The EPIRARE proposal of a set of indicators and common data elements for the European platform for rare disease registration

BACKGROUND: The European Union acknowledges the relevance of registries as key instruments for developing rare disease (RD) clinical research, improving patient care and health service (HS) planning and funded the EPIRARE project to improve standardization and data comparability among patient registries and to support new registries and data collections. METHODS: A reference list of patient registry-based indicators has been prepared building on the work of previous EU projects and on the platform stakeholders' information needs resulting from the EPIRARE surveys and consultations. The variables necessary to compute these indicators have been analysed for their scope and use and then organized in data domains. RESULTS: The reference indicators span from disease surveillance, to socio-economic burden, HS monitoring, research and product development, policy equity and effectiveness. The variables necessary to compute these reference indicators have been selected and, with the exception of more sophisticated indicators for research and clinical care quality, they can be collected as data elements common (CDE) to all rare diseases. They have been organized in data domains characterized by their contents and main goal and a limited set of mandatory data elements has been defined, which allows case notification independently of the physician or the health service. CONCLUSIONS: The definition of a set of CDE for the European platform for RD patient registration is the first step in the promotion of the use of common tools for the collection of comparable data. The proposed organization of the CDE contributes to the completeness of case ascertainment, with the possible involvement of patients and patient associations in the registration process.This work is part of the activities of the project titled “Building Consensus and synergies for the EU Registration of Rare Disease Patients” (EPIRARE), funded by the European Commission within the framework of the Health Project, Work Plan 2010 (Grant n. 20101202).S

Application of Deep Learning Model in the Sonographic Diagnosis of Uterine Adenomyosis

Background: This study aims to evaluate the diagnostic performance of Deep Learning (DL) machine for the detection of adenomyosis on uterine ultrasonographic images and compare it to intermediate ultrasound skilled trainees. Methods: Prospective observational study were conducted between 1 and 30 April 2022. Transvaginal ultrasound (TVUS) diagnosis of adenomyosis was investigated by an experienced sonographer on 100 fertile-age patients. Videoclips of the uterine corpus were recorded and sequential ultrasound images were extracted. Intermediate ultrasound-skilled trainees and DL machine were asked to make a diagnosis reviewing uterine images. We evaluated and compared the accuracy, sensitivity, positive predictive value, F1-score, specificity and negative predictive value of the DL model and the trainees for adenomyosis diagnosis. Results: Accuracy of DL and intermediate ultrasound-skilled trainees for the diagnosis of adenomyosis were 0.51 (95% CI, 0.48–0.54) and 0.70 (95% CI, 0.60–0.79), respectively. Sensitivity, specificity and F1-score of DL were 0.43 (95% CI, 0.38–0.48), 0.82 (95% CI, 0.79–0.85) and 0.46 (0.42–0.50), respectively, whereas intermediate ultrasound-skilled trainees had sensitivity of 0.72 (95% CI, 0.52–0.86), specificity of 0.69 (95% CI, 0.58–0.79) and F1-score of 0.55 (95% CI, 0.43–0.66). Conclusions: In this preliminary study DL model showed a lower accuracy but a higher specificity in diagnosing adenomyosis on ultrasonographic images compared to intermediate-skilled trainees

Using vegetation dynamics to face the challenge of the conservation status assessment in semi-natural habitats

The conservation of semi-natural habitats represents a primary challenge for European nature conservation due to their great species diversity and their vulnerability to ongoing massive land-use changes. As these changes rapidly transform and phase out semi-natural habitats, conservation measures should be prompt and specifically focused on a sound assessment of the degree of conservation. Here we develop a methodological strategy for the assessment of the degree of conservation of semi-natural grasslands based on well-defined criteria rather than on expert opinion. Through mixed effect models, we tested ten potential indicators, encompassing proxies of species composition, habitat structure, and landscape patterns, against a measure of compositional change from habitat favourable condition, i.e., an inverse proxy of conservation status. This measure derives from the re-visitation of 132 sampling units historically sampled between 1966 and 1992 along the Apennines. The compositional change was quantified as the dissimilarity between historical habitat species pools and the composition of current communities. The compositional change was significantly related to the number of habitat diagnostic species and the relative cover of woody species with opposite sign (positive and negative, respectively). We classified and combined the classes of these two indicators in each sampling unit to assess the habitat degree of conservation at the plot and at the Natura 2000 site level. At the plot level, our assessment was in good agreement with the occurrence of species of conservation concern. On the other hand, at the site level, our assessment was not always harmonic with the habitat conservation assessment officially reported for the site investigated

Ecosystem mapping for the implementation of the European biodiversity strategy at the national level. The case of Italy

Several international initiatives, including the European Biodiversity Strategy to 2020, promote the identification and mapping of ecosystems as basic tools for the conservation of biodiversity and related services. On coarse scales, the spatial representation of ecosystems is usually based on broad land cover categories that largely overlook important ecological and biogeographic features of the biotic communities they are meant to exemplify. This paper presents a nationwide ecosystem mapping approach that promotes a degree of thematic detail, which is more suited than that found in the continental maps to meeting biodiversity conservation targets in Italy. The approach is based on the rationale that current and potential vegetation cover are valuable proxies for outlining ecosystems. The resulting Ecosystem Map of Italy includes 43 types of forest ecosystems instead of the 5 woodland, forest and other wooded land types recognized at the European level. We outline the expected advantages of this enhanced thematic detail for a number of conservation purposes and highlight how the resulting maps may help to meet biodiversity conservation targets at the national level. In particular, we refer to the assessment of conservation status, the definition of restoration priorities, the planning of green infrastructure and the identification of collapse risks for the ecosystems identified. Comprehensively, the definition, characterization and assessment of ecosystem types represent the carrying structure of the recently launched national system of natural capital accounting

3D Patient-Specific Virtual Models for Presurgical Planning in Patients with Recto-Sigmoid Endometriosis Nodules: A Pilot Study

Background and Objective: In recent years, 3D printing has been used to support surgical planning or to guide intraoperative procedures in various surgical specialties. An improvement in surgical planning for recto-sigmoid endometriosis (RSE) excision might reduce the high complication rate related to this challenging surgery. The aim of this study was to build novel presurgical 3D models of RSE nodules from magnetic resonance imaging (MRI) and compare them with intraoperative findings. Materials and Methods: A single-center, observational, prospective, cohort, pilot study was performed by enrolling consecutive symptomatic women scheduled for minimally invasive surgery for RSE between November 2019 and June 2020 at our institution. Preoperative MRI were used for building 3D models of RSE nodules and surrounding pelvic organs. 3D models were examined during multi-disciplinary preoperative planning, focusing especially on three domains: degree of bowel stenosis, nodule's circumferential extension, and bowel angulation induced by the RSE nodule. After surgery, the surgeon was asked to subjectively evaluate the correlation of the 3D model with the intra-operative findings and to express his evaluation as "no correlation", "low correlation", or "high correlation" referring to the three described domains. Results: seven women were enrolled and 3D anatomical virtual models of RSE nodules and surrounding pelvic organs were generated. In all cases, surgeons reported a subjective "high correlation" with the surgical findings. Conclusion: Presurgical 3D models could be a feasible and useful tool to support surgical planning in women with recto-sigmoidal endometriotic involvement, appearing closely related to intraoperative findings

Atlante storico del diritto dei dati. Anni 2020-2022

Raccolta anni 2020-2022 della rubrica Diritto e nuove tecnologie pubblicata sulla rivista Persona e Mercat