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Background: The Angelman Syndrome Registry (RISA) was developed as a retrospective study with the following objectives: to evaluate the clinical history of individuals with Angelman Syndrome (AS) in Italy and compare it with the existing literature; to investigate the feasibility of gathering data by directly involving participants in the data collection process; and to explore the relationship between different symptoms and genotypes. Methods: Established in 2018, RISA enrolled a total of 82 participants, with 62 (75.6%) providing complete data. Demographic, clinical, and genetic information was collected using electronic case report forms. Descriptive statistics characterized the sample, while associations between genotype and clinical characteristics were examined. Results: Descriptive analysis revealed a median participant age of 8.0 years, with males comprising 48.8% of the sample. Deletion (58.1%) was the most common genotype. The majority (82.2%) experienced epilepsy, with seizures typically onset before 3 years of age. Most patients (86.2%) required multiple anti-epileptic drugs for control, with generalized tonic-clonic seizures and atypical absence seizures being most prevalent. The deletion group exhibited more severe developmental delays and a trend towards higher seizure severity. Sleep problems affected 69.4% of participants, characterized by difficulties in sleep onset and maintenance. Conclusions: This study offers valuable insights into the clinical history and genetic characteristics of AS in Italy, consistent with the prior literature. Additionally, it underscores the efficacy of patient registries in capturing comprehensive data on rare diseases such as AS, highlighting their potential to advance research and enhance patient care.
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Introduction: From the perspective of healthcare organizations and public health care systems, the value of a clinical trial can be assessed from a clinical and economical perspective. However, to date, there is no standardized model for systematically capturing the economic value of clinical trials at organizational and system levels. The aim of this study was to develop and test a methodology for estimating the avoided costs deriving from the management of patients as part of a clinical trial. Methods: Our methodology is based on the assumption that the economic value of a clinical trial derives from 1) the funding received by the experimental site from a trial's sponsor, and from 2) the cost avoided by the experimental site with the treatment of patients within a study and not according to standard care by the experimental site. Results: By applying the methodology to onco-hematological clinical trials conducted in two academic hospitals from 2011 to 2016, we demonstrate that savings between 2 million and 4 million euros were achieved over a five-year period. Thus, for every 1,000 euros invested by the pharmaceutical company into the clinical studies conducted at these hospitals, the hospitals saved on average 2,200 euros due to costs not incurred as a result of the trials. Conclusions: The study has proposed and tested a methodology for estimating the economic value of clinical trials by taking into account avoided costs deriving from the treatment of patients enrolled in sponsored trials. The study has proposed a management tool for healthcare institutions to govern clinical trials.
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Medical reports often contain a lot of relevant information in the form of free text. To reuse these unstructured texts for biomedical research, it is important to extract structured data from them. In this work, we adapted a previously developed information extraction system to the oncology domain, to process a set of anatomic pathology reports in the Italian language. The information extraction system relies on a domain ontology, which was adapted and refined in an iterative way. The final output was evaluated by a domain expert, with promising results.