Capacidade preditiva de métodos indiretos para identificação precoce de desnutrição em crianças e adolescentes com neoplasias malignas

• Ano de defesa: 2019
• Autor(a) principal: Ferretti, Roberta De Lucena [UNIFESP]
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Paulo (UNIFESP)
• Palavras-chave em Português: Nutritional Assessment; Malnutrition; Child; Adolescent; Malignant Neoplasms; Avaliação Nutricional; Desnutrição; Criança; Adolescente; Neoplasias Malignas
• Link de acesso: https://sucupira.capes.gov.br/sucupira/public/consultas/coleta/trabalhoConclusao/viewTrabalhoConclusao.jsf?popup=true&id_trabalho=7937550; https://repositorio.unifesp.br/handle/11600/59831

Background: Malnutrition in pediatric oncology is considered common, and this diagnosis may be underestimated according to the evaluation method. Anthropometric indicators can be useful tools for the early diagnosis of malnutrition. Objectives: The main objective of this study was to verify if calf circumference (CC), neck circumference (NC) and adductor pollicis muscle thickness (APMT) are independent predictors of malnutrition in this population and propose cutoff points to identify this nutritional status. Methods: A cross-sectional study with 2988 cases of children and adolescents with malignant neoplasms evaluated from October 2015 to April 2017. The variables collected were sex, age, weight, length / height, CC, NC, APMT, and other anthropometric variables. The nutritional status was obtained by classifying the BMI/A z-score according to the recommendations of the World Health Organization, 2006/2007. Multivariate analysis of the anthropometric indicators of interest for predictive capacity evaluation and ROC curves proposals were performed. The level of significance was p <0.05. Results: 1794 cases (56.35% male) were selected for the final sample. The mean age was 8.48 ± 5.55 years. CC showed to be an excellent independent predictor of malnutrition for all age groups and genders (p <0.05). The analysis of each ROC curve for the CC verified high sensitivity and specificity and area under the curve overlap 0.7 (p <0.0001), being proposed CC cutoffs that identify malnutrition. NC and APMT showed no predictive capacity for malnutrition (p> 0.05). Conclusion: It is concluded that CC is the only analyzed anthropometric indicator considered an independent predictor of malnutrition, and this outcome was not observed for NC and APMT. It was possible to propose cutoff points for CC that identify malnutrition, for the male and female sexes, and for all age groups.