risk.
Clinical Implications: Incorporate age-adjusted BAC percentiles into preventive cardiology workflows for women to flag elevated risk even at low ASCVD scores, prompting targeted counseling and risk factor modification.
Key Findings
- BAC was present in 22.7% and increased with age (8% <50y; 61% >70y).
- Each 10-percentile increase in BAC raised MACE risk by 17% (adjusted HR 1.17, P<0.001).
- BAC improved risk prediction beyond ASCVD with NRI of 5% and C-statistic increase from 0.67 to 0.71.
- Risk reclassification benefits were observed across low, intermediate, and high ASCVD risk strata.
Methodological Strengths
- Large multicenter cohort with AI-based quantitative BAC assessment.
- Robust adjustment and competing risks analyses; clinically meaningful reclassification metrics.
Limitations
- Retrospective observational design susceptible to confounding and selection bias.
- Generalizability may depend on imaging protocols and AI algorithm performance across vendors.
Future Directions: Prospective validation, integration into risk calculators, and implementation studies to assess impact on preventive therapy initiation and outcomes.
BACKGROUND: Breast arterial calcification (BAC) detected on routine mammography is an emerging marker of cardiovascular risk in women. However, substantial age-related variability limits its clinical interpretability. Age-adjusted nomograms may improve risk stratification and communication. OBJECTIVES: This study aims to determine whether age-adjusted BAC percentiles derived from mammography predict major adverse cardiovascular events (MACE) independent of atherosclerotic cardiovascular disease (ASCVD) risk scores. METHODS: In this multicenter retrospective cohort study, 21,514 women without known cardiovascular disease and aged ≥40 years (57 ± 12 years) from sites in the United States and Australia underwent screening mammography and ASCVD risk assessment. BAC was quantified using artificial intelligence (cmAngio research edition, CureMetrix Inc) and expressed as age-adjusted percentiles. The primary outcome was MACE (death, ischemic heart disease, stroke, or heart failure). Associations between BAC percentiles with MACE were adjusted for established cardiovascular risk factors using Cox and competing risk regression methods, and incremental predictive value was evaluated. RESULTS: BAC was present in 22.7% of women, increasing with age (8%: <50 years; 61%: >70 years). During a mean follow-up of 4.7 years, 828 MACE (3.8%) occurred. Each 10-percentile increase in BAC was associated with a 17% relative increase in MACE risk (adjusted HR [aHR]: 1.17 [95% CI: 1.015-1.019]; P < 0.001), independent of conventional risk factors. Associations remained significant for each MACE component in competing risk models (all P < 0.001). Women with low ASCVD risk (80% of cohort) had significantly increased MACE with both BAC percentile less than median (aHR: 1.66 [95% CI: 1.35-2.04], P < 0.001) and more than median (aHR: 2.31 [95% CI: 1.82-2.93], P < 0.001). Women with intermediate and high ASCVD risk had greater MACE when BAC was more than median (intermediate aHR: 1.40 [95% CI: 1.07-1.82], P = 0.01; high aHR: 1.65 [95% CI: 1.24-2.21], P < 0.001). The addition of BAC to ASCVD risk score appropriately up-classified 9% of individuals with MACE and down-classified 3% of individuals without events, resulting in an overall net reclassification index of 5% ± 1%. The C-statistic for the clinical model improved from 0.67 to 0.71 (Δ 0.04 [95% CI: 0.01-0.07]; P = 0.042) with addition of BAC. CONCLUSIONS: Age-adjusted BAC independently predicts cardiovascular events beyond traditional ASCVD risk scores and reclassifies low- and intermediate-risk individuals. Integration of BAC into cardiovascular risk assessment frameworks may facilitate early identification of at-risk women.