Special Issue: State of the Art of Cardiac Multimodality Imaging

Imagen cardiaca multimodalImatge cardíaca multimodalCardiac multimodality imagin

Combining 4D Flow MRI and Complex Networks Theory to Characterize the Hemodynamic Heterogeneity in Dilated and Non-dilated Human Ascending Aortas

Dilatació aòrtica; Aneurisma de l'aorta ascendent; Anàlisi espai-temporalDilatación aórtica; Aneurisma de la aorta ascendente; Análisis espacio-temporalAortic dilation; Ascending aorta aneurysm; Spatiotemporal analysisMotivated by the evidence that the onset and progression of the aneurysm of the ascending aorta (AAo) is intertwined with an adverse hemodynamic environment, the present study characterized in vivo the hemodynamic spatiotemporal complexity and organization in human aortas, with and without dilated AAo, exploring the relations with clinically relevant hemodynamic and geometric parameters. The Complex Networks (CNs) theory was applied for the first time to 4D flow magnetic resonance imaging (MRI) velocity data of ten patients, five of them presenting with AAo dilation. The time-histories along the cardiac cycle of velocity-based quantities were used to build correlation-based CNs. The CNs approach succeeded in capturing large-scale coherent flow features, delimiting flow separation and recirculation regions. CNs metrics highlighted that an increasing AAo dilation (expressed in terms of the ratio between the maximum AAo and aortic root diameter) disrupts the correlation in forward flow reducing the correlation persistence length, while preserving the spatiotemporal homogeneity of secondary flows. The application of CNs to in vivo 4D MRI data holds promise for a mechanistic understanding of the spatiotemporal complexity and organization of aortic flows, opening possibilities for the integration of in vivo quantitative hemodynamic information into risk stratification and classification criteria.Open access funding provided by Politecnico di Torino within the CRUI-CARE Agreement

Clinical pre-test probability for obstructive coronary artery disease: insights from the European DISCHARGE pilot study

Computed tomography angiography; Prevalence; Probability of diseaseAngiografía por tomografía computarizada; Prevalencia; Probabilidad de enfermedadAngiografia per tomografia computaritzada; Prevalència; Probabilitat de malaltiaObjectives To test the accuracy of clinical pre-test probability (PTP) for prediction of obstructive coronary artery disease (CAD) in a pan-European setting. Methods Patients with suspected CAD and stable chest pain who were clinically referred for invasive coronary angiography (ICA) or computed tomography (CT) were included by clinical sites participating in the pilot study of the European multi-centre DISCHARGE trial. PTP of CAD was determined using the Diamond-Forrester (D+F) prediction model initially introduced in 1979 and the updated D+F model from 2011. Obstructive coronary artery disease (CAD) was defined by one at least 50% diameter coronary stenosis by both CT and ICA. Results In total, 1440 patients (654 female, 786 male) were included at 25 clinical sites from May 2014 until July 2017. Of these patients, 725 underwent CT, while 715 underwent ICA. Both prediction models overestimated the prevalence of obstructive CAD (31.7%, 456 of 1440 patients, PTP: initial D+F 58.9% (28.1–90.6%), updated D+F 47.3% (34.2–59.9%), both p < 0.001), but overestimation of disease prevalence was higher for the initial D+F (p < 0.001). The discriminative ability was higher for the updated D+F 2011 (AUC of 0.73 95% confidence interval [CI] 0.70–0.76 versus AUC of 0.70 CI 0.67–0.73 for the initial D+F; p < 0.001; odds ratio (or) 1.55 CI 1.29–1.86, net reclassification index 0.11 CI 0.05–0.16, p < 0.001). Conclusions Clinical PTP calculation using the initial and updated D+F prediction models relevantly overestimates the actual prevalence of obstructive CAD in patients with stable chest pain clinically referred for ICA and CT suggesting that further refinements to improve clinical decision-making are needed.Open Access funding provided by Projekt DEAL. The DISCHARGE project is funded by the EU-FP7 Framework Programme (FP7 2007-2013, EC-GA 603266, EC-GA 603266) but the clinical sites did not receive any funding for the pilot study which was an own contribution by all. Only the research staff at the coordinator site received funding for coordinating the pilot study

Impact of SARS-Cov-2 infection in patients with hypertrophic cardiomyopathy: results of an international multicentre registry

COVID-19; SARS-CoV-2 infection; Heart failureCOVID-19; Infección por SARS-CoV-2; Insuficiencia cardiacaCOVID-19; Infecció per SARS-CoV-2; Insuficiència cardíacaAims To describe the natural history of SARS-CoV-2 infection in patients with hypertrophic cardiomyopathy (HCM) compared with a control group and to identify predictors of adverse events. Methods and results Three hundred and five patients [age 56.6 ± 16.9 years old, 191 (62.6%) male patients] with HCM and SARS-Cov-2 infection were enrolled. The control group consisted of 91 131 infected individuals. Endpoints were (i) SARS-CoV-2 related mortality and (ii) severe clinical course [death or intensive care unit (ICU) admission]. New onset of atrial fibrillation, ventricular arrhythmias, shock, stroke, and cardiac arrest were also recorded. Sixty-nine (22.9%) HCM patients were hospitalized for non-ICU level care, and 21 (7.0%) required ICU care. Seventeen (5.6%) died: eight (2.6%) of respiratory failure, four (1.3%) of heart failure, two (0.7%) suddenly, and three (1.0%) due to other SARS-CoV-2-related complications. Covariates associated with mortality in the multivariable were age {odds ratio (OR) per 10 year increase 2.25 [95% confidence interval (CI): 1.12–4.51], P = 0.0229}, baseline New York Heart Association class [OR per one-unit increase 4.01 (95%CI: 1.75–9.20), P = 0.0011], presence of left ventricular outflow tract obstruction [OR 5.59 (95%CI: 1.16–26.92), P = 0.0317], and left ventricular systolic impairment [OR 7.72 (95%CI: 1.20–49.79), P = 0.0316]. Controlling for age and sex and comparing HCM patients with a community-based SARS-CoV-2 cohort, the presence of HCM was associated with a borderline significant increased risk of mortality OR 1.70 (95%CI: 0.98–2.91, P = 0.0600). Conclusions Over one-fourth of HCM patients infected with SARS-Cov-2 required hospitalization, including 6% in an ICU setting. Age and cardiac features related to HCM, including baseline functional class, left ventricular outflow tract obstruction, and systolic impairment, conveyed increased risk of mortality.The project was funded by a grant from the Instituto de Salud Carlos III (ICSIII, COV20 00420). We should state that the SHaRe registry has been supported by an unrestricted grant from MyoKardia/Bristol Myers Squibb

Radiomics-Based Classification of Left Ventricular Non-compaction, Hypertrophic Cardiomyopathy, and Dilated Cardiomyopathy in Cardiovascular Magnetic Resonance

Miocardiopatía dilatada; Miocardiopatía hipertrófica; RadiómicaDilated cardiomyopathy; Hypertrophic cardiomyopathy; RadiomicsMiocardiopatia dilatada; Miocardiopatia hipertròfica; RadiòmicaLeft Ventricular (LV) Non-compaction (LVNC), Hypertrophic Cardiomyopathy (HCM), and Dilated Cardiomyopathy (DCM) share morphological and functional traits that increase the diagnosis complexity. Additional clinical information, besides imaging data such as cardiovascular magnetic resonance (CMR), is usually required to reach a definitive diagnosis, including electrocardiography (ECG), family history, and genetics. Alternatively, indices of hypertrabeculation have been introduced, but they require tedious and time-consuming delineations of the trabeculae on the CMR images. In this paper, we propose a radiomics approach to automatically encode differences in the underlying shape, gray-scale and textural information in the myocardium and its trabeculae, which may enhance the capacity to differentiate between these overlapping conditions. A total of 118 subjects, including 35 patients with LVNC, 25 with HCM, 37 with DCM, as well as 21 healthy volunteers (NOR), underwent CMR imaging. A comprehensive radiomics characterization was applied to LV short-axis images to quantify shape, first-order, co-occurrence matrix, run-length matrix, and local binary patterns. Conventional CMR indices (LV volumes, mass, wall thickness, LV ejection fraction—LVEF—), as well as hypertrabeculation indices by Petersen and Jacquier, were also analyzed. State-of-the-art Machine Learning (ML) models (one-vs.-rest Support Vector Machine—SVM—, Logistic Regression—LR—, and Random Forest Classifier—RF—) were used for one-vs.-rest classification tasks. The use of radiomics models for the automated diagnosis of LVNC, HCM, and DCM resulted in excellent one-vs.-rest ROC-AUC values of 0.95 while generating these results without the need for the delineation of the trabeculae. First-order and texture features resulted to be among the most discriminative features in the obtained radiomics signatures, indicating their added value for quantifying relevant tissue patterns in cardiomyopathy differential diagnosis.This publication was partially funded by the European Union's Horizon 2020 research and innovation euCanSHare project under grant agreement No 825903. KL received funding from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-099898-B-I00. AG has received funding from the Spanish Ministry of Science, Innovation and Universities (IJC2018-037349-I)

Fully Three-Dimensional Hemodynamic Characterization of Altered Blood Flow in Bicuspid Aortic Valve Patients With Respect to Aortic Dilatation: A Finite Element Approach

Bicuspid aortic valve; Congenital heart disease; Magnetic resonance imagingVàlvula aòrtica bicúspide; Cardiopatia congènita; Imatges per ressonància magnèticaVálvula aórtica bicúspide; Cardiopatía congénita; Imágenes de resonancia magnéticaBackground and Purpose: Prognostic models based on cardiovascular hemodynamic parameters may bring new information for an early assessment of patients with bicuspid aortic valve (BAV), playing a key role in reducing the long-term risk of cardiovascular events. This work quantifies several three-dimensional hemodynamic parameters in different patients with BAV and ranks their relationships with aortic diameter. Materials and Methods: Using 4D-flow CMR data of 74 patients with BAV (49 right-left and 25 right-non-coronary) and 48 healthy volunteers, aortic 3D maps of seventeen 17 different hemodynamic parameters were quantified along the thoracic aorta. Patients with BAV were divided into two morphotype categories, BAV-Non-AAoD (where we include 18 non-dilated patients and 7 root-dilated patients) and BAV-AAoD (where we include the 49 patients with dilatation of the ascending aorta). Differences between volunteers and patients were evaluated using MANOVA with Pillai's trace statistic, Mann–Whitney U test, ROC curves, and minimum redundancy maximum relevance algorithm. Spearman's correlation was used to correlate the dilation with each hemodynamic parameter. Results: The flow eccentricity, backward velocity, velocity angle, regurgitation fraction, circumferential wall shear stress, axial vorticity, and axial circulation allowed to discriminate between volunteers and patients with BAV, even in the absence of dilation. In patients with BAV, the diameter presented a strong correlation (> |+/−0.7|) with the forward velocity and velocity angle, and a good correlation (> |+/−0.5|) with regurgitation fraction, wall shear stress, wall shear stress axial, and vorticity, also for morphotypes and phenotypes, some of them are correlated with the diameter. The velocity angle proved to be an excellent biomarker in the differentiation between volunteers and patients with BAV, BAV morphotypes, and BAV phenotypes, with an area under the curve bigger than 0.90, and higher predictor important scores. Conclusions: Through the application of a novel 3D quantification method, hemodynamic parameters related to flow direction, such as flow eccentricity, velocity angle, and regurgitation fraction, presented the best relationships with a local diameter and effectively differentiated patients with BAV from healthy volunteers.This work was funded by ANID – Millennium Science Initiative Program – ICN2021_004 and ANID – Millennium Science Initiative Program – NCN17_129, CONICYT-FONDECYT Postdoctorado #3170737, ANID – FONDECYT Postdoctorado #3220266, ANID Ph. D. Scholarship 21170592, ANID FONDECYT de Iniciación en Investigación #11200481, ANID FONDECYT #1181057, ANID Ph. D. Scholarship 21180391, the Spanish Society of Cardiology (SEC/FEC-INV-CLI 20/015) and the Biomedical Research Networking Center on Cardiovascular Diseases (CIBERCV). AG has received funding from the Spanish Ministry of Science, Innovation and Universities (IJC2018-037349-I)

Biomarkers Predict In-Hospital Major Adverse Cardiac Events in COVID-19 Patients: A Multicenter International Study

COVID-19; Biomarkers; CreatinineCOVID-19; Biomarcadores; CreatininaCOVID-19; Biomarcadors; CreatininaBackground: The COVID-19 pandemic carries a high burden of morbidity and mortality worldwide. We aimed to identify possible predictors of in-hospital major cardiovascular (CV) events in COVID-19. Methods: We retrospectively included patients hospitalized for COVID-19 from 10 centers. Clinical, biochemical, electrocardiographic, and imaging data at admission and medications were collected. Primary endpoint was a composite of in-hospital CV death, acute heart failure (AHF), acute myocarditis, arrhythmias, acute coronary syndromes (ACS), cardiocirculatory arrest, and pulmonary embolism (PE). Results: Of the 748 patients included, 141(19%) reached the set endpoint: 49 (7%) CV death, 15 (2%) acute myocarditis, 32 (4%) sustained-supraventricular or ventricular arrhythmias, 14 (2%) cardiocirculatory arrest, 8 (1%) ACS, 41 (5%) AHF, and 39 (5%) PE. Patients with CV events had higher age, body temperature, creatinine, high-sensitivity troponin, white blood cells, and platelet counts at admission and were more likely to have systemic hypertension, renal failure (creatinine ≥ 1.25 mg/dL), chronic obstructive pulmonary disease, atrial fibrillation, and cardiomyopathy. On univariate and multivariate analysis, troponin and renal failure were associated with the composite endpoint. Kaplan–Meier analysis showed a clear divergence of in-hospital composite event-free survival stratified according to median troponin value and the presence of renal failure (Log rank p < 0.001). Conclusions: Our findings, derived from a multicenter data collection study, suggest the routine use of biomarkers, such as cardiac troponin and serum creatinine, for in-hospital prediction of CV events in patients with COVID-19

MRI Investigation of the Differential Impact of Left Ventricular Ejection Fraction After Myocardial Infarction in Elderly vs. Nonelderly Patients to Predict Readmission for Heart Failure

Acute heart failure; Acute myocardial infarction; ElderlyInsuficiència cardíaca aguda; Infart agut de miocardi; Gent granInsuficiencia cardíaca aguda; Infarto agudo de miocardio; AncianoBackground Patients with ST-segment elevation myocardial infarction (STEMI), especially elderly individuals, have an increased risk of readmission for acute heart failure (AHF). Purpose To study the impact of left ventricular ejection fraction (LVEF) by MRI to predict AHF in elderly (>70 years) and nonelderly patients after STEMI. Study Type Prospective. Population Multicenter registry of 759 reperfused STEMI patients (23.3% elderly). Field Strength/Sequence 1.5-T. Balanced steady-state free precession (cine imaging) and segmented inversion recovery steady-state free precession (late gadolinium enhancement) sequences. Assessment One-week MRI-derived LVEF (%) was quantified. Sequential MRI data were recorded in 579 patients. Patients were categorized according to their MRI-derived LVEF as preserved (p-LVEF, ≥50%), mildly reduced (mr-LVEF, 41%–49%), or reduced (r-LVEF, ≤40%). Median follow-up was 5 [2.33–7.54] years. Statistical Tests Univariable (Student's t, Mann–Whitney U, chi-square, and Fisher's exact tests) and multivariable (Cox proportional hazard regression) comparisons and continuous-time multistate Markov model to analyze transitions between LVEF categories and to AHF. Hazard ratios (HR) with 95% confidence intervals (CIs) were computed. P < 0.05 was considered statistically significant. Results Over the follow-up period, 79 (10.4%) patients presented AHF. MRI-LVEF was the most robust predictor in nonelderly (HR 0.94 [0.91–0.98]) and elderly patients (HR 0.94 [0.91–0.97]). Elderly patients had an increased AHF risk across the LVEF spectrum. An excess of risk (compared to p-LVEF) was noted in patients with r-LVEF both in nonelderly (HR 11.25 [5.67–22.32]) and elderly patients (HR 7.55 [3.29–17.34]). However, the mr-LVEF category was associated with increased AHF risk only in elderly patients (HR 3.66 [1.54–8.68]). Less transitions to higher LVEF states (n = 19, 30.2% vs. n = 98, 53%) and more transitions to AHF state (n = 34, 53.9% vs. n = 45, 24.3%) were observed in elderly than nonelderly patients. Data Conclusion MRI-derived p-LVEF confers a favorable prognosis and r-LVEF identifies individuals at the highest risk of AHF in both elderly and nonelderly patients. Nevertheless, an excess of risk was also found in the mr-LVEF category in the elderly group. Evidence Level 2. Technical Efficacy Stage 2.Grant sponsor: This work was supported by “Instituto de Salud Carlos III” and “Fondos Europeos de Desarrollo Regional FEDER” (grant numbers PI20/00637, PI15/00531, and CIBERCV16/11/00486, CIBERCV16/11/00420, CIBERCV16/11/00479, and CM21/00175 to V.M.-G.), Fundació La Marató TV3 (grant 20153030-31-32), La Caixa Banking Foundation (HR17-00527) and by Conselleria de Educación – Generalitat Valenciana (PROMETEO/2021/008). J.G. acknowledges financial support from the “Agencia Estatal de Investigación” (grant FJC2020-043981-I/AEI/10.13039/501100011033)