Awais Sheikh

Background: Transthyretin-lowering therapies target the underlying cause of transthyretin amyloid cardiomyopathy (ATTR-CM), but their effects on myocardial amyloid burden and cardiac remodelling remain incompletely characterized. We evaluated the myocardial effects of sustained transthyretin suppression with eplontersen using serial multiparametric cardiovascular magnetic resonance (CMR). Methods: This CMR study was a pre-specified, international, prospective study embedded within the randomized, placebo-controlled phase 3 CARDIO-TTRansform trial. Participants underwent CMR at baseline and planned assessments at weeks 25, 49, 97, and 140. The primary imaging analysis evaluated longitudinal changes in CMR parameters using mixed models for repeated measures. Myocardial amyloid regression and progression were defined as ≥5 percentage-point decreases and increases in extracellular volume (ECV), respectively. All other patients were considered stable. Results: We enrolled 120 participants with baseline and ≥1 follow-up CMR assessment (eplontersen, n=58; placebo, n=62; mean age, 76.7±6.5 years; 95% male; baseline stabilizer, 12.5%). At week 140 eplontersen resulted in lower ECV, compared with placebo (least-squares mean difference [95% CI], –6.7 percentage points [ –8.7 to –4.7]; P<0.001) and lower native T1 ( –25.9ms [–41.7 to –10.2 ]; P=0.002) and left ventricular mass (–19.1g [–28.0 to –10.2]; P<0.001). Eplontersen also increased left ventricular ejection fraction (+5.1% [1.8–8.3]; P=0.002), right ventricular ejection fraction (+4.6% [0.6–8.7]; P=0.03), and left ventricular global longitudinal, circumferential, and radial strain and right ventricular longitudinal strain (all P<0.05), compared to placebo. At week-140, myocardial amyloid regression occurred in 12.5% receiving eplontersen versus 0% receiving placebo, whereas progression occurred in 12.5% versus 76%, respectively. Conclusions: Sustained transthyretin suppression with eplontersen resulted in substantially lower myocardial ECV and favorable changes in cardiac structure, biventricular systolic function, and myocardial deformation compared with placebo in a cohort of patients mostly not on background stabilizer at baseline. These findings provide randomized evidence that lowering circulating transthyretin modifies myocardial disease progression in ATTR-CM and support serial multiparametric CMR for characterizing myocardial treatment response.

BACKGROUND: Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the imaging overlap between CA and more prevalent causes of a hypertrophic phenotype. This study sought to (1) evaluate the performance of artificial-intelligence (AI) derived measurements incorporated into the established multiparametric echocardiographic scoring system to detect CA; (2) develop and validate an AI-based deep-learning model for video-based detection of CA on echocardiography. METHODS: The study population comprised 5776 patients (CA, 2756; controls, 3020). The training data set included patients from the UK National Amyloidosis Center and Taiwan MacKay Memorial Hospital (CA, 2241; controls, 2130). External test data sets were obtained from the US Duke University Health System (CA, 334; left ventricular hypertrophy controls, 668) and Japan National Cerebral and Cardiovascular Center (CA, 181; left ventricular hypertrophy controls, 222). RESULTS: The multiparametric echocardiographic score computed using AI-derived measurements achieved an accuracy of 79.5% (sensitivity, 75.4%; specificity, 81.5%) in the United States cohort and 79.7% (sensitivity, 81.6%; specificity, 78.1%) in the Japan cohort. The deep-learning model demonstrated accuracies of 96.2% (sensitivity, 96.8%; specificity, 95.7%) and 95.8% (sensitivity, 97.3%; specificity, 94.3%) in the internal validation and internal test sets, respectively. External validation of the deep-learning model showed accuracies of 87.5% (sensitivity, 86.6%; specificity, 87.9%) in the United States and 88.4% (sensitivity, 92.3%; specificity, 85.3%) in the Japanese cohort. Subgroup analysis demonstrated that the deep-learning model showed robust discrimination of CA from other hypertrophic phenocopies: CA versus hypertension (area under the curve [AUC], 0.92 [95% CI, 0.91–0.94]), CA versus hypertrophic cardiomyopathy (AUC, 0.91 [95% CI, 0.87–0.94]), CA versus aortic stenosis (AUC, 0.93 [95% CI, 0.90–0.95]), CA versus chronic kidney disease (AUC, 0.93 [95% CI, 0.91–0.95]). The deep-learning model was able to classify a greater proportion of patients compared with the AI-derived multiparametric echocardiographic score and achieved superior diagnostic accuracy (AUC, 0.93 [95% CI, 0.91–0.95] versus AUC, 0.88 [95% CI, 0.85–0.90]; P <0.001). CONCLUSIONS: Both the multiparametric echocardiographic score computed from AI-derived measurements and the fully automated deep-learning model can accurately identify patients with CA in globally diverse cohorts, with the deep-learning model providing superior performance.

Corona Virus Disease 2019 (COVID-19) pneumonitis associated with severe respiratory failure carries a high mortality. Coagulopathy has emerged as a significant contributor to thrombotic complications. We describe two cases of severe COVID-19 pneumonitis refractory to conventional mechanical ventilation and proning position, transferred to our specialist centre for cardiorespiratory failure. Cross-sectional imaging demonstrated concurrent venous and aortic thrombosis with end-organ ischaemic changes. One patient received thrombolysis with a partial response. This could not be offered to the other patient due to a recent haemorrhagic event. Both patients died of multi-organ failure in the hospital. Concurrent aortic and venous thromboses are rare. This finding in COVID-19 cases, who were both critically ill patients, likely reflects the strongly thrombogenic nature of this illness which ultimately contributed to poor outcomes. The absence of deep vein thrombosis or a potential systemic source of embolism suggests in situ thrombosis. Further, the management of anticoagulation and thrombolysis is challenging in patients where an attendant bleeding risk exists.