Coleção de Artigos Acadêmicos
URI permanente para esta coleçãohttps://repositorio.insper.edu.br/handle/11224/3227
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Artigo Científico Proposição e validação de ferramenta de avaliação da desnutrição hospitalar, com base no Global Leadership Initiative on Malnutrition: protocolo do Estudo GLIM-BR(2022) Lopes, Giovanna Guimarães; Piovacari, Silvia Maria Fraga; Pereira, Adriano José; ANDRE FILIPE DE MORAES BATISTAIntrodução: O consenso do Global Leadership Initiative on Malnutrition (GLIM) publicou uma proposta de diretrizes para estruturação de uma ferramenta, composta por critérios diagnósticos fenotípicos e etiológicos. Posteriormente, foi publicado o Guia de Validação do GLIM, que visa estimular e direcionar iniciativas de validação desta nova ferramenta. O objetivo deste artigo é compartilhar o protocolo do Estudo GLIM-BR, em andamento, o qual irá propor e validar um novo instrumento nacional de classificação diagnóstica de desnutrição hospitalar baseado no GLIM, utilizando desfechos clínicos. Método: Estudo de validação prospectivo observacional multicêntrico, dividido em 3 fases, realizado em 4 hospitais brasileiros terciários, com pacientes internados e com expectativa de permanência maior que 48h. Modelos preditivos baseados em aprendizado de máquina/inteligência artificial serão utilizados na definição do conjunto ótimo de variáveis e cutoffs capazes de predizer desfechos clínicos, como óbito e tempo de permanência. Resultados: Na fase 1, das 12 variáveis apresentadas e discutidas no painel de opinião de especialistas, 8 tiveram aprovação sem alteração. As demais variáveis foram ajustadas por meio do consenso Grupo GLIM-BR. Todas as variáveis foram escolhidas com base na literatura atual (racional teórico) e utilizando, sempre que possível, outras ferramentas já validadas. A Fase 2 já possui resultados preliminares (subestudo) apresentados em Congresso internacional (ESPEN) e que serão submetidos para publicação em periódico científico internacional nos próximos meses. A Fase 3 está em curso e as variáveis de interesse selecionadas para serem avaliadas pelo modelo preditivo do estudo GLIM-BR, em cada uma das categorias propostas pelo GLIM, estão divulgadas neste artigo, juntamente com detalhes do protocolo de pesquisa em curso. Conclusão: Almeja-se desenvolver uma ferramenta validada para diagnóstico da desnutrição hospitalar, que contorne limitações identificadas em ferramentas de avaliação nutricional vigentes, prática e pronta para uso pela comunidade de nutricionistas nos serviços hospitalares.Artigo Científico Early identification of older individuals at risk of mobility decline with machine learning(2022) Nascimento, Carla Ferreira do; ANDRE FILIPE DE MORAES BATISTA; Duarte, Yeda Aparecida Oliveira; Chiavegatto Filho, Alexandre Dias PortoBackground: : The early identification of individuals at risk of mobility decline can improve targeted strategies of prevention. Aims: : To evaluate the predictive performance of machine learning (ML) algorithms in identifying older in dividuals at risk of future mobility decline. Methods: : We used data from the SABE Study (Health, Well-being and Aging Study), a representative sample of people aged 60 years and more, living in the Municipality of São Paulo, Brazil. Mobility decline was assessed 5 years after admission in the study by self-reported difficulty to walk a block, climb steps, being able to stoop, crouch and kneel, or lifting or carrying weights greater than 5 kg. Popular machine learning algorithms were trained in 70% of the sample with 10-fold cross-validation, and predictive performance metrics were obtained from applying the trained algorithms to the other 30% (test set). Results: : Of the 1,615 individuals, 48% developed difficulty in at least one of the four tasks, 32% in stooping, crouching and kneeling, and 30% in carrying weights. The random forest algorithm had the best predictive performance for most outcomes. The tasks that the algorithm was able to predict with better performance were crouching and kneeling (AUC-ROC: 0.81[0.76–0.85]), and lifting or carrying weights (AUC-ROC: 0.80 [0.75–0.84]). Age was the most important variable for the algorithms, followed by education and back pain, according to the SHAP (SHapley Additive exPlanations) values. Conclusion: : Applications of ML algorithms are a promising tool to identify older patients at risk of mobility decline, with the potential of improving targeted preventive programs.Artigo Científico Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study(2022) Wichmann, Roberta Moreira; Fagundes, Thales Pardini; Oliveira, Tiago Almeida de; ANDRE FILIPE DE MORAES BATISTA; Chiavegatto Filho, Alexandre Dias PortoArtificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform doctors about their results. The aim of this study was to investigate how healthcare professionals prefer to receive predictions generated by machine learning algorithms. A systematic search in MEDLINE, via PubMed, EMBASE and Web of Science was first performed. We developed a mobile app, RandomIA, to predict the occurrence of clinical outcomes, initially for COVID-19 and later expected to be expanded to other diseases. A questionnaire called System Usability Scale (SUS) was selected to assess the usability of the mobile app. A total of 69 doctors from the five regions of Brazil tested RandomIA and evaluated three different ways to visualize the predictions. For prognostic outcomes (mechanical ventilation, admission to an intensive care unit, and death), most doctors (62.9%) preferred a more complex visualization, represented by a bar graph with three categories (low, medium, and high probability) and a probability density graph for each outcome. For the diagnostic prediction of COVID-19, there was also a majority preference (65.4%) for the same option. Our results indicate that doctors could be more inclined to prefer receiving detailed results from predictive machine learning algorithms.Artigo Científico How Reliable Are The Screening Tools As A Triage Element For The Application Of The Global Leadership Initiative On Malnutrition (Glim)? Prospective Multicenter Observational Study(2023) Lopes, G.G.; Piovacari, S. M. F.; Moraes, J. R.; Santos, H. B. C.; Rakovicius, A. K. Z.; ANDRE FILIPE DE MORAES BATISTA; Pereira, A.J.Rationale: The international GLIM guideline recommends on the use of nutritional screening for the diagnosis of hospital malnutrition. However, as clinical outcomes were not included in the original validation of these instruments and their sensitivity (true positive rate) is unknown, it is not possible to report a chance of a truly at nutritional risk patient not being identified by screening and consequently not being evaluated by the GLIM. Methods: Multicenter, prospective observational trial in 3 Brazilian tertiary hospitals, carried out between December/21 to February/22. A convenience sample was used based on patients with expected length of stay at hospital longer than 48 hours. Pregnant women, under 18 years old, palliative care, lymphedema and muscle atrophy of neurological causes patients were excluded. NRS 2002, MNA-SF and ESPEN 2019 tools were applied to the specific populations (following international guidelines) by trained and validated Dietitians. Hospital mortality data were extracted from the local electronic medical records. Results: 676 patients were included, 54% male, 90% white with a mean age of 63 years (SD: ±21) and BMI of 27.50 kg/m2 (SD: ±4.72), hospitalized in the wards (68%). The most used nutritional screening was NRS 2002 (52%). In the sample, 39% were at nutritional risk, of these 5.6% died. An overview of nutritional screening tools’ performance are shown in the Table. In addition, accuracy found was 0.59 and area under the curve was 0,69 for predicting in-hospital deaths.Artigo Científico Data-driven decision making for the screening of cognitive impairment in primary care: a machine learning approach using data from the ELSA-Brasil study(2023) Szlejf, C.; ANDRE FILIPE DE MORAES BATISTA; Bertola, L.; Lotufo, P.A.; Benseñor, I.M.; Chiavegatto Filho, A.D.P.; Suemoto, C.K.The systematic assessment of cognitive performance of older people without cognitive complaints is controversial and unfeasible. Identifying individuals at higher risk of cognitive impairment could optimize resource allocation. We aimed to develop and test machine learning models to predict cognitive impairment using variables obtainable in primary care settings. In this cross-sectional study, we included 8,291 participants of the baseline assessment of the ELSA-Brasil study, who were aged between 50 and 74 years and were free of dementia. Cognitive performance was assessed with a neuropsychological battery and cognitive impairment was defined as global cognitive z-score below 2 standard deviations. Variables used as input to the prediction models included demographics, social determinants, clinical conditions, family history, lifestyle, and laboratory tests. We developed machine learning models using logistic regression, neural networks, and gradient boosted trees. Participants’ mean age was 58.3±6.2 years, 55% were female. Cognitive impairment was present in 328 individuals (4%). Machine learning algorithms presented fair to good discrimination (areas under the ROC curve between 0.801 and 0.873). Extreme Gradient Boosting presented the highest discrimination, high specificity (97%), and negative predictive value (97%). Seventy-six percent of the individuals with cognitive impairment were included among the highest ranked individuals by this algorithm. In conclusion, we developed and tested a machine learning model to predict cognitive impairment based on primary care data that presented good discrimination and high specificity. These characteristics could support the detection of patients who would not benefit from cognitive assessment, facilitating the allocation of human and economic resources.