(1) Background: COVID-19 vaccination campaigns offer the best hope of controlling the pandemic. However, the fast production of COVID-19 vaccines has caused concern among the general public regarding their safety and efficacy. In particular, patients with chronic illnesses, such as celiac disease (CD), may be more fearful. Information on vaccine hesitancy plays a pivotal role in the development of an efficient vaccination campaign. In our study, we aimed to evaluate COVID-19 vaccine hesitancy among Italian CD patients. (2) Methods: an anonymous questionnaire was sent to CD patients followed at our tertiary referral center for CD in Milan, Italy. Patients were defined as willing, hesitant and refusing. We evaluated the reasons for hesitancy/refusal and the possible determinants, calculating crude and adjusted odds ratios [AdjORs] with 95% confidence intervals [CIs]. (3) Results: the questionnaire was sent to 346 patients with a response rate of 29.8%. Twenty-six (25.2%) of the 103 respondents were hesitant, with a total refusal rate of 4.8%. The main reason was fear of adverse events related to vaccination (68.2%). Among hesitant patients, 23% declared that their opinion was influenced by their CD. The determinants positively influencing willingness to be vaccinated against COVID-19 were adherence to a GFD, perception of good knowledge about COVID-19 and its vaccines, and a positive attitude to previous vaccines (AdjOR 12.71, 95% CI 1.82–88.58, AdjOR 6.50, 95% CI 1.44–29.22, AdjOR 0.70, 95% CI 0.11–4.34, respectively). (4) Conclusions: CD patients should be vaccinated against COVID-19 and a specific campaign to address the determinants of hesitancy should be developed.

No abstract

Background Telemedicine is one of the major changes that clinicians have encountered over the past decade; in particular during the COVID-19 pandemic, televisits were rapidly implemented to guarantee patients’ assistance, with the intention of Health Care Providers (HCPs) to continue to use them beyond the pandemic. The aim of our national survey was to evaluate the current usage of telemedicine for IBD patients from their perspective, investigating patients’ impressions about telemedicine and factors affecting them through a Machine Learning (ML) analysis. Methods In March 2021, the Italian IBD patients’ association (AMICI Onlus) distributed to their members - through its mailing list and on social media platforms - an anonymous online questionnaire investigating the use of telemedicine. Socio-demographic and IBD characteristics were collected; the usage, patients’ satisfaction and trust of telemedicine were assessed through Likert scales. ML tools - Decision Trees (DT) and Random Forest (RF) - were applied to identify the determinants of patient’s perceptions about telemedicine; the produced RF ranking displays two indicators: %IncMSE and IncNodePurity. Results Nine hundred and seventy-eight IBD patients (women 58.9%) from every Italian region completed the questionnaire. Among the respondents, 87 (8.9%) personally had a telemedicine experience; 153 reported that their Centre performed a telemedicine service during the COVID-19 pandemic (24.2% televisits, 39.2% e-mails, 24.8% phone-calls, 3.9 % dedicated website, 7.9% others). Overall, 707 (72.3%) would trust a telemedicine service, 760 (77.7%) would like to have it also with another HCP (e.g., nutritionist, psychologist) and 778/961 (81%) would like to use telemedicine in the future (17 did not answer to this specific question); 792 (81%) stated they thought useful to have the possibility to use telemedicine and 847 (86.6%) would like their Centre to offer them this facility. Considering this last question as the output at the DT, the variable which have been found to influence the most this patients’ willingness is patient’s perception of the usefulness of telemedicine in treating their disease, since it represented the root of the tree explaining the results. The RF rankings confirmed that this variable influenced the most patients’ perception with the highest levels of %IncMSE and IncNodePurity(Figure 1). Conclusion The practice of telemedicine in the management of IBD patients has not been very relevant throughout Italy so far (less than 10%), but more than four every five respondents would like to use telemedicine. Machine learning analysis shows that the perceived usefulness of telemedicine service is the key point for patients who would like it was a part of usual clinical practice.

No abstract

A major challenge in reservoir characterization is the integration of information at different scale from different sources. Coupling seismic data and depositional model is a way forward to capture uncertainty of complex geological heterogeneities. The main object of this paper is to present an application of reservoir uncertainty evaluation through seismic petrophysics and multi-scenario approach to a green field in early production phase, to handle large-scale geological features variability. Starting from a shared petro-elastic facies model for geological and seismic characterization, advanced geostatistical techniques conditioned by seismic elastic inversion attributes are used to generate multiple scenarios in an automated way. In the proposed methodology, core data, well logs and synthetic elastic curves, are key input to a statistical cluster analysis in order to discriminate facies in the petrophysical, petro-elastic and elastic space of seismic inversion. The petro-elastic facies classification allowed a reliable sedimentological and petrophysical characterization, while assuring the maximum discrimination within the elastic space of inversion with a minimum number of classes. The petro-elastic facies model plays the role of common hard data for reservoir modelling and classification of seismic inversion attributes to facies probabilities. A consistent picture of reservoir heterogeneity is then achieved coupling seismic probabilities and Multiple-Point Statistics (MPS). Seismic probabilities provide low frequency trends for reservoir heterogeneity among wells, while process-based modelling is used to generate Training Images (TI) for MPS, capturing depositional patterns and medium-scale geological heterogeneities. MPS modelling, conditioned by seismic trends, reconstructs turbidite Geo-bodies, honoring depositional patterns and well data. A nested Sequential Indicator Simulation conditioned by seismic is then performed to model reservoir internal architecture by distributing productive facies within Geo-bodies realizations. Seismic also drives modelling of porosity by facies. Petrophysical geostatistical simulations based on facies complete the process. Following this methodology, multiple depositional scenarios were generated, combining different Training Images and seismic probabilities weights in MPS simulations. Process automation makes it possible: 1) to investigate a wide distribution of cases and 2) to converge on a subset of realizations whose reservoir dynamic simulation were fitting to the observed production data. The same workflow is also suitable to handle reservoir static properties variability on the selected scenario, capturing the impact of uncertainties on fluid flow behavior. This integrated approach was successful in increasing the geological realism and consistency compared to a traditional deterministic one. Nevertheless, the presented workflow also fits the requirements of a fast-track strategy accelerating model computation and updating.