Daily Cardiology Research Analysis

BACKGROUND: Earlier identification of high coronary artery disease (CAD) risk individuals may enable more effective prevention strategies. However, existing 10-year risk frameworks are ineffective at earlier identification. We sought to understand how the variable importance of genomic and clinical factors across life stages may significantly improve lifelong CAD event prediction. METHODS: A longitudinal study was performed using data from 2 cohort studies: the FOS (Framingham Offspring Study) with 3588 participants aged 19 to 57 years and the UKB (UK Biobank) with 327 837 participants aged 40 years to 70 years. A total of 134 765 and 3 831 734 person-time years were observed in FOS and UKB, respectively. Hazard ratios for CAD were calculated for polygenic risk score (PRS) and clinical risk factors at each age of enrollment. The relative importance of PRS and pooled cohort equations in predicting CAD events was also evaluated by age groups. RESULTS: The importance of CAD PRS diminished over the life course, with a hazard ratio of 3.58 (95% CI, 1.39-9.19) at the age of 19 years in FOS and a hazard ratio of 1.51 (95% CI, 1.48-1.54) by the age of 70 years in UKB. Clinical risk factors exhibited similar age-dependent trends. PRS significantly outperformed pooled cohort equations in identifying subsequent CAD events in the 40- to 45-year age group, with 3.2-fold more appropriately identified events. Overall, adding PRS improved the area under the receiving operating curve of the pooled cohort equations by an average of +5.1% (95% CI, 4.9%-5.2%) across all age groups; among individuals <55 years, PRS augmented the area under the receiver operater curve (ROC) of the pooled cohort equations by 6.5% (95% CI, 5.5%-7.5%; CONCLUSIONS: Genomic and clinical risk factors for CAD display time-varying importance across the lifespan. The study underscores the added value of CAD PRS, particularly among individuals younger than 55 years, for enhancing early risk prediction and prevention strategies. All results are available at https://surbut.github.io/dynamicHRpaper/index.html.

BACKGROUND: Intravascular imaging (IVI) is widely recognized to improve outcomes after percutaneous coronary intervention (PCI). However, IVI is underutilized and is not yet established as a performance measure for quality PCI. METHODS: We examined temporal trends of IVI use for all PCIs performed at Veterans Affairs hospitals in the United States from 2010 to 2022 using retrospective observational cohorts. IVI was defined as intravascular ultrasound or optical coherence tomography. A contemporary subset of PCIs from 2020 to 2022 was used to examine clinical characteristics associated with IVI use and test the reliability of IVI as a pass/fail performance measure. We then used a generalized linear mixed model to estimate the proportion of IVI use variability attributable to the hospital, physician, and patient level. Cox proportional hazard models were used to assess the association of IVI with clinical outcomes at 1 year. RESULTS: IVI use increased from 12.3% in 2010 to 43.1% in 2022 in 136 071 PCIs included in the study. Among 22 918 PCIs in the contemporary cohort, IVI was more frequent with nonemergent presentations, chronic total occlusions, and left main lesions but usage variability was primarily attributable to hospital (54%) and physician (33%) levels. As a pass/fail performance measure, reliability was high (>0.96) at hospital and physician levels. However, IVI use was not associated with statistically significant differences in mortality or major adverse cardiovascular events in this cohort. CONCLUSIONS: IVI use for PCI is increasing rapidly at Veterans Affairs hospitals in the United States but with marked variation at the hospital and physician levels. IVI meets established criteria for an effective performance measure and should be measured and reported by local and national organizations to encourage further uptake. Ongoing training and quality improvement may be required to maximize the benefit of IVI as it is more widely utilized.

OBJECTIVES: To determine whether plaque composition analysis defined by cardiac CT can provide incremental prognostic value above coronary artery disease (CAD) burden markers in symptomatic patients with obstructive CAD. MATERIALS AND METHODS: Between 2009 and 2019, a multicentric registry included all consecutive symptomatic patients with obstructive CAD (at least one ≥ 50% stenosis on CCTA) and was followed for major adverse cardiovascular (MACE) defined by cardiovascular death or nonfatal myocardial infarction. Each coronary segment was scored visually for both the degree of stenosis and composition of plaque, which were classified as non-calcified, mixed, or calcified. To assess the prognostic value of each CCTA findings, different multivariable Cox regression models were used: model 1: clinical (traditional risk factors); model 2: model 1 + CAD burden (number of proximal segments with stenosis ≥ 50% + number of vessels with obstructive CAD); and model 3: model 2 + plaques feature (number of segments with non-calcified plaque). RESULTS: Of 2312 patients (mean age 70 ± 12 years, 46% men), 319 experienced a MACE (13.8%) (median follow-up: 6.7 years (5.9-9.1)). The number of proximal segments with ≥ 50% stenosis, the number of vessels with obstructive CAD, and the number of segments with non-calcified plaque were all independently associated with MACEs (all p < 0.001). The addition of plaque composition analysis (model 3) showed the best improvement in model discrimination and reclassification (C-statistic improvement = 0.03; net reclassification improvement = 28.6%; integrative discrimination index = 5.4%, all p < 0.001). CONCLUSIONS: In this population, the analysis of coronary plaque composition had an incremental prognostic value to predict MACEs above a model combining traditional risk factors and CAD burden. KEY POINTS: Question Several coronary computed tomography angiography (CCTA) studies have shown the potential interest of plaque composition analysis, which can be further evaluated. Findings In symptomatic patients with obstructive coronary artery disease (CAD), plaque composition analysis had an incremental prognostic value above a model combining traditional risk factors and CAD burden. Clinical relevance In symptomatic patients with obstructive CAD, plaque composition analysis using CCTA has a strong incremental prognostic value above a model combining traditional risk factors and CAD burden, thus translating into a more accurate long-term major adverse cardiovascular event prediction.