Penningtonnunez2119
Biological disease-modifying anti-rheumatic drugs (bDMARDs) can be tapered in some rheumatoid arthritis (RA) patients in sustained remission. The purpose of this study was to assess the feasibility of building a model to estimate the individual flare probability in RA patients tapering bDMARDs using machine learning methods.
Longitudinal clinical data of RA patients on bDMARDs from a randomized controlled trial of treatment withdrawal (RETRO) were used to build a predictive model to estimate the probability of a flare. Four basic machine learning models were trained, and their predictions were additionally combined to train an ensemble learning method, a stacking meta-classifier model to predict the individual flare probability within 14 weeks after each visit. Prediction performance was estimated using nested cross-validation as the area under the receiver operating curve (AUROC). Predictor importance was estimated using the permutation importance approach.
Data of 135 visits from 41 patients were included. A model selection approach based on nested cross-validation was implemented to find the most suitable modeling formalism for the flare prediction task as well as the optimal model hyper-parameters. Moreover, an approach based on stacking different classifiers was successfully applied to create a powerful and flexible prediction model with the final measured AUROC of 0.81 (95%CI 0.73-0.89). The percent dose change of bDMARDs, clinical disease activity (DAS-28 ESR), disease duration, and inflammatory markers were the most important predictors of a flare.
Machine learning methods were deemed feasible to predict flares after tapering bDMARDs in RA patients in sustained remission.
Machine learning methods were deemed feasible to predict flares after tapering bDMARDs in RA patients in sustained remission.
Nurturing care interventions have the potential to promote health and development in early childhood. Amagugu Asakhula was designed to promote developmentally important dietary and movement behaviours among children of preschool age (3-5 years) in South Africa. An initial formative study in Cape Town found the intervention to be feasible and acceptable when delivered by community health workers (CHWs) linked to a community-based organisation. This study evaluated the delivery of the Amagugu Asakhula intervention by CHWs linked to a public sector primary health care facility in Soweto, as this mode of delivery could have more potential for sustainability and scalability.
A qualitative design was utilised to assess feasibility, acceptability, adoption, appropriateness, implementation, fidelity and context. CHWs (n = 14) delivered the intervention to caregivers (n = 23) of preschool-age children in Soweto over 6 weeks. Following the completion of the intervention, focus group discussions were held with CHWs effectiveness when delivered by CHWs linked to community-based organisations. The present study further demonstrates how implementation challenges can be identified through qualitative feasibility studies and subsequently addressed prior to large-scale trials, avoiding the wasting of research and resources.
Based on these findings, delivery of the Amagugu Asakhula intervention is not recommended through public sector CHWs in South Africa. This feasibility study informs the optimisation of implementation and supports further testing of the intervention's effectiveness when delivered by CHWs linked to community-based organisations. The present study further demonstrates how implementation challenges can be identified through qualitative feasibility studies and subsequently addressed prior to large-scale trials, avoiding the wasting of research and resources.
Disease outbreak not only carries the risk of death to the public due to the infection, but it also can lead to unbearable psychological impact on the mental health of the individuals. This study aims to explore and evaluate the burden of psychological problems on the Iranian general population during the outbreak of COVID-19.
A cross-sectional web-based survey was conducted among the general population of Iran age 15 and above. Demographic variables, depression, and anxiety symptoms were evaluated using the Patient Health Questionnaire-9 and General Anxiety Disorder-7 questionnaires.
Among the 8591 participants, the mean age was 34.37 (± 11.25) years and 66.4% were female while 33.6% were male. Based on our results, 1295 (15.1%) and 1733 (20.1%) of the general populationhad clinically significant depressive and anxiety symptoms, respectively. Based on the demographic variables, female gender was associated with a higher risk for developing depression and anxiety symptoms, whereas getting information about the disease from medical journals and articles, being older, and being married were considered as associated protective factors. In terms of depression, being a healthcare worker was an associated risk factor. On the other hand, for anxiety, having higher education was a protective factor while a higher number of individuals in a household was considered as a risk factor.
This study identified a major mental health problem in the Iranian population during the time of the COVID-19 outbreak. learn more Therefore, establishing a targeted mental health support program during the time of public emergencies, such as the disease outbreak, is advised.
This study identified a major mental health problem in the Iranian population during the time of the COVID-19 outbreak. Therefore, establishing a targeted mental health support program during the time of public emergencies, such as the disease outbreak, is advised.Less than a year since the start of the COVID-19 pandemic, ten vaccines against SARS-CoV-2 have been approved for at least limited use, with over sixty others in clinical trials. This swift achievement has generated excitement and arrives at a time of great need, as the number of COVID-19 cases worldwide continues to rapidly increase. Two vaccines are currently approved for full use, both built on mRNA and lipid nanotechnology platforms, a success story of mRNA technology 20 years in the making. For patients with cancer, questions arise around the safety and efficacy of these vaccines in the setting of immune alterations engendered by their malignancy and/or therapies. We summarize the current data on leading COVID-19 vaccine candidates and vaccination of patients undergoing immunomodulatory cancer treatments. Most current cancer therapeutics should not prevent the generation of protective immunity. We call for more research in this area and recommend that the majority of patients with cancer receive COVID vaccinations when possible.
