Impact of Coronary Microvascular Dysfunction in Takotsubo Syndrome: Cause, Consequence or Both?

Takotsubo syndrome (TTS) is an acute cause of heart failure characterized by a reversible left ventricular (LV) impairment usually induced by a physical or emotional trigger. TTS is not always a benign disease since it is associated with a relatively higher risk of life-threatening complications, such as cardiogenic shock, ventricular arrhythmias, respiratory failure, cardiopulmonary resuscitation and death. Despite notable advancements in the management of patients with TTS, physiopathological mechanisms underlying transient LV dysfunction remain largely unknown. Since TTS carries similar prognostic implications than acute myocardial infarction, the identification of mechanisms and predictors of worse prognosis remain key to establish appropriate treatments. The greater prevalence of TTS among post-menopausal women and the activation of the neuro-cardiac axis triggered by physical or emotional stressors paved the way forward to several studies focused on coronary microcirculation and impaired blood flow as the main physiopathological mechanisms of TTS. However, whether microvascular dysfunction is the cause or a consequence of transient LV impairment remains still unsettled. This review provides an up-to-date summary of available evidence supporting the role of microvascular dysfunction in TTS pathogenesis, summarizing contemporary invasive and non-invasive diagnostic techniques for its assessment. We will also discuss novel techniques focused on microvascular dysfunction in TTS which may support clinicians for the implementation of tailored treatments.

Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry.

Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant mitral regurgitation or left ventricular (LV) dysfunction undergoing late gadolinium enhancement (LGE) cardiac MRI between October 2007 and June 2020 in 15 European tertiary centers. The study end point was a composite of sustained ventricular tachycardia, (aborted) sudden cardiac death, or unexplained syncope. Unsupervised data-driven hierarchical k-mean algorithm was utilized to identify phenotypic clusters. The association between clusters and the study end point was assessed by Cox proportional hazards model. Results A total of 474 patients (mean age, 47 years ± 16 [SD]; 244 female, 230 male) with two phenotypic clusters were identified. Patients in cluster 2 (199 of 474, 42%) had more severe mitral valve degeneration (ie, bileaflet MVP and leaflet displacement), left and right heart chamber remodeling, and myocardial fibrosis as assessed with LGE cardiac MRI than those in cluster 1. Demographic and clinical features (ie, symptoms, arrhythmias at Holter monitoring) had negligible contribution in differentiating the two clusters. Compared with cluster 1, the risk of developing the study end point over a median follow-up of 39 months was significantly higher in cluster 2 patients (hazard ratio: 3.79 [95% CI: 1.19, 12.12], P = .02) after adjustment for LGE extent. Conclusion Among patients with MVP without significant mitral regurgitation or LV dysfunction, unsupervised machine learning enabled the identification of two phenotypic clusters with distinct arrhythmic outcomes based primarily on cardiac MRI features. These results encourage the use of in-depth imaging-based phenotyping for implementing arrhythmic risk prediction in MVP. Keywords: MR Imaging, Cardiac, Cardiac MRI, Mitral Valve Prolapse, Cluster Analysis, Ventricular Arrhythmia, Sudden Cardiac Death, Unsupervised Machine Learning Supplemental material is available for this article. © RSNA, 2024.

Cardiac magnetic resonance in advanced heart failure.

Heart failure (HF) is a chronic and progressive disease that often progresses to an advanced stage where conventional therapy is insufficient to relieve patients' symptoms. Despite the availability of advanced therapies such as mechanical circulatory support or heart transplantation, the complexity of defining advanced HF, which requires multiple parameters and multimodality assessment, often leads to delays in referral to dedicated specialists with the result of a worsening prognosis. In this review, we aim to explore the role of cardiac magnetic resonance (CMR) in advanced HF by showing how CMR is useful at every step in managing these patients: from diagnosis to prognostic stratification, hemodynamic evaluation, follow-up and advanced therapies such as heart transplantation. The technical challenges of scanning advanced HF patients, which often require troubleshooting of intracardiac devices and dedicated scans, will be also discussed.

Cardiac Magnetic Resonance to Predict Cardiac Mass Malignancy: The CMR Mass Score.

BACKGROUND: Multimodality imaging is currently suggested for the noninvasive diagnosis of cardiac masses. The identification of cardiac masses' malignant nature is essential to guide proper treatment. We aimed to develop a cardiac magnetic resonance (CMR)-derived model including mass localization, morphology, and tissue characterization to predict malignancy (with histology as gold standard), to compare its accuracy versus the diagnostic echocardiographic mass score, and to evaluate its prognostic ability. METHODS: Observational cohort study of 167 consecutive patients undergoing comprehensive echocardiogram and CMR within 1-month time interval for suspected cardiac mass. A definitive diagnosis was achieved by histological examination or, in the case of cardiac thrombi, by histology or radiological resolution after adequate anticoagulation treatment. Logistic regression was performed to assess CMR-derived independent predictors of malignancy, which were included in a predictive model to derive the CMR mass score. Kaplan-Meier curves and Cox regression were used to investigate the prognostic ability of predictors. RESULTS: In CMR, mass morphological features (non-left localization, sessile, polylobate, inhomogeneity, infiltration, and pericardial effusion) and mass tissue characterization features (first-pass perfusion and heterogeneity enhancement) were independent predictors of malignancy. The CMR mass score (range, 0-8 and cutoff, ≥5), including sessile appearance, polylobate shape, infiltration, pericardial effusion, first-pass contrast perfusion, and heterogeneity enhancement, showed excellent accuracy in predicting malignancy (areas under the curve, 0.976 [95% CI, 0.96-0.99]), significantly higher than diagnostic echocardiographic mass score (areas under the curve, 0.932; P=0.040). The agreement between the diagnostic echocardiographic mass and CMR mass scores was good (κ=0.66). A CMR mass score of ≥5 predicted a higher risk of all-cause death (P<0.001; hazard ratio, 5.70) at follow-up. CONCLUSIONS: A CMR-derived model, including mass morphology and tissue characterization, showed excellent accuracy, superior to echocardiography, in predicting cardiac masses malignancy, with prognostic implications.

Acute Response of the Noninfarcted Myocardium and Surrounding Tissue Assessed by T2 Mapping After STEMI.

BACKGROUND: ST-segment elevation myocardial infarction (STEMI) is associated with a systemic and local inflammatory response with edema. However, their role at the tissue level is poorly characterized. OBJECTIVES: This study aims to characterize T2 values of the noninfarcted myocardium (NIM) and surrounding tissue and to investigate prognostic relevance of higher NIM T2 values after STEMI. METHODS: A total of 171 consecutive patients with STEMI without prior cardiovascular events who underwent cardiac magnetic resonance after primary percutaneous coronary intervention were analyzed in terms of standard infarct characteristics. Edema of the NIM, liver, spleen, and pectoralis muscle was assessed based on T2 mapping. Follow-up was available for 130 patients. The primary endpoint was major adverse cardiac events (MACE), defined as cardiovascular death, myocardial infarction, unplanned coronary revascularization or rehospitalization for heart failure. The median time from primary percutaneous coronary intervention to cardiac magnetic resonance was 3 days (IQR: 2-5 days). RESULTS: Higher (above the median value of 45 ms) T2 values in the NIM area were associated with larger infarct size, microvascular obstruction, and left ventricular dysfunction and did not correlate with C-reactive protein, white blood cells, or T2 values of the pectoralis muscle, liver, and spleen. At a median follow-up of 17 months, patients with higher (>45 ms) NIM T2 values had increased risk of MACE (P < 0.001) compared with subjects with NIM T2 values ≤45 ms, mainly caused by a higher rate of myocardial reinfarction (26.3% vs 1.4%; P < 0.001). At multivariable analysis, higher NIM T2 values independently predicted MACE (HR: 2.824 [95% CI: 1.254-6.361]; P = 0.012). CONCLUSIONS: Higher NIM T2 values after STEMI are independently associated with worse cardiovascular outcomes, mainly because of higher risk of myocardial infarction.