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Long-term Kidney Function Recovery and Mortality After COVID-19-associated Acute Kidney Injury: an International Multi-centre Observational Cohort Study
Abstract: Background While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings Advanced age (HR 2.77, 95%CI 2.53-3.04, p 0.0001), severe COVID-19 (HR 2....
Improved Upper Limb Function in Non-ambulant Children with SMA Type 2 and 3 During Nusinersen Treatment: a Prospective 3-years SMArtCARE Registry Study
Abstract: Background The development and approval of disease modifying treatments have dramatically changed disease progression in patients with spinal muscular atrophy (SMA). Nusinersen was approved in Europe in 2017 for the treatment of SMA patients irrespective of age and disease severity. Most data on therapeutic efficacy are available for the infantile-onset SMA. For patients with SMA type 2 and type 3, there is still a lack of sufficient evidence and long-term experience for nusinersen treatment. Here, we report data from the SMArtCARE registry of non-ambulant children with SMA type 2 and typen 3 under nusinersen treatment with a follow-up period of up to 38 months. Methods SMArtCARE i...
Modeling Activity Tracker Data Using Deep Boltzmann Machines
Abstract: Commercial activity trackers are set to become an essential tool in health research, due to increasing availability in the general population. The corresponding vast amounts of mostly unlabeled data pose a challenge to statistical modeling approaches. To investigate the feasibility of deep learning approaches for unsupervised learning with such data, we examine weekly usage patterns of Fitbit activity trackers with deep Boltzmann machines (DBMs). This method is particularly suitable for modeling complex joint distributions via latent variables. We also chose this specific procedure because it is a generative approach, i.e., artificial samples can be generated to explore the learned structure. We describe how the data can be preprocessed to be compatible with binary DBMs. The results reveal two distinct usage patterns in which one group frequently uses trackers on Mondays and Tuesdays, whereas the other uses trackers during the entire week. This exemplary result shows that DBMs are feasible and can be useful for modeling activity tracker data
Improvements in Walking Distance During Nusinersen Treatment - a Prospective 3-year SMArtCARE Registry Study
Abstract: Background and objectives: Disease progression in patients with spinal muscular atrophy (SMA) has changed dramatically within the past years due to the approval of three different disease-modifying treatments. Nusinersen was the first drug to be approved for the treatment of SMA patients. Clinical trials provided data from infants with SMA type 1 and children with SMA type 2, but there is still insufficient evidence and only scarcely reported long-term experience for nusinersen treatment in ambulant patients. Here, we report data from the SMArtCARE registry of ambulant patients under nusinersen treatment with a follow-up period of up to 38 months. Methods: SMArtCARE is a disease-sp...
