Journal of Cardiovascular Magnetic Resonance最新文献

Background: There is conflicting evidence regarding the response to a fixed dose of regadenoson in patients with high body weight. The aim of this study was to evaluate the effectiveness of regadenoson in patients with varying body weights using novel quantitative cardiovascular magnetic resonance (CMR) perfusion parameters in addition to standard clinical markers.

Methods: Consecutive patients with typical angina and/or risk factors for coronary artery disease (N = 217) underwent regadenoson stress CMR perfusion imaging using a dual-sequence quantitative protocol with perfusion parameters generated from an artificial intelligence (AI)-based algorithm. CMR was performed on 1.5T scanners using a standard 0.4 mg injection of regadenoson. A cohort of consecutive patients undergoing adenosine stress perfusion (N = 218) was used as a control group.

Results: An inverse association of myocardial perfusion reserve and weight (mean decrease -0.05 per 10 kg increase, 95% confidence interval [CI] -0.009/-0.0001, P = 0.045) was noted in the regadenoson group but not in patients stressed with adenosine (P = 0.77). Adjusted logistic regression analysis revealed a 10 kg increase resulted in 36% increased odds for inadequate stress response (odds ratio [OR] = 1.36, 95% CI 1.10-1.69, P = 0.005). Moreover, a significant interaction (OR = 1.09, 95% CI 1.02-1.16, P = 0.012) between stressor type (regadenoson vs adenosine) and weight was noted. This was also confirmed in the propensity-matched subgroup (P = 0.024) and was not attenuated after adjustment (P = 0.041). Body surface area (BSA) (P = 0.006) but not body mass index (P = 0.055) was differentially associated with inadequate response conditional to the stressor used, and this association remained significant after adjustment for confounders (P = 0.025). Patients in the highest quartile of weight (>93 kg) or BSA (>2.06 m²) had substantially increased odds for inadequate response with regadenoson (OR = 8.19, 95% CI 2.04-32.97, P = 0.003 for increased weight and OR = 7.75, 95% CI 1.93-31.13, P = 0.004 for increased BSA). Both weight and BSA had excellent discriminative ability for inadequate regadenoson response (receiver operating characteristic area under curves 0.84 and 0.83, respectively).

Conclusion: Using quantitative perfusion CMR in patients undergoing pharmacological stress with regadenoson, we found an inverse relationship between patient weight and both clinical response and myocardial perfusion parameters. A fixed-dose bolus approach may not be adequate to induce maximal hyperemia in patients with increased weight. Weight-adjusted stressors, such as adenosine, may be considered instead in patients with body weight >93 kg and BSA >2.06 m².

Background: Quantitative myocardial tissue characterization with T₁ and T₂ parametric mapping can provide an accurate and complete assessment of tissue abnormalities across a broad range of cardiomyopathies. However, current clinical T₁ and T₂ mapping tools rely predominantly on two-dimensional (2D) breath-hold sequences. Clinical adoption of three-dimensional (3D) techniques is limited by long scan duration. The aim of this study is to develop and validate a time-efficient 3D free-breathing simultaneous T₁ and T₂ mapping sequence using multi-parametric SAturation-recovery and Variable-flip-Angle (mSAVA).

Methods: mSAVA acquires four volumes for simultaneous whole-heart T₁ and T₂ mapping. We validated mSAVA using simulations, phantoms, and in-vivo experiments at 3T in 11 healthy subjects and 11 patients with diverse cardiomyopathies. T₁ and T₂ values by mSAVA were compared with modified Look-Locker inversion recovery (MOLLI) and gradient and spin echo (GraSE), respectively. The clinical performance of mSAVA was evaluated against late gadolinium enhancement (LGE) imaging in patients.

Results: Phantom T₁ and T₂ by mSAVA showed a strong correlation to reference sequences (R² = 0.98 and 0.99). In-vivo imaging with an imaging resolution of 1.5 × 1.5 × 8 mm³ could be achieved. Myocardial T₁ and T₂ of healthy subjects by mSAVA were 1310 ± 46 and 44.6 ± 2.0 ms, respectively, with T₁ standard deviation higher than MOLLI (105 ± 12 vs 60 ± 16 ms) and T₂ standard deviation lower than GraSE (4.5 ± 0.8 vs 5.5 ± 1.0 ms). mSAVA T₁ and T₂ maps presented consistent findings in patients undergoing LGE. Myocardial T₁ and T₂ of all patients by mSAVA were 1421 ± 79 and 47.2 ± 3.3 ms, respectively.

Conclusion: mSAVA is a fast 3D technique promising for clinical whole-heart T₁ and T₂ mapping.

Background: Heart failure (HF) most commonly occurs in patients who have had a myocardial infarction (MI), but factors other than MI size may be deterministic. Fibrosis of myocardium remote from the MI is associated with adverse remodeling. We aimed to 1) investigate the association between remote myocardial fibrosis, measured using cardiovascular magnetic resonance (CMR) extracellular volume fraction (ECV), and HF and death following MI, 2) identify predictors of remote myocardial fibrosis in patients with evidence of MI and determine the relationship with infarct size.

Methods: Multicenter prospective cohort study of 1199 consecutive patients undergoing CMR with evidence of MI on late gadolinium enhancement. Median follow-up was 1133 (895-1442) days. Cox proportional hazards modeling was used to identify factors predictive of the primary outcome, a composite of first hospitalization for HF (HHF) or all-cause mortality, post-CMR. Linear regression modeling was used to identify determinants of remote ECV.

Results: Remote myocardial fibrosis was a strong predictor of primary outcome (χ²: 15.6, hazard ratio [HR]: 1.07 per 1% increase in ECV, 95% confidence interval [CI]: 1.04-1.11, p < 0.001) and was separately predictive of both HHF and death. The strongest predictors of remote ECV were diabetes, sex, natriuretic peptides, and body mass index, but, despite extensive phenotyping, the adjusted model R² was only 0.283. The relationship between infarct size and remote fibrosis was very weak.

Conclusion: Myocardial fibrosis, measured using CMR ECV, is a strong predictor of HHF and death in patients with evidence of MI. The mechanisms underlying remote myocardial fibrosis formation post-MI remain poorly understood, but factors other than infarct size appear to be important.

Background: Individuals with a Fontan circulation encompass a heterogeneous group with adverse outcomes linked to ventricular dilation, dysfunction, and dyssynchrony. The purpose of this study was to assess if unsupervised machine learning cluster analysis of cardiovascular magnetic resonance (CMR)-derived dyssynchrony metrics can separate ventricles in the Fontan circulation from normal control left ventricles and identify prognostically distinct subgroups within the Fontan cohort.

Methods: This single-center, retrospective study used 503 CMR studies from Fontan patients (median age 15 y) and 42 from age-matched controls from January 2005 to May 2011. Feature tracking on short-axis cine stacks assessed radial and circumferential strain, strain rate, and displacement. Unsupervised K-means clustering was applied to 24 mechanical dyssynchrony metrics derived from these deformation measurements. Clusters were compared for demographic, anatomical, and composite outcomes of death, or heart transplantation.

Results: Four distinct phenotypic clusters were identified. Over a median follow-up of 4.2 y (interquartile ranges 1.7-8.8 y), 58 (11.5%) patients met the composite outcome. The highest-risk cluster (largely comprised of right or mixed ventricular morphology and dilated, dyssynchronous ventricles) exhibited a higher hazard for the composite outcome compared to the lowest-risk cluster while controlling for ventricular morphology (hazard ratio [HR] 6.4; 95% confidence interval [CI] 2.1-19.3; P value 0.001) and higher indexed end-diastolic volume (HR 3.2; 95% CI 1.04-10.0; P value 0.043) per 10 mL/m².

Conclusion: Unsupervised machine learning using CMR-derived dyssynchrony metrics identified four distinct clusters of patients with Fontan circulation and healthy controls with varying clinical characteristics and risk profiles. This technique can be used to guide future studies and identify more homogeneous subsets of patients from an overall heterogeneous population.

Background: Clinical guidelines and scientific data increasingly support the appropriate use of cardiovascular magnetic resonance (CMR) . The extent of CMR adoption across the United States (US) remains unclear. This observational analysis aims to capture CMR practice patterns in the US.

Methods: Commissioned reports from the Society for Cardiovascular Magnetic Resonance (SCMR), pre-existing survey data from CMR centers, and socioeconomic and coronary heart disease data from the Centers for Disease Control and Prevention were used. The location of imaging centers performing CMR was based on 2018 Medicare claims. Secondary analysis was performed on center-specific survey data from 2017-2019, which were collected by members of the SCMR US Advocacy Subcommittee for quality improvement purposes. The correlation between the number of imaging centers billing for CMR services per million persons, socioeconomic determinants, and coronary heart disease epidemiology was determined.

Results: A total of 591 imaging centers billed the Center for Medicare & Medicaid Services for CMR services in 2018 and 112 (of 155) unique CMR centers responded to the survey. In 2018, CMR services were available in almost all 50 states. Minnesota was the state with the highest number of CMR centers per million Medicare beneficiaries (52.6 centers per million), and Maine had the lowest (4.4 per million). The total density of CMR centers was 16 per million for US Medicare beneficiaries. Sixty-eight percent (83 of 112) of survey responders were cardiologists, and 28% (31/112) were radiologists. In 72% (71/112) of centers, academic health care systems performed 81%-100% of CMR exams. The number of high-volume centers (>500 scans per year) increased by seven between 2017 and 2019. In 2019, 53% (59/112) of centers were considered high-volume centers and had an average of 19 years of experience. Centers performing <50 scans had on average 3.5 years of experience. Approximate patient wait time for a CMR exam was 2 weeks to 1 month.

Conclusion: Despite increasing volume and availability in almost all 50 states, CMR access remains geographically variable. Advocacy efforts to improve access and innovations that reduce imaging time and exam complexity have the potential to increase the adoption of CMR technology.

Background: While late gadolinium enhancement (LGE) is proposed as a diagnostic criterion for arrhythmogenic right ventricular cardiomyopathy (ARVC), the potential of LGE to distinguish ARVC from differentials remains unknown. We aimed to assess the diagnostic value of LGE for ARVC diagnosis.

Methods: We included 132 subjects (60% male, 47 ± 11 years) who had undergone cardiac magnetic resonance imaging with LGE assessment for ARVC or ARVC differentials. ARVC was diagnosed as per 2010 Task Force Criteria (n = 55). ARVC differentials consisted of familial/genetic dilated cardiomyopathy (n = 25), myocarditis (n = 13), sarcoidosis (n = 20), and amyloidosis (n = 19). The diagnosis of all differentials was based on the most current standard of reference. The presence of LGE was evaluated using a 7-segment right ventricle (RV) and 17-segment left ventricle (LV) model. Subsequently, we assessed LGE patterns for every patient individually for fulfilling LV- and/or RV-LGE per Padua criteria, independent of their clinical diagnosis (i.e. phenotype). Diagnostic values were analyzed using sensitivity and specificity for any RV-LGE, any LV-LGE, RV-LGE per Padua criteria, and prevalence graphs for LV-LGE per Padua criteria. The optimal integration of LGE for ARVC diagnosis was determined using classification and regression tree analysis.

Results: One-third (38%) of ARVC patients had RV-LGE, while half (51%) had LV-LGE. RV-LGE was less frequently observed in ARVC vs non-ARVC patients (38% vs 58%, p = 0.034) leading to a poor discriminatory potential (any RV-LGE: sensitivity 38%, specificity 42%; RV-LGE per Padua criteria: sensitivity 36%, specificity 44%). Compared to ARVC patients, non-ARVC patients more often had LV-LGE (91% vs 51%, p < 0.001) which was also more globally distributed (median 9 [interquartile range (IQR): 3-13] vs 0 [IQR: 0-3] segments, p < 0.001). The absence of anteroseptal and absence of extensive (≥5 segments) mid-myocardial LV-LGE, and absence of moderate (≥2 segments) mid-myocardial LV-LGE predicted ARVC with good diagnostic performance (sensitivity 93%, specificity 78%).

Conclusion: LGE is often present in ARVC differentials and may lead to false positive diagnoses when used without knowledge of LGE patterns. Moderate RV-LGE without anteroseptal and mid-myocardial LV-LGE is typically observed in ARVC.

Background: Cardiovascular magnetic resonance (CMR) is increasingly utilized to evaluate expanding cardiovascular conditions. The Society for Cardiovascular Magnetic Resonance (SCMR) Registry is a central repository for real-world clinical data to support cardiovascular research, including those relating to outcomes, quality improvement, and machine learning. The SCMR Registry is built on a regulatory-compliant, cloud-based infrastructure that houses searchable content and Digital Imaging and Communications in Medicine images. The goal of this study is to summarize the status of the SCMR Registry at 150,000 exams.

Methods: The processes for data security, data submission, and research access are outlined. We interrogated the Registry and presented a summary of its contents.

Results: Data were compiled from 154,458 CMR scans across 20 United States sites, containing 299,622,066 total images (∼100 terabytes of storage). Across reported values, the human subjects had an average age of 58 years (range 1 month to >90 years old), were 44% (63,070/145,275) female, 72% (69,766/98,008) Caucasian, and had a mortality rate of 8% (9,962/132,979). The most common indication was cardiomyopathy (35,369/131,581, 27%), and most frequently used current procedural terminology code was 75561 (57,195/162,901, 35%). Macrocyclic gadolinium-based contrast agents represented 89% (83,089/93,884) of contrast utilization after 2015. Short-axis cines were performed in 99% (76,859/77,871) of tagged scans, short-axis late gadolinium enhancement (LGE) in 66% (51,591/77,871), and stress perfusion sequences in 30% (23,241/77,871). Mortality data demonstrated increased mortality in patients with left ventricular ejection fraction <35%, the presence of wall motion abnormalities, stress perfusion defects, and infarct LGE, compared to those without these markers. There were 456,678 patient-years of all-cause mortality follow-up, with a median follow-up time of 3.6 years.

Conclusion: The vision of the SCMR Registry is to promote evidence-based utilization of CMR through a collaborative effort by providing a web mechanism for centers to securely upload de-identified data and images for research, education, and quality control. The Registry quantifies changing practice over time and supports large-scale real-world multicenter observational studies of prognostic utility.