Predicting anti-TNF treatment response in rheumatoid arthritis: An artificial intelligence-driven model using cytokine profile and routine clinical practice parameters

Introduction: Rheumatoid arthritis (RA) is a heterogeneous disease in which therapeutic strategies used have evolved dramatically. Despite significant progress in treatment strategies such as the development of anti-TNF drugs, it is still not possible to differentiate those patients who will respond from who will not. This can lead to effective-treatment delays and unnecessary costs. The aim of this study was to utilize a profile of the patient's characteristics, clinical parameters, immune status (cytokine profile) and artificial intelligence to assess the feasibility of developing a tool that could allow us to predict which patients will respond to treatment with anti-TNF drugs. Methods: This study included 38 patients with RA from the RA-Paz cohort. Clinical activity was measured at baseline and after 6 months of treatment. The cytokines measured before the start of anti-TNF treatment were IL-1, IL-12, IL-10, IL-2, IL-4, IFNg, TNFa, and IL-6. Statistical analyses were performed using the Wilcoxon-Rank-Sum Test and the Benjamini-Hochberg method. The predictive model viability was explored using the 5-fold cross-validation scheme in order to train the logistic regression models. Results: Statistically significant differences were found in parameters such as IL-6, IL-2, CRP and DAS-ESR. The predictive model performed to an acceptable level in correctly classifying patients (ROC-AUC 0.804167 to 0.891667), suggesting that it would be possible to develop a clinical classification tool. Conclusions: Using a combination of parameters such as IL-6, IL-2, CRP and DAS-ESR, it was possible to develop a predictive model that can acceptably discriminate between remitters and non-remitters. However, this model needs to be replicated in a larger cohort to confirm these findings

A eficácia informativa da demonstração do valor adicionado

O cenário mundial contemporâneo vem sendo caracterizado por significativas mudanças nas áreas social, política e econômica, com reflexos diretos no ambiente empresarial, através da imposição de novos padrões de competitividade e uma necessidade de alterações nos processos de trabalho e nas práticas de gestão. Neste contexto, a informação contábil tradicional não responde totalmente às demandas novas dos usuários com interesses diferentes daqueles contemplados nos relatórios contábeis clássicos. A Demonstração do Valor Adicionado é parte integrante deste novo grupo de relatórios desenvolvidos pela contabilidade para assistir melhor essas necessidades emergentes, visando, principalmente, a evidenciar o papel social das empresas, apresentando claramente a riqueza gerada, para que toda a sociedade conheça sua função positiva na criação de valor para a comunidade. Este trabalho apresenta aspectos relacionados ao valor adicionado pela atividade empresarial e analisa a importância deste relatório ao facilitar o entendimento da informação sócio-econômica sobre a companhia e sua relação com o ambiente onde está localizada.<br>Nowadays, the global environment is characterized by significant changes in the social, political and economic areas with direct impacts on managerial activities, through the imposition of new patterns of competitiveness and a need to change work processes and managerial practices. In this context, the traditional accounting information does not totally attend the users' new demands, whose interests are different from those contemplated in the traditional accounting reports. The Value Added Statement is part of this new group of accounting reports created to attend to those emerging needs in a better way, mainly seeking to disclose companies' social activities, clearly presenting the generated wealth so that the whole society knows about the its positive function in the creation of value for the community. This paper presents aspects related to the value added by enterprises and analyzes the importance of this report in making it easier to understand socioeconomic information about the company and its relationship with the environment in which it is located