European Heart Journal - Cardiovascular Imaging最新文献

Aims: The AI-CVD initiative seeks to extract actionable insights from coronary artery calcium (CAC) scans beyond the traditional CAC score. We previously demonstrated that AI-derived cardiac chamber volumes from CAC scans predict incident heart failure (HF). We aimed to evaluate whether left-to-right cardiac chamber volume ratios outperform chamber volumes in predicting HF.

Method and results: We used AI-CVD cardiac chambers volumetry data from CAC scans of 5,732 asymptomatic Multi-Ethnic Study of Atherosclerosis (MESA) participants (age 62.2±10.3 years; 47.7% male). Left-to-right ventricular (LV/RV), atrial (LA/RA), and left atrial-to-right ventricular (LA/RV) volume ratios were evaluated using multivariable Cox models and feature selection techniques. External validation was performed in the Framingham Heart Study Offspring (FHS-O) cohort (N=1,052, age:58.3±8.3, 42.9% male). During a median follow-up of 17.7 years in MESA, 369 participants (6.3%) developed HF. Elevated ratios (≥75th & ≥95th percentile) of LV/RV, LA/RA, and LA/RV were strongly associated with incident HF: hazard ratio (HR) for ≥95th percentile were 4.04 (95% CI:2.89-5.65), 2.90 (95% CI:2.07-4.06), and 2.61 (95% CI:1.87-3.46), respectively. Among participants with normal LV sizes (interquartile-range), LV/RV ≥95th significantly predicted HF (HR:2.34; 95% CI:1.29-4.25). In FHS-O (median follow-up 14.4 years), 56 HF events (5.3%) occurred. LV/RV ≥75th percentile was significantly associated with HF (HR:2.23; 95% CI:1.16-4.30), whereas LA/RA was not (HR:1.22; 95% CI:0.65-2.29). Feature selection techniques identified LV/RV as the strongest predictor.

Conclusion: In these two prospective cohorts, AI-derived LV/RV ratio from CAC scans strongly predicted HF. New clinical trials guided by these imaging biomarkers are warranted to establish their clinical utility.

Aims: Cardiovascular risk factors predict adverse clinical outcomes, including acute coronary syndromes (ACS). A recent study showed close correlation between the number of modifiable risk factors and plaque vulnerability. However, the relationship between non-modifiable risk factors (NMRFs) and plaque characteristics has been unexplored. This study aimed to correlate the number of NMRFs with coronary plaque characteristics defined by optical coherence tomography (OCT).

Methods and results: Patients with ACS were divided into four groups based on the number of NMRFs (age ≥70 years, male sex and family history of coronary artery disease). Lesion characteristics in both culprit and non-culprit plaques were analyzed. A total of 2345 plaques (1663 culprit and 682 non-culprit plaques) were analyzed. In culprit plaques, the prevalence of both OCT-defined vulnerable features and plaque rupture did not increase as the number of NMRFs increased (p-trend > 0.05 for each vulnerable feature and p-trend for plaque rupture: 0.856). In non-culprit plaques, no association between the number of NMRFs and vulnerable features was observed. However, the number of NMRF was directly correlated with calcified plaque (OR 2.60, 95% CI 2.01-3.37, p < 0.001) and inversely correlated with erosion (OR 0.72, 95% CI 0.61-0.84, p < 0.001).

Conclusions: In patients with ACS, the burden of NMRFs was not correlated with OCT-defined plaque vulnerability or the prevalence of plaque rupture, both at culprit and non-culprit lesions. Conversely, a higher number of NMRF was linked to a greater probability of calcified plaque and a lower probability of plaque erosion.

Background: Assessing cardiac function is critical for managing cardiovascular disease, guiding treatment, monitoring progression, and risk stratification. While left ventricular ejection fraction (LVEF) is firmly established, it has limitations. Myocardial contraction fraction (MCF) - the ratio of stroke volume to myocardial volume, is simple to compute without additional analysis and offers a promising alternative to LVEF.

Methods: MCF was assessed across four datasets spanning healthy controls and chronic structural cardiac disease, with direct comparison to LVEF. Association between age, sex and MCF were investigated in 3,541 healthy subjects from the UK Biobank and sex-specific reference ranges derived. Several cohorts were recruited to investigate the discriminative power of MCF and LVEF between health and physiological adaption (n=278 veteran athletes), pathological hypertrophy (hypertrophic cardiomyopathy, amyloid, Fabry, severe aortic stenosis, and hypertension; n=633) and dilatation (n=103 dilated cardiomyopathy). Ability to track disease severity was assessed by looking at 41,558 subjects from the UK Biobank. Finally, prognostication was assessed on 1,277 consecutive patients from an independent external dataset. All images were analysed using the same validated AI algorithm.

Results: MCF varied with sex (mean MCF: 0.94 male; 1.1 female) but not age. Sex-specific reference ranges were established: [0.68-1.20] for male, [0.82-1.38] for female. MCF decreased in pathological disease (e.g. mean MCF: 0.72 HCM; 0.69 severe AS; 0.5 amyloid; 0.9 hypertension) but there was no significant decrease in LVEF other than in amyloid (mean EF: 76% HCM; 64% severe AS; amyloid 56%; 65% hypertension). Both MCF and EF decreased in DCM (EF 34%; MCF 0.58). MCF decreased with worsening hypertension, whereas LVEF increased (P<0.05). MCF had superior prognostic ability to LVEF (MCF vs LVEF: HR=0.772 vs HR=0.816; χ²=198 vs χ²=151; p<0.001).

Conclusions: We established MCF reference ranges, showing superior performance for detecting early disease and tracking progression compared to LVEF. MCF offers enhanced prognostic utility, complementing established metrics of LV function.

Aims: Cardiac computed tomography (CCT) assesses coronary anatomy and enables delayed phase imaging, including extracellular volume fraction (ECV) for diffuse myocardial fibrosis and late iodine enhancement (LIE) for focal myocardial replacement fibrosis. ECV and LIE reflect distinct pathological processes; combining these measures may improve subclinical myocardial injury detection. This study evaluated LIE and ECV in patients undergoing CCT for coronary artery assessment and examined their association with clinical outcomes. Primary outcome was a composite of all-cause death and unplanned cardiovascular hospitalizations; secondary outcome was cardiovascular events, defined as cardiac death and unplanned cardiovascular hospitalization.

Methods and results: We analyzed 1,207 consecutive patients who underwent CCT between January 2020 and September 2022. Patients were categorized into four groups based on the presence of LIE and elevated ECV. Associations with LIE and ECV, individually and combined, were assessed using Cox proportional hazards models. Of 1,305 patients, 1,207 met inclusion criteria and were followed for a mean of 26.0 ± 19.1 months. Kaplan-Meier analysis demonstrated a stepwise increase in risk across the four groups, with those having LIE and elevated ECV showing the highest cumulative incidence of composite events (log-rank p = 0.027). This group had increased risk for the composite outcome (HR 1.84, 95% confidence interval [CI] 1.22-2.79) and cardiovascular events (HR 2.67, 95% CI 1.32-5.41).

Conclusion: In patients undergoing CCT for coronary artery evaluation, coexistence of LIE and elevated ECV is associated with higher risk of cardiovascular events and their assessment may provide synergistic prognostic value.

Aims: Cardiac magnetic resonance (CMR) compliments transthoracic echocardiography (TTE) for heart valve evaluation, however TTE remains more widely available. We sought to optimize transthoracic echocardiographic (TTE) quantification of significant aortic regurgitation (AR) by developing TTE-based algorithms to identify CMR-defined severe AR.

Methods and results: Patients with ≥moderate-to-severe AR undergoing both TTE and CMR within 3-months were studied. A historical cohort 2006-2018 (n = 193) was used to derive TTE-based decision tree regression algorithms to best identify severe AR based on holodiastolic flow reversal (HDR) using CMR, then validated in a prospective AR cohort (n = 97) during 2019-2021.Mean AR regurgitant volumes, fractions and proportions with HDR by TTE/CMR were 48/31 mL, 41/25% and 43%/27% for the historical derivation cohort and 51/37 mL, 47/29% and 54%/41% for the prospective validation cohort. Decision-tree analyses found regurgitant volume≥45 mL and left ventricular end-diastolic volume index (LVEDVi) ≥ 93 mL/m2 by TTE to best identify CMR-derived severe AR. Areas under curves (95%CIs) of the novel algorithms (PISA and Doppler methods) compared with current guidelines criteria for detecting CMR-derived severe AR were 0.80 (0.71-0.88) and 0.74 (0.65-0.83) versus 0.72 (0.63-0.81) in the derivation cohort, and 0.76 (0.66-0.87) and 0.71 (0.61-0.82) versus 0.58 (0.46-0.70) in the validation cohort; and for predicting left ventricular remodeling where follow-up TTE wad available were 0.65 (0.58-0.73) and 0.62 (0.54-0.70) versus 0.53 (0.45-0.61) respectively.

Conclusion: Novel TTEs algorithm increased TTE accuracy of identifying significant AR defined by CMR especially in the prospective cohort, compared to the current guidelines criteria, and was able to modestly discriminate LV remodeling.