DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR.

BACKGROUND AND AIMS: This study investigated the additional prognostic value of epicardial adipose tissue (EAT) volume for major adverse cardiovascular events (MACE) in patients undergoing stress cardiac magnetic resonance (CMR) imaging. METHODS: 730 consecutive patients [mean age: 63 ± 10 years; 616 men] who underwent stress CMR for known or suspected coronary artery disease were randomly divided into derivation (n = 365) and validation (n = 365) cohorts. MACE was defined as non-fatal myocardial infarction and cardiac deaths. A deep learning algorithm was developed and trained to quantify EAT volume from CMR. EAT volume was adjusted for height (EAT volume index). A composite CMR-based risk score by Cox analysis of the risk of MACE was created. RESULTS: In the derivation cohort, 32 patients (8.7 %) developed MACE during a follow-up of 2103 days. Left ventricular ejection fraction (LVEF) < 35 % (HR 4.407 [95 % CI 1.903-10.202]; p<0.001), stress perfusion defect (HR 3.550 [95 % CI 1.765-7.138]; p<0.001), late gadolinium enhancement (LGE) (HR 4.428 [95%CI 1.822-10.759]; p = 0.001) and EAT volume index (HR 1.082 [95 % CI 1.045-1.120]; p<0.001) were independent predictors of MACE. In a multivariate Cox regression analysis, adding EAT volume index to a composite risk score including LVEF, stress perfusion defect and LGE provided additional value in MACE prediction, with a net reclassification improvement of 0.683 (95%CI, 0.336-1.03; p<0.001). The combined evaluation of risk score and EAT volume index showed a higher Harrel C statistic as compared to risk score (0.85 vs. 0.76; p<0.001) and EAT volume index alone (0.85 vs.0.74; p<0.001). These findings were confirmed in the validation cohort. CONCLUSIONS: In patients with clinically indicated stress CMR, fully automated EAT volume measured by deep learning can provide additional prognostic information on top of standard clinical and imaging parameters.

Early Biological Valve Failure: Structural Valve Degeneration, Thrombosis, or Endocarditis?

Biological valve failure (BVF) is an inevitable condition that compromises the durability of biological heart valves (BHVs). It stems from various causes, including rejection, thrombosis, and endocarditis, leading to a critical state of valve dysfunction. Echocardiography, cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging play pivotal roles in the diagnostic multimodality workup of BVF. By providing a comprehensive overview of the pathophysiology of BVF and the diagnostic approaches in different clinical scenarios, this review aims to aid clinicians in their decision-making process. The significance of early detection and appropriate management of BVF cannot be overstated, as these directly impact patients' prognosis and their overall quality of life. Ensuring timely intervention and tailored treatments will not only improve outcomes but also alleviate the burden of this condition on patients' life. By prioritizing comprehensive assessments and adopting the latest advancements in diagnostic technology, medical professionals can significantly enhance their ability to manage BVF effectively.

Quantification of extracellular volume with cardiac computed tomography in patients with dilated cardiomyopathy.

BACKGROUND: Cardiac computed tomography (CCT) was recently validated to measure extracellular volume (ECV) in the setting of cardiac amyloidosis, showing good agreement with cardiovascular magnetic resonance (CMR). However, no evidence is available with a whole-heart single source, single energy CT scanner in the clinical context of newly diagnosed left ventricular dysfunction. Therefore, the aim of this study was to test the diagnostic accuracy of ECVCCT in patients with a recent diagnosis of dilated cardiomyopathy, having ECVCMR as the reference technique. METHODS: 39 consecutive patients with newly diagnosed dilated cardiomyopathy (LVEF <50%) scheduled for clinically indicated CMR were prospectively enrolled. Myocardial segment evaluability assessment with each technique, agreement between ECVCMR and ECVCCT, regression analysis, Bland-Altman analysis and interclass correlation coefficient (ICC) were performed. RESULTS: Mean age of enrolled patients was 62 â€‹± â€‹11 years, and mean LVEF at CMR was 35.4 â€‹± â€‹10.7%. Overall radiation exposure for ECV estimation was 2.1 â€‹± â€‹1.1 â€‹mSv. Out of 624 myocardial segments available for analysis, 624 (100%) segments were assessable by CCT while 608 (97.4%) were evaluable at CMR. ECVCCT demonstrated slightly lower values compared to ECVCMR (all segments, 31.8 â€‹± â€‹6.5% vs 33.9 â€‹± â€‹8.0%, p â€‹< â€‹0.001). At regression analysis, strong correlations were described (all segments, r â€‹= â€‹0.819, 95% CI: 0.791 to 0.844). On Bland-Altman analysis, bias between ECVCMR and ECVCCT for global analysis was 2.1 (95% CI: -6.8 to 11.1). ICC analysis showed both high intra-observer and inter-observer agreement for ECVCCT calculation (0.986, 95%CI: 0.983 to 0.988 and 0.966, 95%CI: 0.960 to 0.971, respectively). CONCLUSIONS: ECV estimation with a whole-heart single source, single energy CT scanner is feasible and accurate. Integration of ECV measurement in a comprehensive CCT evaluation of patients with newly diagnosed dilated cardiomyopathy can be performed with a small increase in overall radiation exposure.

Is red distribution width a valid tool to predict impaired iron transport in heart failure?

Background: Impaired iron transport (IIT) is a form of iron deficiency (ID) defined as transferrin saturation (TSAT) < 20% irrespective of serum ferritin levels. It is frequently observed in heart failure (HF) where it negatively affects prognosis irrespective of anaemia. Objectives: In this retrospective study we searched for a surrogate biomarker of IIT. Methods: We tested the predictive power of red distribution width (RDW), mean corpuscular volume (MCV) and mean corpuscular haemoglobin concentration (MCHC) to detect IIT in 797 non-anaemic HF patients. Results: At ROC analysis, RDW provided the best AUC (0.6928). An RDW cut-off value of 14.2% identified patients with IIT, with positive and negative predictive values of 48 and 80%, respectively. Comparison between the true and false negative groups showed that estimated glomerular filtration rate (eGFR) was significantly higher (p = 0.0092) in the true negative vs. false negative group. Therefore, we divided the study population according to eGFR value: 109 patients with eGFR ≥ 90 ml/min/1.73 m2, 318 patients with eGFR 60-89 ml/min/1.73 m2, 308 patients with eGFR 30-59 ml/min/1.73 m2 and 62 patients with eGFR < 30 ml/min/1.73 m2. In the first group, positive and negative predictive values were 48 and 81% respectively, 51 and 85% in the second group, 48 and 73% in the third group and 43 and 67% in the fourth group. Conclusion: RDW may be seen as a reliable marker to exclude IIT in non-anaemic HF patients with eGFR ≥60 ml/min/1.73 m2.

Role of impaired iron transport on exercise performance in heart failure patients.

AIMS: Impaired iron transport (IIT) occurs frequently in heart failure (HF) patients, even in the absence of anaemia and it is associated with a poor quality of life and prognosis. The impact of IIT on exercise capacity, as assessed by the cardiopulmonary exercise test (CPET), in HF is at present unknown. The aim of this article is to evaluate in HF patients the impact on exercise performance of IIT, defined as transferrin saturation (TSAT) <20%. METHODS AND RESULTS: We collected data of 676 patients hospitalized for HF. All underwent laboratory analysis, cardiac ultrasound, and CPET. Patients were grouped by the presence/absence of IIT and anaemia (haemoglobin <13 and <12 g/dL in male and female, respectively): Group 1 (G1) no anaemia, no IIT; Group 2 (G2) anaemia, no IIT; Group 3 (G3) no anaemia, IIT; Group 4 (G4) anaemia and IIT. Peak oxygen uptake (peakVO2) reduced from G1 to G3 and from G2 to G4 (G1: 1266 ± 497 mL/min, G2: 1011 ± 385 mL/min, G3: 1041 ± 395 mL/min, G4: 833 ± 241 mL/min), whereas the ventilation to carbon dioxide relationship slope (VE/VCO2 slope) increased (G1: 31.8 ± 7.5, G2: 34.5 ± 7.4, G3: 36.1 ± 10.2, G4: 37.5 ± 8.4). At multivariate regression analysis, peakVO2 independent predictors were anaemia, brain natriuretic peptide (BNP), and left ventricular ejection fraction, whereas VE/VCO2 slope independent predictors were IIT and BNP. CONCLUSION: In HF IIT is associated with exercise performance impairment independently from anaemia, and it is a predictor of elevated VE/VCO2 slope, a pivotal index of HF prognosis.