Long-term outcome in patients with takotsubo syndrome : A single center study from Vienna.

BACKGROUND: There is an increasing amount of evidence suggesting multiple fatal complications in takotsubo syndrome; however, findings on the long-term outcome are scarce and show inconsistent evidence. METHODS: This is a single center study of long-term prognosis in takotsubo patients admitted to the Klinik Ottakring, Vienna, Austria, from September 2006 to August 2019. We investigated the clinical features, prognostic factors and outcome of patients with takotsubo syndrome. Furthermore, survivors and non-survivors and patients with a different cause of death were compared. RESULTS: Overall, 147 patients were included in the study and 49 takotsubo patients (33.3%) died during the follow-up, with a median of 126 months. The most common cause of death was a non-cardiac cause (71.4% of all deaths), especially malignancies (26.5% of all deaths). Moreover, non-survivors were older and more often men with more comorbidities (chronic kidney disease, malignancy). Patients who died because of cardiovascular disease were older and more often women than patients who died due to non-cardiovascular cause. Adjusted analysis showed no feature of an independent predictor of cardiovascular mortality for takotsubo patients. Female gender (HR = 0.32, CI: 0.16-0.64, p < 0.001), cancer (HR = 2.35, CI: 1.15-4.8, p = 0.019) and chronic kidney disease (HR = 2.61, CI: 1.11-6.14, p = 0.028) showed to be independent predictors of non-cardiovascular mortality. CONCLUSION: Long-term prognosis of takotsubo patients is not favorable, mainly due to noncardiac comorbidities. Hence, consequent outpatient care in regular intervals after a takotsubo event based on risk factor control and early detection of malignancies seems justified.

Artificial intelligence-driven design of fuel mixtures.

High-performance fuel design is imperative to achieve cleaner burning and high-efficiency engine systems. We introduce a data-driven artificial intelligence (AI) framework to design liquid fuels exhibiting tailor-made properties for combustion engine applications to improve efficiency and lower carbon emissions. The fuel design approach is a constrained optimization task integrating two parts: (i) a deep learning (DL) model to predict the properties of pure components and mixtures and (ii) search algorithms to efficiently navigate in the chemical space. Our approach presents the mixture-hidden vector as a linear combination of each single component's vectors in each blend and incorporates it into the network architecture (the mixing operator (MO)). We demonstrate that the DL model exhibits similar accuracy as competing computational techniques in predicting the properties for pure components, while the search tool can generate multiple candidate fuel mixtures. The integrated framework was evaluated to showcase the design of high-octane and low-sooting tendency fuel that is subject to gasoline specification constraints. This AI fuel design methodology enables rapidly developing fuel formulations to optimize engine efficiency and lower emissions.