Manganese-Enhanced Magnetic Resonance Imaging in Takotsubo Syndrome.

BACKGROUND: Takotsubo syndrome is an acute cardiac emergency characterized by transient left ventricular systolic dysfunction typically following a stressful event. Despite its rapidly rising incidence, its pathophysiology remains poorly understood. Takotsubo syndrome may pass unrecognized, especially if timely diagnostic imaging is not performed. Defective myocardial calcium homeostasis is a central cause of contractile dysfunction and has not been explored in takotsubo syndrome. We aimed to investigate myocardial calcium handling using manganese-enhanced magnetic resonance imaging during the acute and recovery phases of takotsubo syndrome. METHODS: Twenty patients with takotsubo syndrome (63±12 years of age; 90% female) and 20 volunteers matched on age, sex, and cardiovascular risk factors (59±11 years of age; 70% female) were recruited from the Edinburgh Heart Centre between March 2020 and October 2021. Patients underwent gadolinium and manganese-enhanced magnetic resonance imaging during index hospitalization with repeat manganese-enhanced magnetic resonance imaging performed after at least 3 months. RESULTS: Compared with matched control volunteers, patients had a reduced left ventricular ejection fraction (51±11 versus 67±8%; P<0.001), increased left ventricular mass (86±11 versus 57±14 g/m2; P<0.001), and, in affected myocardial segments, elevated native T1 (1358±49 versus 1211±28 ms; P<0.001) and T2 (60±7 versus 38±3 ms; P<0.0001) values at their index presentation. During manganese-enhanced imaging, kinetic modeling demonstrated a substantial reduction in myocardial manganese uptake (5.1±0.5 versus 8.2±1.1 mL/[100 g of tissue ·min], respectively; P<0.0001), consistent with markedly abnormal myocardial calcium handling. After recovery, left ejection fraction, left ventricular mass, and T2 values were comparable with those of matched control volunteers. Despite this, native and postmanganese T1 and myocardial manganese uptake remained abnormal compared with matched control volunteers (6.6±0.5 versus 8.2±1.1 mL/[100 g of tissue ·min]; P<0.0001). CONCLUSIONS: In patients with takotsubo syndrome, there is a profound perturbation of myocardial manganese uptake, which is most marked in the acute phase but persists for at least 3 months despite apparent restoration of normal left ventricular ejection fraction and resolution of myocardial edema, suggesting abnormal myocardial calcium handling may be implicated in the pathophysiology of takotsubo syndrome. Manganese-enhanced magnetic resonance imaging has major potential to assist in the diagnosis, characterization, and risk stratification of patients with takotsubo syndrome. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04623788.

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82-0.84) and a balanced accuracy of 0.78 (95% CI 0.77-0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40-0.74). Quantitative PCR validated LEF1-AS1's adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.