Jessimara Ribeiro Henrique1, Ramon Gonçalves Pereira2, Rosaria Silva Ferreira3, Heather Keller4, Marian de Van der Schueren5, Maria Cristina Gonzalez6, Wagner Meira7, Maria Isabel Toulson Davisson Correia8. 1. Food Sciences Post Graduation Program, Pharmacy School, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil. 2. Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil. 3. Department of Statistics, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil. 4. Department of Nutrition & Aging, Schlegel-UW Research Institute for Aging, Ontario, Canada. 5. Department of Nutrition and Health, School of Allied Health, HAN University of Applied Sciences, Nijmegen, the Netherlands. 6. Postgraduate Program in Health and Behavior, Catholic University of Pelotas, Pelotas, Brazil. 7. Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, Brazil. 8. Department of Surgery, Medical School, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil. Electronic address: isabel_correia@uol.com.br.
Abstract
OBJECTIVES: The Global Leadership Initiative on Malnutrition (GLIM) was proposed to provide a common malnutrition diagnostic framework. The aims of this study were to evaluate the applicability and validity of the GLIM and use machine-learning techniques to help provide the best malnutrition-related variables/combinations to predict complications in patients undergoing gastrointestinal (GI) surgeries. METHOD: This was a prospective cohort study enrolling surgical patients with GI diseases. Malnutrition prevalence was classified by the GLIM, subjective global assessment (SGA), and various anthropometric parameters. The various combination of the phenotypic criteria generated 10 different models. Sensibility (SE) and specificity (SP) were calculated using SGA as the reference criterion. Machine-learning approaches were used to predict complications. P < 0.05 was set as statistically significant. RESULTS: We evaluated 206 patients. Half of the patients were malnourished according SGA, and 16.5% had postoperative complications. The prevalence of malnutrition using GLIM varied from 10.7% to 41.3% among the whole population, 11.7% and 43.6% in the elderly, from 0 to 24% in overweight non-obese and from 0 to 19.6% in obese patients. SE and SP values varied between 61.2% and 100% and 55.3% and 98.1%, respectively, for the general population. Machine-learning models indicated that midarm circumference, one of the GLIM models, and midarm muscle area were the most relevant criteria to predict complications. CONCLUSIONS: The various GLIM combinations provided different rates of malnutrition according to the population. Machine-learning techniques supported the use of common single variables and one GLIM model to predict postoperative complications.
OBJECTIVES: The Global Leadership Initiative on Malnutrition (GLIM) was proposed to provide a common malnutrition diagnostic framework. The aims of this study were to evaluate the applicability and validity of the GLIM and use machine-learning techniques to help provide the best malnutrition-related variables/combinations to predict complications in patients undergoing gastrointestinal (GI) surgeries. METHOD: This was a prospective cohort study enrolling surgical patients with GI diseases. Malnutrition prevalence was classified by the GLIM, subjective global assessment (SGA), and various anthropometric parameters. The various combination of the phenotypic criteria generated 10 different models. Sensibility (SE) and specificity (SP) were calculated using SGA as the reference criterion. Machine-learning approaches were used to predict complications. P < 0.05 was set as statistically significant. RESULTS: We evaluated 206 patients. Half of the patients were malnourished according SGA, and 16.5% had postoperative complications. The prevalence of malnutrition using GLIM varied from 10.7% to 41.3% among the whole population, 11.7% and 43.6% in the elderly, from 0 to 24% in overweight non-obese and from 0 to 19.6% in obesepatients. SE and SP values varied between 61.2% and 100% and 55.3% and 98.1%, respectively, for the general population. Machine-learning models indicated that midarm circumference, one of the GLIM models, and midarm muscle area were the most relevant criteria to predict complications. CONCLUSIONS: The various GLIM combinations provided different rates of malnutrition according to the population. Machine-learning techniques supported the use of common single variables and one GLIM model to predict postoperative complications.
Keywords:
Global Leadership Initiative on Malnutrition; Malnutrition; Nutritional assessment; Postoperative complications; Subjective Global Assessment