Role of cardiac magnetic resonance in stratifying arrhythmogenic risk in mitral valve prolapse patients: a systematic review and meta-analysis.

OBJECTIVES: To perform a systematic review and meta-analysis of studies investigating the diagnostic value of cardiac magnetic resonance (CMR) features for arrhythmic risk stratification in mitral valve prolapse (MVP) patients. MATERIALS AND METHODS: EMBASE, PubMed/MEDLINE, and CENTRAL were searched for studies reporting MVP patients who underwent CMR with assessment of: left ventricular (LV) size and function, mitral regurgitation (MR), prolapse distance, mitral annular disjunction (MAD), curling, late gadolinium enhancement (LGE), and T1 mapping, and reported the association with arrhythmia. The primary endpoint was complex ventricular arrhythmias (co-VAs) as defined by any non-sustained ventricular tachycardia, sustained ventricular tachycardia, ventricular fibrillation, or aborted sudden cardiac death. Meta-analysis was performed when at least three studies investigated a CMR feature. PROSPERO registration number: CRD42023374185. RESULTS: The meta-analysis included 11 studies with 1278 patients. MR severity, leaflet length/thickness, curling, MAD distance, and mapping techniques were not meta-analyzed as reported in < 3 studies. LV end-diastolic volume index, LV ejection fraction, and prolapse distance showed small non-significant effect sizes. LGE showed a strong and significant association with co-VA with a LogORs of 2.12 (95% confidence interval (CI): [1.00, 3.23]), for MAD the log odds-ratio was 0.95 (95% CI: [0.30, 1.60]). The predictive accuracy of LGE was substantial, with a hierarchical summary ROC AUC of 0.83 (95% CI: [0.69, 0.91]) and sensitivity and specificity rates of 0.70 (95% CI: [0.41, 0.89]) and 0.80 (95% CI: [0.67, 0.89]), respectively. CONCLUSIONS: Our study highlights the role of LGE as the key CMR feature for arrhythmia risk stratification in MVP patients. MAD might complement arrhythmic risk stratification. CLINICAL RELEVANCE STATEMENT: LGE is a key factor for arrhythmogenic risk in MVP patients, with additional contribution from MAD. Combining MRI findings with clinical characteristics is critical for evaluating and accurately stratifying arrhythmogenic risk in MVP patients. KEY POINTS: MVP affects 2-3% of the population, with some facing increased risk for arrhythmia. LGE can assess arrhythmia risk, and MAD may further stratify patients. CMR is critical for MVP arrhythmia risk stratification, making it essential in a comprehensive evaluation.

Neural network-derived perfusion maps: A model-free approach to computed tomography perfusion in patients with acute ischemic stroke.

Objective: In this study, we investigate whether a Convolutional Neural Network (CNN) can generate informative parametric maps from the pre-processed CT perfusion data in patients with acute ischemic stroke in a clinical setting. Methods: The CNN training was performed on a subset of 100 pre-processed perfusion CT dataset, while 15 samples were kept for testing. All the data used for the training/testing of the network and for generating ground truth (GT) maps, using a state-of-the-art deconvolution algorithm, were previously pre-processed using a pipeline for motion correction and filtering. Threefold cross validation had been used to estimate the performance of the model on unseen data, reporting Mean Squared Error (MSE). Maps accuracy had been checked through manual segmentation of infarct core and total hypo-perfused regions on both CNN-derived and GT maps. Concordance among segmented lesions was assessed using the Dice Similarity Coefficient (DSC). Correlation and agreement among different perfusion analysis methods were evaluated using mean absolute volume differences, Pearson correlation coefficients, Bland-Altman analysis, and coefficient of repeatability across lesion volumes. Results: The MSE was very low for two out of three maps, and low in the remaining map, showing good generalizability. Mean Dice scores from two different raters and the GT maps ranged from 0.80 to 0.87. Inter-rater concordance was high, and a strong correlation was found between lesion volumes of CNN maps and GT maps (0.99, 0.98, respectively). Conclusion: The agreement between our CNN-based perfusion maps and the state-of-the-art deconvolution-algorithm perfusion analysis maps, highlights the potential of machine learning methods applied to perfusion analysis. CNN approaches can reduce the volume of data required by deconvolution algorithms to estimate the ischemic core, and thus might allow the development of novel perfusion protocols with lower radiation dose deployed to the patient.

Role of Cardiovascular Magnetic Resonance in the Management of Atrial Fibrillation: A Review.

Atrial fibrillation (AF) is the most common arrhythmia, and its prevalence is growing with time. Since the introduction of catheter ablation procedures for the treatment of AF, cardiovascular magnetic resonance (CMR) has had an increasingly important role for the treatment of this pathology both in clinical practice and as a research tool to provide insight into the arrhythmic substrate. The most common applications of CMR for AF catheter ablation are the angiographic study of the pulmonary veins, the sizing of the left atrium (LA), and the evaluation of the left atrial appendage (LAA) for stroke risk assessment. Moreover, CMR may provide useful information about esophageal anatomical relationship to LA to prevent thermal injuries during ablation procedures. The use of late gadolinium enhancement (LGE) imaging allows to evaluate the burden of atrial fibrosis before the ablation procedure and to assess procedural induced scarring. Recently, the possibility to assess atrial function, strain, and the burden of cardiac adipose tissue with CMR has provided more elements for risk stratification and clinical decision making in the setting of catheter ablation planning of AF. The purpose of this review is to provide a comprehensive overview of the potential applications of CMR in the workup of ablation procedures for atrial fibrillation.

Emergency room comprehensive assessment of demographic, radiological, laboratory and clinical data of patients with COVID-19: determination of its prognostic value for in-hospital mortality.

Mortality risk in COVID-19 patients is determined by several factors. The aim of our study was to adopt an integrated approach based on clinical, laboratory and chest x-ray (CXR) findings collected at the patient's admission to Emergency Room (ER) to identify prognostic factors. Retrospective study on 346 consecutive patients admitted to the ER of two North-Western Italy hospitals between March 9 and April 10, 2020 with clinical suspicion of COVID-19 confirmed by reverse transcriptase-polymerase reaction chain test (RT-PCR), CXR performed within 24 h (analyzed with two different scores) and recorded prognosis. Clinical and laboratory data were collected. Statistical analysis on the features of 83 in-hospital dead vs 263 recovered patients was performed with univariate (uBLR), multivariate binary logistic regression (mBLR) and ROC curve analysis. uBLR identified significant differences for several variables, most of them intertwined by multiple correlations. mBLR recognized as significant independent predictors for in-hospital mortality age > 75 years, C-reactive protein (CRP) > 60 mg/L, PaO2/FiO2 ratio (P/F) < 250 and CXR "Brixia score" > 7. Among the patients with at least two predictors, the in-hospital mortality rate was 58% against 6% for others [p < 0.0001; RR = 7.6 (4.4-13)]. Patients over 75 years had three other predictors in 35% cases against 10% for others [p < 0.0001, RR = 3.5 (1.9-6.4)]. The greatest risk of death from COVID-19 was age above 75 years, worsened by elevated CRP and CXR score and reduced P/F. Prompt determination of these data at admission to the emergency department could improve COVID-19 pretreatment risk stratification.

Ultra-Low-Dose Whole-Body Computed Tomography Protocol Optimization for Patients With Plasma Cell Disorders: Diagnostic Accuracy and Effective Dose Analysis From a Reference Center.

BACKGROUND: The whole-body low-dose CT (WBLDCT) is the first-choice imaging technique in patients with suspected plasma cell disorder to assess the presence of osteolytic lesions. We investigated the performances of an optimized protocol, evaluating diagnostic accuracy and effective patient dose reduction using a latest generation scanner. METHODS AND MATERIALS: Retrospective study on 212 patients with plasma cell disorders performed on a 256-row CT scanner. First, WBLDCT examinations were performed using a reference protocol with acquisition parameters obtained from literature. A phantom study was performed for protocol optimization for subsequent exams to minimize dose while maintaining optimal diagnostic accuracy. Images were analyzed by three readers to evaluate image quality and to detect lesions. Effective doses (E) were evaluated for each patient considering the patient dimensions and the tube current modulation. RESULTS: A similar, very good image quality was observed for both protocols by all readers with a good agreement at repeated measures ANOVA test (p>0.05). An excellent inter-rater agreement for lesion detection was achieved obtaining high values of Fleiss' kappa for all the districts considered (p<0.001). The optimized protocol resulted in a 56% reduction of median DLP (151) mGycm, interquartile range (IQR) 128-188 mGycm vs. 345 mGycm, IQR 302-408 mGycm), of 60% of CTDIvol (2.2 mGy, IQR 1.9-2.7 mGy vs. 0.9 mGy, IQR 0.8-1.2 mGy). The median E value was about 2.6 mSv (IQR 1.7-3.5 mSv) for standard protocol and about 1.5 mSv (IQR 1.4-1.7 mSv) for the optimized one. Dose reduction was statistically significant with p<0.001. CONCLUSIONS: Protocol optimization makes ultra-low-dose WBLDCT feasible on latest generation CT scanners for patients with plasma cell disorders with effective doses inferior to conventional skeletal survey while maintaining excellent image quality and diagnostic accuracy. Dose reduction is crucial in such patients, as they are likely to undergo multiple whole-body CT scans during follow-up.

Baseline chest X-ray in coronavirus disease 19 (COVID-19) patients: association with clinical and laboratory data.

PURPOSE: To assess the reliability of CXR and to describe CXR findings and clinical and laboratory characteristics associated with positive and negative CXR. METHODS: Retrospective two-center study on consecutive patients admitted to the emergency department of two north-western Italian hospitals in March 2020 with clinical suspicion of COVID-19 confirmed by RT-PCR and who underwent CXR within 24 h of the swab execution. 260 patients (61% male, 62.8 ± 15.8 year) were enrolled. CXRs were rated as positive (CXR+) or negative (CXR-), and features reported included presence and distribution of airspace opacities, pleural effusion and reduction in lung volumes. Clinical and laboratory data were collected. Statistical analysis was performed with nonparametric tests, binary logistic regression (BLR) and ROC curve analysis. RESULTS: Sensitivity of CXR was 61.1% (95%CI 55-67%) with a typical presence of bilateral (62.3%) airspace opacification, more often with a lower zone (88.7%) and peripheral (43.4%) distribution. At univariate analysis, several factors were found to differ significantly between CXR+ and CXR-. The BLR confirmed as significant predictors only lactate dehydrogenase (LDH), C-reactive protein (CRP) and interval between the onset of symptoms and the execution of CXR. The ROC curve procedure determined that CRX+ was associated with LDH > 500 UI/L (AUC = 0.878), CRP > 30 mg/L (AUC = 0.830) and interval between the onset of symptoms and the execution of CXR > 4 days (AUC = 0.75). The presence of two out of three of the above-mentioned predictors resulted in CXR+ in 92.5% of cases, whereas their absence in 7.4%. CONCLUSION: CXR has a low sensitivity. LDH, CRP and interval between the onset of symptoms and the execution of CXR are major predictors for a positive CXR.