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ABSTRACT Objective: to develop and validate a mobile application for the guidance of family members of children and adolescents undergoing treatment with oral antineoplastic drugs. Method: this is methodological research developed in seven steps: search for themes through interviews with twenty-three family members of children and adolescents undergoing oral chemotherapy; theoretical study; mobile application development; validation with thirteen expert judges; adequacy; validation with twelve family members of children and adolescents undergoing oral chemotherapy and mobile application final adaptation. The interviews were analyzed using the Interface de R pour Analyses Multidimensionnelles de Textes et de Questionneires software and validated with a Likert-type scale, considering validated items with a Concordance Index equal to or greater than 70%. Results: the application "Quimio em Casa" was returned with two interfaces, a desktop version aimed at health professionals and the application for family members of children and adolescents undergoing treatment with oral chemotherapy. The contents that guided the application development were storage, handling, administration, specific care for each chemotherapy agent, adverse effects, when to go to the emergency room and daily checking of administrations. The application was designed and validated with an adequacy Concordance Index of 96.7% among judges, and 100% by family members. Conclusion: the application proved to be valid as an educational technology in practical application with family members of children and adolescents undergoing oral chemotherapy.
RESUMEN Objetivo: desarrollar y validar una aplicación móvil para la orientación de familiares de niños y adolescentes en tratamiento con antineoplásicos orales. Método: investigación metodológica desarrollada en siete etapas: búsqueda de temas a través de entrevistas con veintitrés familiares de niños y adolescentes en quimioterapia oral; estudio teórico; desarrollo de aplicaciones móviles; validación con trece jueces expertos; adecuación; validación con doce familiares de niños y adolescentes en quimioterapia oral y adaptación final de la aplicación móvil. Las entrevistas fueron analizadas mediante el software Interface de R pour Analyses Multidimensionnelles de Textes et de Questionneires y validadas con la escala de Likert, considerando ítems con Índice de Concordancia igual o superior al 70% validados. Resultados: se devolvió la aplicación "Quimio em Casa" con dos interfaces, una versión de escritorio dirigida a profesionales de la salud y la aplicación para familiares de niños y adolescentes en tratamiento con quimioterapia oral. Los contenidos que orientaron el desarrollo de la aplicación fueron: almacenamiento; manipulación; administración; el cuidado específico de cada agente de quimioterapia; efectos adversos; cuándo acudir a urgencias y controles diarios de administración. La aplicación fue diseñada y validada con un Índice de Concordancia de Adecuación del 96,7% entre los jueces y del 100% por parte de los familiares. Conclusión: la aplicación demostró ser válida como tecnología educativa en aplicación práctica con familiares de niños y adolescentes en quimioterapia oral.
RESUMO Objetivo: elaborar e validar um aplicativo móvel para a orientação de familiares de crianças e adolescentes em tratamento com antineoplásicos orais. Método: pesquisa metodológica desenvolvida em sete etapas: busca dos temas por meio de entrevistas com vinte e três familiares de crianças e adolescentes em quimioterapia oral; estudo teórico; desenvolvimento do aplicativo móvel; validação com treze juízes especialistas; adequação; validação com doze familiares de crianças e adolescentes em quimioterapia oral e adequação final do aplicativo móvel. As entrevistas foram analisadas pelo software Interface de R pour Analyses Multidimensionnelles de Textes et de Questionneires e validadas com a escala Likert, considerando-se validados itens com Índice de Concordância igual ou maior a 70%. Resultados: o aplicativo "Quimio em Casa" foi devolvido com duas interfaces, uma versão desktop voltada para os profissionais de saúde e o aplicativo para os familiares de crianças e adolescentes em tratamento com quimioterápicos orais. Os conteúdos que direcionaram a elaboração do aplicativo foram: armazenamento; manipulação; administração; os cuidados específicos de cada quimioterápico; efeitos adversos; quando ir à emergência e checagem diária das administrações. O aplicativo foi elaborado e validado com Índice de Concordância de adequação de 96,7% entre os juízes, e de 100% pelos familiares. Conclusão: o aplicativo mostrou-se válido como tecnologia educacional na aplicação prática junto aos familiares de crianças e adolescentes em quimioterapia oral.
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Tecnologia Educacional , Estudo de Validação , Aplicativos Móveis , Enfermagem Oncológica , Administração Oral , AntineoplásicosRESUMO
RESUMO Objetivo descrever as necessidades de aprendizagem de familiares de crianças e adolescentes com câncer quanto ao tratamento com quimioterápicos antineoplásicos orais. Método pesquisa qualitativa descritiva desenvolvida em um hospital federal do Rio de Janeiro, Brasil. Os dados foram coletados nos meses de julho a setembro de 2020 a partir de entrevistas semiestruturadas com vinte e três familiares de crianças e adolescentes com câncer em quimioterapia antineoplásica oral. Os dados foram processados no software Interface de R pour Analyses Multidimensionnelles de Textes et de Questionnaires pela Classificação Hierárquica Descendente. Resultados dentre os temas que demandam aprendizagem pelos familiares estão administração oral, armazenamento e manipulação dos quimioterápicos orais, além dos efeitos adversos e emergências que demandam atendimento hospitalar. Conclusão e implicações para a prática no tratamento com quimioterápicos orais, as necessidades de aprendizagem dos familiares de crianças e adolescentes precisam ser problematizadas em práticas educativas dialógicas para, assim, favorecer a segurança, a adesão e a eficácia do tratamento.
RESUMEN Objetivo describir las necesidades de aprendizaje de familiares de niños y adolescentes con cáncer en cuanto al tratamiento con quimioterápicos antineoplásicos orales. Método investigación cualitativa descriptiva desarrollada en un hospital federal de Río de Janeiro, Brasil. Los datos fueron recogidos en los meses de julio a septiembre de 2020 a partir de entrevistas semiestructuradas con veintitrés familiares de niños y adolescentes con cáncer en quimioterapia antineoplásica oral. Los datos fueron procesados en el software Interface de R pour Analyses Multidimensionnelles de Textes et de Questionnaires por la Clasificación Jerárquica Descendente. Resultados entre los temas que demandan aprendizaje por los familiares están administración oral, almacenamiento y manipulación de los quimioterápicos orales, además de los efectos adversos y emergencias que demandan atención hospitalaria. Conclusión e implicaciones para la práctica en el tratamiento con quimioterápicos orales, las necesidades de aprendizaje de los familiares de niños y adolescentes necesitan ser problematizadas en prácticas educativas dialógicas para, así, favorecer la seguridad, la adhesión y la eficacia del tratamiento.
ABSTRACT Objective to describe the learning needs of family members of children and adolescents with cancer regarding treatment with oral antineoplastic chemotherapies. Method a descriptive qualitative research developed in a federal hospital in Rio de Janeiro, Brazil. Data were collected in the months from July to September 2020 from semi-structured interviews with twenty-three family members of children and adolescents with cancer undergoing oral antineoplastic chemotherapy. Data was processed in the software Interface de R pour Analyses Multidimensionnelles de Textes et de Questionnaires by the Descending Hierarchical Classification. Results among the themes that demand learning by the family members are oral administration, storage and handling of oral antineoplastic drugs, as well as adverse effects and emergencies that require hospital care. Conclusion and implications for practice in oral antineoplastic treatment, the learning needs of family members of children and adolescents need to be problematized in dialogic educational practices in order to favor the safety, adherence, and efficacy of the treatment.
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Humanos , Masculino , Feminino , Criança , Adolescente , Adulto , Educação em Saúde , Cuidadores/educação , Neoplasias/terapia , Antineoplásicos/uso terapêutico , Cuidado da Criança , Metotrexato/uso terapêutico , Administração Oral , Pesquisa Qualitativa , Armazenamento de Medicamentos , Temozolomida/uso terapêutico , Mercaptopurina/uso terapêutico , Antineoplásicos/administração & dosagemRESUMO
Myocardial perfusion imaging (MPI) plays an important role in patients with suspected and documented coronary artery disease (CAD). Machine Learning (ML) algorithms have been developed for many medical applications with excellent performance. This study used ML algorithms to discern normal and abnormal gated Single Photon Emission Computed Tomography (SPECT) images. We analyzed one thousand and seven polar maps from a database of patients referred to a university hospital for clinically indicated MPI between January 2016 and December 2018. These studies were reported and evaluated by two different expert readers. The image features were extracted from a specific type of polar map segmentation based on horizontal and vertical slices. A senior expert reading was the comparator (gold standard). We used cross-validation to divide the dataset into training and testing subsets, using data augmentation in the training set, and evaluated 04 ML models. All models had accuracy >90% and area under the receiver operating characteristics curve (AUC) >0.80 except for Adaptive Boosting (AUC = 0.77), while all precision and sensitivity obtained were >96 and 92%, respectively. Random Forest had the best performance (AUC: 0.853; accuracy: 0,938; precision: 0.968; sensitivity: 0.963). ML algorithms performed very well in image classification. These models were capable of distinguishing polar maps remarkably into normal and abnormal.
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Myocardial perfusion imaging (MPI) is an essential tool used to diagnose and manage patients with suspected or known coronary artery disease. Additionally, the General Data Protection Regulation (GDPR) represents a milestone about individuals' data security concerns. On the other hand, Machine Learning (ML) has had several applications in the most diverse knowledge areas. It is conceived as a technology with huge potential to revolutionize health care. In this context, we developed ML models to evaluate their ability to distinguish an individual's sex from MPI assessment. We used 260 polar maps (140 men/120 women) to train ML algorithms from a database of patients referred to a university hospital for clinically indicated MPI from January 2016 to December 2018. We tested 07 different ML models, namely, Classification and Regression Tree (CART), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Adaptive Boosting (AB), Random Forests (RF) and, Gradient Boosting (GB). We used a cross-validation strategy. Our work demonstrated that ML algorithms could perform well in assessing the sex of patients undergoing myocardial scintigraphy exams. All the models had accuracy greater than 82%. However, only SVM achieved 90%. KNN, RF, AB, GB had, respectively, 88, 86, 85, 83%. Accuracy standard deviation was lower in KNN, AB, and RF (0.06). SVM and RF had had the best area under the receiver operating characteristic curve (0.93), followed by GB (0.92), KNN (0.91), AB, and NB (0.9). SVM and AB achieved the best precision. Our results bring some challenges regarding the autonomy of patients who wish to keep sex information confidential and certainly add greater complexity to the debate about what data should be considered sensitive to the light of the GDPR.
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BACKGROUND: The COVID-19 pandemic has led caregivers to modify patient healthcare, with a high impact on patients with chronic pain. AIMS: To map recommendations for the management of chronic pain patients during the COVID-19 pandemic and propose a workflow for the management of these patients. DESIGN: This was a scoping review. METHODS: The databases searched were PubMed, Embase, CINAHL, Scopus, Cochrane Library, and LILACS. The studies were examined by two independent reviewers. The disagreements between reviewers were resolved through discussion or with a third reviewer. RESULTS: We presented the results in the form of a table, a workflow, and a narrative summary. The search resulted in 13 studies selected for full reading, including one consensus, five guidance documents, two expert panels, one joint statement, two educational flyers, and two free comments. We considered new technologies, including telemedicine. Each pain service needs to establish a screening model, classifying patients according to corresponding severity. Particular attention should be given to patients who use opioids and are at risk of misusing them. Nonpharmacological approaches and pain education should be maintained, considering the use of telehealth. CONCLUSIONS: Recommendations for the management of chronic pain during COVID-19 include adjustments to the patient care model. The workflow proposes the use of telemedicine, screening for painful intensity, and the use of color-signaled intervention packages according to severity (green, yellow, and red).
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COVID-19/enfermagem , Dor Crônica/enfermagem , Cuidados de Enfermagem/normas , Manejo da Dor/normas , Guias de Prática Clínica como Assunto , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , SARS-CoV-2RESUMO
The recent advances at hardware level and the increasing requirement of personalization of care associated with the urgent needs of value creation for the patients has helped Artificial Intelligence (AI) to promote a significant paradigm shift in the most diverse areas of medical knowledge, particularly in Cardiology, for its ability to support decision-making and improve diagnostic and prognostic performance. In this context, the present work does a non-systematic review of the main papers published on AI in Cardiology, focusing on its main applications, potential impacts and challenges.
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Inteligência Artificial , Cardiologia , HumanosRESUMO
Resumo Os recentes avanços ao nível de hardware e a crescente exigência de personalização dos cuidados associados às necessidades urgentes de criação de valor para os pacientes contribuíram para que a Inteligência Artificial (IA) promovesse uma mudança significativa de paradigma nas mais diversas áreas do conhecimento médico, em particular em Cardiologia, por sua capacidade de apoiar a tomada de decisões e melhorar o desempenho diagnóstico e prognóstico. Nesse contexto, o presente trabalho faz uma revisão não-sistemática dos principais trabalhos publicados sobre IA em Cardiologia, com foco em suas principais aplicações, possíveis impactos e desafios.
Abstract The recent advances at hardware level and the increasing requirement of personalization of care associated with the urgent needs of value creation for the patients has helped Artificial Intelligence (AI) to promote a significant paradigm shift in the most diverse areas of medical knowledge, particularly in Cardiology, for its ability to support decision-making and improve diagnostic and prognostic performance. In this context, the present work does a non-systematic review of the main papers published on AI in Cardiology, focusing on its main applications, potential impacts and challenges.
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Humanos , Inteligência Artificial , CardiologiaRESUMO
Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis.
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Doença de Alzheimer/diagnóstico , Doença de Alzheimer/fisiopatologia , Diagnóstico por Computador/métodos , Transtornos Mentais/diagnóstico , Transtornos Mentais/fisiopatologia , Modelos Biológicos , Doença de Alzheimer/classificação , Teorema de Bayes , Humanos , Transtornos Mentais/classificaçãoRESUMO
Os recentes avanços da Informática na área de multimídia permitem visualizar um novo o profícuo campo de pesquisa: a integração destes recursos aos sistemas ICAI. Sabidamente o poder de transmissão do conhecimento por meio de imagens e sons é por vezes superior aos tradicionais métodos de leitura. Considera-se que a integração destes novos meios permitirá um salto qualitativo importante no desenvolvimento dos sistemas ICAI, especialmente em determinadas áreas do conhecimento em que imagem e som são de reconhecida importância. Pode-se citar como uma destas áreas o ensino do diagnóstico médico a partir de imagens (EEG, ECG, ultrasons, etc), considerada como a motivação principal deste trabalho. O projeto pretende fornecer um sistema capaz de integrar o usuário a um meio que permita, por exemplo, auxiliar no aprendizado da cardiologia, fazendo com que este aprendizado seja o mais próximo possível de um tutor real. Para isto utilizará técnicas de inteligência artificial, sistemas distribuídos e multimídia.