Pilot study GLIM criteria for categorization of a malnutrition diagnosis of patients undergoing elective gastrointestinal operations: A pilot study of applicability and validation.

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.

Ultrasonography to assess body composition: Relevance of training.

OBJECTIVE: The aim of this study was to evaluate the relevance of ultrasonography training by non-experts carrying out quadriceps muscle mass assessment. METHODS: Two non-expert evaluators were trained by two radiologists on the basic principles of ultrasonography and quadriceps muscle measurements. Afterward, they performed assessments on 30 healthy volunteers to determine interobserver agreement, considering two landmarks (two-thirds of the femoral distance, and 10 cm above the patella), which were tested by the intraclass correlation coefficient (ICC). RESULTS: In all, 342 measurements were acquired. Better ICC data were seen for the muscle at two-thirds (ICC = 0.74-0.86) of the landmark than at the 10-cm landmark (ICC = 0.63-0.91). However, the thickness measurements indicated inadequate agreement (ICC = 0.71). The ICC values for both the rectus femoris area and thickness progressively increased when comparing the first 10 measurements with the last 10 for the two-thirds landmark. For the 10-cm landmark, worse data were observed in the last measurements, perhaps due to the increased number of obese volunteers. CONCLUSIONS: Measurements at the 10-cm landmark are more difficult to acquire, especially in the obese. Intensive training for non-expert examiners is mandatory.