scholarly journals Limitations of a non-invasive model of the estimation of pulmonary vascular resistance by magnetic resonance imaging in patients with heart failure

2021 ◽  
Vol 22 (Supplement_2) ◽  
Author(s):  
V Vidal Urrutia ◽  
A Cubillos-Arango ◽  
P Garcia-Gonzalez ◽  
J Gradoli-Palmero ◽  
J Nunez-Villota ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Heart Failure ◽  
Magnetic Resonance ◽  
Pulmonary Vascular Resistance ◽  
Altman Analysis ◽  
Resonance Imaging ◽  
Bland Altman Analysis ◽  
Non Invasive ◽  
Patients With Heart Failure ◽  
The Mean

Abstract Funding Acknowledgements Type of funding sources: None. Introduction. Pulmonary vascular resistance (PVR) is a hemodynamic parameter with important diagnostic and prognostic implications in patients with heart failure. Currently the gold standard technique for its quantification is right heart catheterization (RHC). However, cardiovascular magnetic resonance imaging (CMR) has been postulated as a non-invasive alternative for its estimation. The aim of this study is to assess the accuracy of a non-invasive model of PVR estimated by CMR in a specific subgroup of patients with acute heart failure (AHF). Methods. Between January 2014 and December 2018, 108 patients with AHF who underwent RHC and CMR on the same day were prospectively included. PVR was assessed by CMR using the model: 19.38 - [4.62 x Ln mean pulmonary artery velocity - 0.08 x right ventricular ejection fraction (RVEF)]. During RHC, PVR were calculated using the ratio between transpulmonary gradient and cardiac output. We evaluated their correlation using the Spearman correlation coefficient, receiver operating characteristic [ROC] curves, and Bland-Altman analysis. Results. The mean age of our cohort was 65 ± 11 years and 64.8% were male. The median PVR (Wood Units, WU) assessed by CMR and RHC were 5.1 WU (3.4 - 6.8) and 3 WU (1.5 - 3.9); p < 0.001, respectively. A weak correlation was observed between the PVR obtained by RHC and those obtained by CMR in our population (r = 0.21; p = 0.02). On Bland-Altman analysis, the mean bias was -1.7, and the 95% limits of agreement ranged from -10.02 to 6.6 WU. The area under the ROC curve for PVR assessed by CMR to detect PVR ³3 WU was 0.57, 95% confidence interval (CI): 0.47-0.68. Conclusions. In patients with AHF, the non-invasive estimation of PVR using CMR shows poor accuracy, as well as a limited capacity to discriminate increased PVR values.

scholarly journals The Role of Cardiovascular Magnetic Resonance Imaging in Heart Failure

2016 ◽  
pp. 115 ◽  
Author(s):  
Mark A Peterzan ◽  
Oliver J Rider ◽  
Lisa J Anderson
Keyword(s):  
Magnetic Resonance Imaging ◽  
Heart Failure ◽  
Cardiovascular Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiovascular Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Cardiac Amyloid ◽  
Patients With Heart Failure ◽  
Established Role

Cardiovascular imaging is key for the assessment of patients with heart failure. Today, cardiovascular magnetic resonance imaging plays an established role in the assessment of patients with suspected and confirmed heart failure syndromes, in particular identifying aetiology. Its role in informing prognosis and guiding decisions around therapy are evolving. Key strengths include its accuracy; reproducibility; unrestricted field of view; lack of radiation; multiple abilities to characterise myocardial tissue, thrombus and scar; as well as unparalleled assessment of left and right ventricular volumes. T2* has an established role in the assessment and follow-up of iron overload cardiomyopathy and a role for T1 in specific therapies for cardiac amyloid and Anderson–Fabry disease is emerging.

Contrast-enhanced digital mammography and magnetic resonance imaging: reproducibility compared to pathologic anatomy

2021 ◽  
pp. 030089162110501
Author(s):  
Alessandro Liguori ◽  
Catherine Depretto ◽  
Chiara Maura Ciniselli ◽  
Andrea Citterio ◽  
Giulia Boffelli ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Digital Mammography ◽  
Breast Size ◽  
Reference Standard ◽  
Altman Analysis ◽  
Resonance Imaging ◽  
Bland Altman Analysis ◽  
Contrast Enhanced ◽  
Mass Lesions

Purpose: To compare the reproducibility between contrast-enhanced digital mammography (CEDM) and magnetic resonance imaging (MRI) with the postsurgical pathologic examination. In addition, the applicability of the Breast Imaging–Reporting and Data System (BI-RADS) lexicon of MRI to CEDM was evaluated for mass lesions. Methods: A total of 62 patients with a histologically proven diagnosis of breast cancer were included in this study, for a total of 67 lesions. Fifty-nine patients underwent both methods. The reproducibility between MRI vs CEDM and the reference standard (postoperative pathology) was assessed by considering the lesion and breast size as pivotal variables. Reproducibility was evaluated by computing the concordance correlation coefficient (CCC). Bland-Altman plots were used to depict the observed pattern of agreement as well as to estimate the associated bias. Furthermore, the pattern of agreement between the investigated methods with regard to the breast lesion characterization (i.e. mass/nonmass; shape; margins; internal enhanced characteristics) was assessed by computing the Cohen kappa and its 95% confidence interval (CI). Results: The reproducibility between MRI and the reference standard and between CEDM and the reference standard showed substantial agreement, with a CCC value of 0.956 (95% CI, 0.931–0.972) and 0.950 (95% CI, 0.920–0.969), respectively. By looking at the Bland-Altman analysis, bias values of 2.344 and 1.875 mm were observed for MRI and CEDM vs reference evaluation, respectively. The agreement between MRI and CEDM is substantial with a CCC value of 0.969 (95% CI, 0.949–0.981). The Bland-Altman analysis showed bias values of −0.469 mm when comparing CEDM vs MRI. Following the Landis and Koch classification criteria, moderate agreement was observed between the two methods in describing BI-RADS descriptors of mass lesions. Conclusion: CEDM is able to measure and describe tumor masses comparably to MRI and can be used for surgical planning.

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