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Cardiac Magnetic Resonance to Detect the Underlying Substrate in Patients with Frequent Idiopathic Ventricular Arrhythmias.

ABSTRACT: A routine diagnostic work-up does not identify structural abnormalities in a substantial proportion of patients with idiopathic ventricular arrhythmias (VAs). We investigated the added value of cardiac magnetic resonance (CMR) imaging in this group of patients. A single-centre prospective study was undertaken of 72 patients (mean age 46 ± 16 years; 53% females) with frequent premature ventricular contractions (PVCs ≥ 500/24 h) and/or non-sustained ventricular tachycardia (NSVT), an otherwise normal electrocardiogram, normal echocardiography and no coronary artery disease. CMR provided an additional diagnostic yield in 54.2% of patients. The most prevalent diagnosis was previous myocarditis (23.6%) followed by possible PVC-related cardiomyopathy (20.8%), non-ischaemic cardiomyopathy (8.3%) and ischaemic heart disease (1.4%). The predictors of abnormal CMR findings were male gender, age and PVCs/NSVT non-outflow tract-related or with multiple morphologies. Patients with VAs had an impaired peak left ventricular (LV) global radial strain (GRS) compared with the controls (28.88% (IQR: 25.87% to 33.97%) vs. 36.65% (IQR: 33.19% to 40.2%), p < 0.001) and a global circumferential strain (GCS) (-17.66% (IQR: -19.62% to -16.23%) vs. -20.66% (IQR: -21.72% to -19.6%), p < 0.001). CMR reveals abnormalities in a significant proportion of patients with frequent idiopathic VAs. Male gender, age and non-outflow tract PVC origin can be clinical indicators for CMR referral.

SUBMITTER: Nikolaidou C

PROVIDER: S-EPMC8233842 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Cardiac Magnetic Resonance to Detect the Underlying Substrate in Patients with Frequent Idiopathic Ventricular Arrhythmias.

Nikolaidou Chrysovalantou C Kotanidis Christos P CP Wijesurendra Rohan R Leal-Pelado Joana J Kouskouras Konstantinos K Vassilikos Vassilios P VP Karvounis Haralambos H Ntusi Ntobeko N Antoniades Charalambos C Neubauer Stefan S Karamitsos Theodoros D TD

Diagnostics (Basel, Switzerland) 20210618 6

<h4>Background</h4>A routine diagnostic work-up does not identify structural abnormalities in a substantial proportion of patients with idiopathic ventricular arrhythmias (VAs). We investigated the added value of cardiac magnetic resonance (CMR) imaging in this group of patients.<h4>Methods</h4>A single-centre prospective study was undertaken of 72 patients (mean age 46 ± 16 years; 53% females) with frequent premature ventricular contractions (PVCs ≥ 500/24 h) and/or non-sustained ventricular ta ...[more]

PMID: 34207123

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Project description:Arrhythmia can significantly alter the image quality of cardiovascular magnetic resonance (CMR); automatic detection and sorting of the most frequent types of arrhythmias during the CMR acquisition could potentially improve image quality. New CMR techniques, such as non-Cartesian CMR, can allow self-gating: from cardiac motion-related signal changes, we can detect cardiac cycles without an electrocardiogram. We can further use this data to obtain a surrogate for RR intervals (valley intervals: VV). Our purpose was to evaluate the feasibility of an automated method for classification of non-arrhythmic (NA) (regular cycles) and arrhythmic patients (A) (irregular cycles), and for sorting of common arrhythmia patterns between atrial fibrillation (AF) and premature ventricular contraction (PVC), using the cardiac motion-related signal obtained during self-gated free-breathing radial cardiac cine CMR with compressed sensing reconstruction (XD-GRASP).One hundred eleven patients underwent cardiac XD-GRASP CMR between October 2015 and February 2016; 33 were included for retrospective analysis with the proposed method (6 AF, 8 PVC, 19 NA; by recent ECG). We analyzed the VV, using pooled statistics (histograms) and sequential analysis (Poincaré plots), including the median (medVV), the weighted mean (meanVV), the total number of VV values (VVval), and the total range (VVTR) and half range (VVHR) of the cumulative frequency distribution of VV, including the median to half range (medVV/VVHR) and the half range to total range (VVHR/VVTR) ratios. We designed a simple algorithm for using the VV results to differentiate A from NA, and AF from PVC.Between NA and A, meanVV, VVval, VVTR, VVHR, medVV/VVHR and VVHR/VVTR ratios were significantly different (p values = 0.00014, 0.0027, 0.000028, 5×10-9, 0.002, respectively). Between AF and PVC, meanVV, VVval and medVV/VVHR ratio were significantly different (p values = 0.018, 0.007, 0.044, respectively). Using our algorithm, sensitivity, specificity, and accuracy were 93 %, 95 % and 94 % to discriminate between NA and A, and 83 %, 71 %, and 77 % to discriminate between AF and PVC, respectively; areas under the ROC curve were 0.93 and 0.89.Our study shows we can reliably detect arrhythmias and differentiate AF from PVC, using self-gated cardiac cine XD-GRASP CMR.

Project description:BackgroundPatients with nonischemic dilated cardiomyopathy (NIDCM) and malignant ventricular arrhythmia (MVA) often have a poor prognosis and a high risk of sudden cardiac death. Although the diagnosis of MVA is straightforward by electrocardiogram (ECG), the underlying abnormalities of ventricular mechanics in these patients are unknown. This study aims to preliminarily explore the value of cardiac magnetic resonance feature tracking (CMR-FT) for MVA in dilated cardiomyopathy.MethodsIn this retrospective study, patients with NIDCM who met inclusion criteria were divided into an MVA group and a non-MVA group (included from January 2018 to September 2021). The interobserver agreement of myocardial strain parameters, including global longitudinal strain (GLS), global circumferential strain (GCS) and global radial strain (GRS), were tested. The GLS, GCS, GRS, left ventricular ejection fraction (LVEF), Tpeak-Tend interval on ECG and brain natriuretic peptide (BNP) were compared between groups. Single-factor and multifactor receiver operating characteristic (ROC) curve analyses were conducted to calculate the area under the ROC curve (AUC), cut-off point, sensitivity, and specificity of these parameters in predicting MVA in NIDCM.ResultsA total of 161 NIDCM patients were included (54 in the MVA group). GLS, GCS, and GRS had good interobserver agreement (all intraclass correlation coefficients >0.80). The absolute GLS and GCS, GRS and LVEF were lower in the MVA group than the non-MVA group (P<0.001), Tpeak-Tend and BNP were higher (P<0.001). Single-factor ROC curve analysis showed that GLS, GCS and GRS had certain diagnostic value for MVA (AUC =0.795, 0.802, and 0.754, respectively). Among them, GCS had higher sensitivity and specificity (GCS 0.796/0.776, GLS 0.778/0.757, GRS 0.741/0.692). Multifactor ROC curve analysis showed the combination of GLS and GCS (AUC =0.810), the combination of GCS and GRS (AUC =0.802), the combination of GLS and GRS (AUC =0.787), the combination of GLS, GCS, and GRS (AUC =0.810).ConclusionsThe three-dimensional myocardial strain parameters (especially GLS and GCS) measured by CMR-FT had certain diagnostic value and could reflect the underlying abnormality of ventricular mechanics of NIDCM with MVA.

Project description:ObjectivesTo assess the diagnostic accuracy of an automated algorithm to detect left ventricular (LV) dilatation on non-ECG gated CT, using cardiac magnetic resonance (CMR) as reference standard.MethodsConsecutive patients with contrast-enhanced CT thorax and CMR within 31 days (2016-2020) were analysed (n = 84). LV dilatation was defined against age-, sex- and body surface area-specific values for CMR. CTs underwent automated artificial intelligence(AI)-derived analysis that segmented ventricular chambers, presenting maximal LV diameter and volume. Area under the receiver operator curve (AUC-ROC) analysis identified CT thresholds with ≥90% sensitivity and highest specificity and ≥90% specificity with highest sensitivity. Youden's Index was used to identify thresholds with optimised sensitivity and specificity.ResultsAutomated diameter analysis was feasible in 92% of cases (77/84; 45 men, age 61 ± 14 years, mean CT to CMR interval 10 ± 8 days). Relative to CMR as a reference standard, 45% had LV dilatation. In males, an automated LV diameter measurement of ≥55.5 mm was ≥90% specific for CMR-defined LV dilatation (positive predictive value (PPV) 85.7%, negative predictive value (NPV) 61.2%, accuracy 68.9%). In females, an LV diameter of ≥49.7 mm was ≥90% specific for CMR-defined LV dilatation (PPV 66.7%, NPV 73.1%, accuracy 71.9%). AI CT volumetry data did not significantly improve AUC performance.ConclusionFully automated AI-derived analysis LV dilatation on routine unselected non-gated contrast-enhanced CT thorax studies is feasible. We have defined thresholds for the detection of LV dilatation on CT relative to CMR, which could be used to routinely screen for dilated cardiomyopathy at the time of CT.Advances in knowledgeWe show, for the first time, that a fully-automated AI-derived analysis of maximal LV chamber axial diameter on non-ECG-gated thoracic CT is feasible in unselected real-world cases and that the derived measures can predict LV dilatation relative to cardiac magnetic resonance imaging, the non-invasive reference standard for determining cardiac chamber size. We have derived sex-specific cut-off values to screen for LV dilatation on routine contrast-enhanced thoracic CT. Future work should validate these thresholds and determine if technology can alter clinical outcomes in a cost-effective manner.

Dataset Information

Cardiac Magnetic Resonance to Detect the Underlying Substrate in Patients with Frequent Idiopathic Ventricular Arrhythmias.

Publications

Cardiac Magnetic Resonance to Detect the Underlying Substrate in Patients with Frequent Idiopathic Ventricular Arrhythmias.

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