Normative healthy reference values for global and segmental 3D principal and geometry dependent strain from cine cardiac magnetic resonance imaging.

3-Dimensional (3D) myocardial deformation analysis (3D-MDA) enables novel descriptions of geometry-independent principal strain (PS). Applied to routine 2D cine cardiovascular magnetic resonance (CMR), this provides unique measures of myocardial biomechanics for disease diagnosis and prognostication. However, healthy reference values remain undefined. This study describes age- and sex-stratified reference values from CMR-based 3D-MDA, including 3D PS. One hundred healthy volunteers were prospectively recruited following institutional ethics approval and underwent CMR imaging. 3D-MDA was performed using validated software. Age- and sex-stratified global and segmental strain measures were derived for conventional geometry-dependent [circumferential (CS), longitudinal (LS), and radial (RS)] and geometry-independent [minimum (minPS) and maximum principal (maxPS)] directions of deformation. Layer-specific contraction angle interactions were determined using local minPS vectors. The average age was 43 ± 15 years and 55% were women. Strain measures were higher in women versus men. 3D PS-based assessment of maximum tissue shortening (minPS) and maximum tissue thickening (maxPS) were greater than corresponding geometry-dependent markers of LS and RS, consistent with improved representation of local tissue deformations. Global maxPS amplitude best discriminated both age and sex. Segmental analyses showed greater strain amplitudes in apical segments. Transmural PS contraction angles were higher in females and showed a heterogeneous distribution across segments. In this study we provided age and sex-based reference values for 3D strain from CMR imaging, demonstrating improved capacity for 3D PS to document maximal local tissue deformations and to discriminate age and sex phenotypes. Novel markers of layer-specific strain angles from 3D PS were also described.

Machine learning prediction of atrial fibrillation in cardiovascular patients using cardiac magnetic resonance and electronic health information.

Background: Atrial fibrillation (AF) is a commonly encountered cardiac arrhythmia associated with morbidity and substantial healthcare costs. While patients with cardiovascular disease experience the greatest risk of new-onset AF, no risk model has been developed to predict AF occurrence in this population. We hypothesized that a patient-specific model could be delivered using cardiovascular magnetic resonance (CMR) disease phenotyping, contextual patient health information, and machine learning. Methods: Nine thousand four hundred forty-eight patients referred for CMR imaging were enrolled and followed over a 5-year period. Seven thousand, six hundred thirty-nine had no prior history of AF and were eligible to train and validate machine learning algorithms. Random survival forests (RSFs) were used to predict new-onset AF and compared to Cox proportional-hazard (CPH) models. The best performing features were identified from 115 variables sourced from three data domains: (i) CMR-based disease phenotype, (ii) patient health questionnaire, and (iii) electronic health records. We evaluated discriminative performance of optimized models using C-index and time-dependent AUC (tAUC). Results: A RSF-based model of 20 variables (CIROC-AF-20) delivered an overall C-index of 0.78 for the prediction of new-onset AF with respective tAUCs of 0.80, 0.79, and 0.78 at 1-, 2- and 3-years. This outperformed a novel CPH-based model and historic AF risk scores. At 1-year of follow-up, validation cohort patients classified as high-risk of future AF by CIROC-AF-20 went on to experience a 17.3% incidence of new-onset AF, being 24.7-fold higher risk than low risk patients. Conclusions: Using phenotypic data available at time of CMR imaging we developed and validated the first described risk model for the prediction of new-onset AF in patients with cardiovascular disease. Complementary value was provided by variables from patient-reported measures of health and the electronic health record, illustrating the value of multi-domain phenotypic data for the prediction of AF.

Influence of Sex-Based Differences in Cardiac Phenotype on Atrial Fibrillation Recurrence in Patients Undergoing Pulmonary Vein Isolation.

Background: Pulmonary vein isolation (PVI) is a commonly engaged therapy for symptomatic atrial fibrillation (AF). Prior studies have documented elevated AF recurrence rates among females vs. males. Sex-specific mechanisms underlying this phenomenon are poorly understood. This prospective cohort study aimed to evaluate the sex-based differences in cardiac phenotype and their influence on (AF) recurrence following first-time PVI. Methods: A total of 204 consecutive patients referred for first-time PVI and 101 healthy subjects were prospectively studied by cardiovascular magnetic resonance (CMR) imaging. Multi-chamber volumetric and functional measures were assessed by sex-corrected Z-score analyses vs. healthy subjects. Patients were followed for a median of 2.6 years for the primary outcome of clinical AF recurrence. Multivariable analyses adjusting for age and comorbidities were performed to identify independent predictors of AF recurrence. Results: AF recurrence following first PVI occurred in 41% of males and 59% of females (p = 0.03). Females were older with higher prevalence of hypertension and thyroid disorders. Z-score-based analyses revealed significantly reduced ventricular volumes, greater left atrial (LA) volumes, and reduced LA contractility in females vs. males. Multivariable analysis revealed each of LA minimum and pre-systolic volumes and booster EF Z-scores to be independently associated with AF recurrence, providing respective hazard ratios of 1.10, 1.19, and 0.89 (p = 0.001, 0.03, and 0.01). Conclusion: Among patients referred for first time PVI, females were older and demonstrated significantly poorer LA contractile health vs. males, the latter independently associated with AF recurrence. Assessment of LA contractile health may therefore be of value to identify female patients at elevated risk of AF recurrence. Factors influencing female patient referral for PVI at more advanced stages of atrial disease warrant focused investigation.

Improved accuracy and precision with three-parameter simultaneous myocardial T1 and T2 mapping using multiparametric SASHA.

PURPOSE: To develop and validate a three-parameter model for improved precision multiparametric SAturation-recovery single-SHot Acquisition (mSASHA) cardiac T1 and T2 mapping with high accuracy in a single breath-hold. METHODS: The mSASHA acquisition consists of nine images of variable saturation recovery and T2 preparation in 11 heartbeats with T1 and T2 values calculated using a three-parameter model. It was validated in simulations and phantoms at 3 T with comparison to a four-parameter joint T1 -T2 technique. The mSASHA acquisition was compared with MOLLI, SASHA, and T2 -prepared balanced SSFP in 10 volunteers. RESULTS: The mSASHA technique had high accuracy in phantoms compared to spin echo, with -0.2 ± 0.3% T1 error and -2.4 ± 1.3% T2 error. The mSASHA coefficient of variation in phantoms for T1 was similar to MOLLI (0.7 ± 0.2% for both) and T2 -prepared balanced SSFP for T2 (1.3 ± 0.7% vs 1.4 ± 0.3%, adjusted p > .05 for both). In simulations, three-parameter mSASHA had higher precision than four-parameter joint T1 -T2 for both T1 and T2 (46% and 11% reductions in T1 and T2 interquartile range for native myocardium). In vivo myocardial mSASHA T1 was similar to SASHA (1523 ± 18 ms vs 1520 ± 18 ms) with similar coefficient of variation to both MOLLI and SASHA (3.3 ± 0.6% vs 3.1 ± 0.6% and 3.3 ± 0.5% respectively, adjusted p > .05 for all). Myocardial mSASHA T2 was 37.1 ± 1.1 ms with similar precision to T2 -prepared balanced SSFP (6.7 ± 1.7% vs 6.0 ± 1.6%, adjusted p > .05). CONCLUSION: Three-parameter mSASHA provides high-accuracy cardiac T1 and T2 quantification in a single breath-hold with similar precision to MOLLI and T2 -prepared balanced SSFP. Further study is required to both establish normative values and demonstrate clinical utility in patient populations.

Effect of Active Cancer on the Cardiac Phenotype: A Cardiac Magnetic Resonance Imaging-Based Study of Myocardial Tissue Health and Deformation in Patients With Chemotherapy-Naïve Cancer.

Background The overlap between cancer and cardiovascular care continues to expand, with intersections emerging before, during, and following cancer therapies. To date, emphasis has been placed on how cancer therapeutics influence downstream cardiac health. However, whether active malignancy itself influences chamber volumes, function, or overall myocardial tissue health remains uncertain. We sought to perform a comprehensive cardiovascular magnetic resonance-based evaluation of cardiac health in patients with chemotherapy-naïve cancer with comparison with a healthy volunteer population. Methods and Results Three-hundred and eighty-one patients with active breast cancer or lymphoma before cardiotoxic chemotherapy exposure were recruited in addition to 102 healthy volunteers. Both cohorts underwent standardized cardiovascular magnetic resonance imaging with quantification of chamber volumes, ejection fraction, and native myocardial T1. Left ventricular mechanics were incrementally assessed using three-dimensional myocardial deformation analysis, providing global longitudinal, circumferential, radial, and principal peak-systolic strain amplitude and systolic strain rate. The mean age of patients with cancer was 53.8±13.4 years; 79% being women. Despite similar left ventricular ejection fraction, patients with cancer showed smaller chambers, increased strain amplitude, and systolic strain rate in both conventional and principal directions, and elevated native T1 versus sex-matched healthy volunteers. Adjusting for age, sex, hypertension, and diabetes mellitus, the presence of cancer remained associated with these cardiovascular magnetic resonance parameters. Conclusions The presence of cancer is independently associated with alterations in cardiac chamber size, function, and objective markers of tissue health. Dedicated research is warranted to elucidate pathophysiologic mechanisms underlying these findings and to explore their relevance to the management of patients with cancer referred for cardiotoxic therapies.

Rapid Response to Cytokine Storm Inhibition Using Anakinra in a Patient With COVID-19 Myocarditis.

A 62-year-old woman with coronavirus disease 2019 developed acute respiratory failure and cardiogenic shock in the setting of a systemic hyperinflammatory state and apparent ST-elevation myocardial infarction. Cardiac magnetic resonance imaging showed fulminant acute myocarditis with severe left ventricular dysfunction. Treatment with the recombinant interleukin-1 receptor antagonist anakinra and dexamethasone resulted in rapid clinical improvement, reduction in serum inflammatory markers, and a marked recovery in cardiac magnetic resonance--based markers of inflammation and contractile dysfunction. The patient was subsequently discharged from the hospital. Emerging evidence supports use of anti-inflammatory therapies, including anakinra and dexamethasone, in severe cases of coronavirus disease 2019.

Une femme de 62 ans atteinte de la COVID-19 a développé une insuffisance respiratoire aiguë et un choc cardiogène dans le contexte d'un état hyperinflammatoire général et d'un infarctus du myocarde avec élévation du segment ST apparent. L'imagerie par résonance magnétique cardiaque a révélé une myocardite aiguë fulminante accompagnée d'une dysfonction ventriculaire gauche sévère. Le traitement par l'anakinra, un antagoniste des récepteurs de l'interleukine 1 recombinant, et la dexaméthasone, a entraîné une amélioration clinique rapide, une diminution des marqueurs inflammatoires sériques et un rétablissement marqué selon les marqueurs de l'inflammation et de la dysfonction contractile à la résonance magnétique cardiaque. La patiente a par la suite reçu son congé de l'hôpital. De nouvelles données probantes militent en faveur de l'emploi de traitements anti-inflammatoires, comme l'anakinra et la dexaméthasone, dans les cas sévères de COVID-19.

Neural-Network-Based Diagnosis Using 3-Dimensional Myocardial Architecture and Deformation: Demonstration for the Differentiation of Hypertrophic Cardiomyopathy.

The diagnosis of cardiomyopathy states may benefit from machine-learning (ML) based approaches, particularly to distinguish those states with similar phenotypic characteristics. Three-dimensional myocardial deformation analysis (3D-MDA) has been validated to provide standardized descriptors of myocardial architecture and deformation, and may therefore offer appropriate features for the training of ML-based diagnostic tools. We aimed to assess the feasibility of automated disease diagnosis using a neural network trained using 3D-MDA to discriminate hypertrophic cardiomyopathy (HCM) from its mimic states: cardiac amyloidosis (CA), Anderson-Fabry disease (AFD), and hypertensive cardiomyopathy (HTNcm). 3D-MDA data from 163 patients (mean age 53.1 ± 14.8 years; 68 females) with left ventricular hypertrophy (LVH) of known etiology was provided. Source imaging data was from cardiac magnetic resonance (CMR). Clinical diagnoses were as follows: 85 HCM, 30 HTNcm, 30 AFD, and 18 CA. A fully-connected-layer feed-forward neural was trained to distinguish HCM vs. other mimic states. Diagnostic performance was compared to threshold-based assessments of volumetric and strain-based CMR markers, in addition to baseline clinical patient characteristics. Threshold-based measures provided modest performance, the greatest area under the curve (AUC) being 0.70. Global strain parameters exhibited reduced performance, with AUC under 0.64. A neural network trained exclusively from 3D-MDA data achieved an AUC of 0.94 (sensitivity 0.92, specificity 0.90) when performing the same task. This study demonstrates that ML-based diagnosis of cardiomyopathy states performed exclusively from 3D-MDA is feasible and can distinguish HCM from mimic disease states. These findings suggest strong potential for computer-assisted diagnosis in clinical practice.

MRI for patients with cardiac implantable electronic devices: simplifying complexity with a 'one-stop' service model.

BACKGROUND: Patients with cardiac pacemakers and defibrillators are disadvantaged because of poor access to MRI scans, leading to late and misdiagnosis particularly for cancer and neurological disease. New technology allied to tested protocols now allows safe MRI scanning of such patients; however, logistical barriers persist. AIM: To deliver a streamlined sustainable service that provides timely MRI scans to patients with cardiac implantable electronic devices (CIEDs). METHODS: Patients requested a 'one-stop' service for MRI, whereby devices could be reprogrammed and scans acquired at a single location and visit. To provide this 'one-stop' service, we trained a team including administrators, physicians, cardiac physiologists and radiographers. A standard protocol was used to prevent unnecessary request refusals and delays to scheduling. Service volume, waiting time and safety were analysed 6 months before and 2 years after service redesign. Waiting times for internal and external inpatient referrals plus time to treatment for patients on a cancer pathway were analysed. RESULTS: 215 MRI scans were performed over 2 years. After service redesign, MRI provision increased six-fold to 20 times the national average with reduced waiting time from 60 to 15 days and no adverse events. Departmental throughput was maintained. 85 (40%) referrals were external. 41 (19%) inpatients were scanned, reducing bed-stay by 3 days for internal referrals. 24 (11%) scans were for suspected cancer, 83% allowed treatment within the national standard of 62 days. There was no preintervention service for either inpatients or suspected cancer investigation. CONCLUSION: Implementation of a 'one-stop' service model to provide MRI for patients with CIEDs is safe, streamlined, scalable and has reduced delays making economic and clinical sense. Protocols and checklists are available at mrimypacemaker.com.

Clinical impact of cardiovascular magnetic resonance with optimized myocardial scar detection in patients with cardiac implantable devices.

BACKGROUND: Myocardial scar assessment using late gadolinium enhancement Cardiovascular Magnetic Resonance (LGE CMR) is commonly indicated for patients with cardiac implantable electronic devices (CIEDs), however metal artifact can degrade images. We evaluated the clinical impact of LGE CMR incorporating a device-dependent metal artifact reduction strategy in patients with CIEDs. METHODS: 136 CMR studies were performed in 133 consecutive patients (age 56 ±â€¯19 years, 69% male) with CIEDs (22% implantable loop recorders [ILRs], 40% permanent pacemakers [PPMs], 38% implantable cardioverter defibrillators [ICDs]; 42% non-MRI conditional) over 2 years, without complication. LGE imaging was tailored to the CIED, using a wideband sequence for left-sided PPMs and ICDs and conventional sequences for ILRs and right-sided PPMs, scoring segmental artifact. Diagnostic utility and impact on clinical management were scored by consensus of experts. RESULTS: CMR provided unexpected diagnoses in 22 (16%) and changed management in 113 (83%) patients. Myocardial scar was present in 92 (68%), with other abnormalities detected in another 13%. Using conventional LGE, 43 (32%) studies were non-diagnostic (79% of defibrillators) compared to 0% using wideband LGE imaging. Wideband LGE results changed clinical management in an additional 39 (75%) defibrillator patients and 10 (19%) pacemaker patients when compared to imaging with conventional LGE sequences. CONCLUSION: The clinical yield from CMR using optimized LGE sequences in patients with CIEDs is high with no demonstrated clinical risk. A device-dependent LGE imaging strategy using wideband LGE is needed to achieve clinical utility especially in ICD recipients.