Fibrosis and expression of extracellular matrix proteins in human interventricular septum in aortic valve stenosis and regurgitation.

Valvular heart disease leads to ventricular pressure and/or volume overload. Pressure overload leads to fibrosis, which might regress with its resolution, but the limits and details of this reverse remodeling are not known. To gain more insight into the extent and nature of cardiac fibrosis in valve disease, we analyzed needle biopsies taken from the interventricular septum of patients undergoing surgery for valve replacement focusing on the expression and distribution of major extracellular matrix protein involved in this process. Proteomic analysis performed using mass spectrometry revealed an excellent correlation between the expression of collagen type I and III, but there was little correlation with the immunohistochemical staining performed on sister sections, which included antibodies against collagen I, III, fibronectin, sarcomeric actin, and histochemistry for wheat germ agglutinin. Surprisingly, the immunofluorescence intensity did not correlate significantly with the gold standard for fibrosis quantification, which was performed using Picrosirius Red (PSR) staining, unless multiplexed on the same tissue section. There was also little correlation between the immunohistochemical markers and pressure gradient severity. It appears that at least in humans, the immunohistochemical pattern of fibrosis is not clearly correlated with standard Picrosirius Red staining on sister sections or quantitative proteomic data, possibly due to tissue heterogeneity at microscale, comorbidities, or other patient-specific factors. For precise correlation of different types of staining, multiplexing on the same section is the best approach.

Coronary Microvascular Dysfunction in Patients With Heart Failure: Characterization of Patterns in HFrEF Versus HFpEF.

BACKGROUND: Coronary microvascular dysfunction (CMD) is involved in heart failure (HF) onset and progression, independently of HF phenotype and obstructive coronary artery disease. Invasive assessment of CMD might provide insights into phenotyping and prognosis of patients with HF. We aimed to assess absolute coronary flow, absolute microvascular resistance, myocardial perfusion, coronary flow reserve, and microvascular resistance reserve in patients with HF with preserved ejection fraction and HF with reduced ejection fraction (HFrEF). METHODS: Single-center, prospective study of 56 consecutive patients with de novo HF with nonobstructive coronary artery disease divided into HF with preserved ejection fraction (n=21) and HFrEF (n=35). CMD was invasively assessed by continuous intracoronary thermodilution and defined as coronary flow reserve <2.5. Left ventricular and left anterior descending artery-related myocardial mass was quantified by echocardiography and coronary computed tomography angiography. Myocardial perfusion (mL/min per g) was calculated as the ratio between absolute coronary flow and left anterior descending artery-related mass. RESULTS: Patients with HFrEF showed a higher left ventricular and left anterior descending artery-related myocardial mass compared with HF with preserved ejection fraction (P<0.010). Overall, 52% of the study population had CMD, with a similar prevalence between the 2 groups. In HFrEF, CMD was characterized by lower absolute microvascular resistance and higher absolute coronary flow at rest (functional CMD; P=0.002). CMD was an independent predictor of a lower rate of left ventricular reverse remodeling at follow-up. In patients with HF with preserved ejection fraction, CMD was mainly due to higher absolute microvascular resistance and lower absolute coronary flow during hyperemia (structural CMD; P≤0.030). CONCLUSIONS: Continuous intracoronary thermodilution allows the definition and characterization of patterns with distinct CMD in patients with HF and could identify patients with HFrEF with a higher rate of left ventricular reverse remodeling at follow-up.

Rationale and design of the ELEANOR trial early aortic valve surgery versus watchful waiting strategy in severe asymptomatic aortic regurgitation, ACRONYM: ELEANOR.

Background: The optimal treatment of patients with severe symptomatic aortic regurgitation (AR) is state-of-the-art surgery. Asymptomatic patients with advanced left ventricular (LV) dilatation and/or impaired ejection fraction should undergo surgical treatment, but there is no guidelines consensus on cut-off values for this recommendation. Multimodality imaging has brought new tools for the accurate selection of asymptomatic patients at risk of early clinical deterioration, however, prospective and randomized data are pending. Cardiac magnetic resonance (CMR)-derived AR quantification along with LV remodeling assessment appears to be the most accurate tool for a selection of such patients at risk. Trial design: The objective of our prospective and multicenter study is to determine whether patients at risk of early clinical deterioration as per CMR assessment will benefit from early surgical treatment. The study is designed as a superiority trial to demonstrate that early surgical treatment is safe and more effective than the standard treatment. A total of 217 asymptomatic patients with severe AR, but without current guidelines-based surgical indication, will be enrolled across all centers. We expect 24 % of patients identified as high clinical risk and therefore eligible for 1:1 randomization to early surgical treatment within 3 months or a watchful waiting strategy. Follow-up will be annual. We expect a complete restoration of LV size and function along with improved quality of life and physical performance in a short-term follow-up of 12 months. The primary endpoint will be a composite safety and efficacy with all criteria mandatory: 15 % or larger reduction of baseline CMR-derived LV end-diastolic volume index, LV ejection fraction >50 %, and no major adverse cardiovascular events. The annual follow-up will continue for a minimum of 4 years until the required number of endpoints is achieved to show a statistically significant difference in cardiovascular morbidity and mortality in early surgically treated patients. Conclusion: The ELEANOR trial is the first multicenter randomized controlled study to compare early surgical treatment with a watchful waiting strategy in asymptomatic patients with chronic severe AR at high risk of early clinical deterioration as per CMR assessment but without guidelines-based indications for surgical treatment.

Predictors of percutaneous coronary intervention derived from CCTA in patients with chronic coronary syndrome.

BACKGROUND: To identify anatomical and morphological plaque features predictors of PCI and create a multiparametric score to increase the predictive yield. Moreover, we assessed the incremental predictive value of FFRCT (Fractional Flow Reserve derived from CCTA) trans-lesion gradient (ΔFFRCT) when integrated into the score. METHODS: Observational cohort study including patients undergoing CCTA for suspected coronary artery disease, with FFRCT available, referred to invasive coronary angiogram and assessment of fractional flow reserve. Plaque analysis was performed using validated semi-automated software. Logistic regression was performed to identify anatomical and morphological plaque features predictive of PCI. Optimal thresholds were defined by area under the receiver-operating characteristics curve (AUC) analysis. A scoring system was developed in a derivation cohort (70 â€‹% of the study population) and tested in a validation cohort (30 â€‹% of patients). RESULTS: The overall study population included 340 patients (455 vessels), among which 238 patients (320 vessels) were included in the derivation cohort. At multivariate logistic regression analysis, absence of left main disease, diameter stenosis (DS), non-calcified plaque (NCP) volume, and percent atheroma volume (PAV) were independent predictors of PCI. Optimal thresholds were: DS â€‹≥ â€‹50 â€‹%, volume of NCP>113 â€‹mm3 and PAV>17 â€‹%. A weighted score (CT-PCI Score) ranging from 0 to 11 was obtained. The AUC of the score was 0.80 (95%CI 0.74-0.86). The integration of ΔFFRCT in the CT-PCI score led to a mild albeit not significant increase in the AUC (0.82, 95%CI 0.77-0.87, p â€‹= â€‹0.328). CONCLUSIONS: Plaque anatomy and morphology derived from CCTA could aid in identifying patients amenable to PCI.

Artificial Intelligence-Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome.

BACKGROUND: A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization. OBJECTIVES: This study sought to investigate the additive value of artificial intelligence-enabled quantitative coronary plaque and hemodynamic analysis (AI-QCPHA). METHODS: Among ACS patients who underwent coronary computed tomography angiography (CTA) from 1 month to 3 years before the ACS event, culprit and nonculprit lesions on coronary CTA were adjudicated based on invasive coronary angiography. The primary endpoint was the predictability of the risk models for ACS culprit lesions. The reference model included the Coronary Artery Disease Reporting and Data System, a standardized classification for stenosis severity, and high-risk plaque, defined as lesions with ≥2 adverse plaque characteristics. The new prediction model was the reference model plus AI-QCPHA features, selected by hierarchical clustering and information gain in the derivation cohort. The model performance was assessed in the validation cohort. RESULTS: Among 351 patients (age: 65.9 ± 11.7 years) with 2,088 nonculprit and 363 culprit lesions, the median interval from coronary CTA to ACS event was 375 days (Q1-Q3: 95-645 days), and 223 patients (63.5%) presented with myocardial infarction. In the derivation cohort (n = 243), the best AI-QCPHA features were fractional flow reserve across the lesion, plaque burden, total plaque volume, low-attenuation plaque volume, and averaged percent total myocardial blood flow. The addition of AI-QCPHA features showed higher predictability than the reference model in the validation cohort (n = 108) (AUC: 0.84 vs 0.78; P < 0.001). The additive value of AI-QCPHA features was consistent across different timepoints from coronary CTA. CONCLUSIONS: AI-enabled plaque and hemodynamic quantification enhanced the predictability for ACS culprit lesions over the conventional coronary CTA analysis. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary Computed Tomography Angiography and Computational Fluid Dynamics II [EMERALD-II]; NCT03591328).