Machine learning models to predict outcomes at 30-days using Global Leadership Initiative on Malnutrition combinations with and without muscle mass in people with cancer.

ABSTRACT:

Background

Equipment to assess muscle mass is not available in all health services. Yet we have limited understanding of whether applying the Global Leadership Initiative on Malnutrition (GLIM) criteria without an assessment of muscle mass affects the ability to predict adverse outcomes. This study used machine learning to determine which combinations of GLIM phenotypic and etiologic criteria are most important for the prediction of 30-day mortality and unplanned admission using combinations including and excluding low muscle mass.

Methods

In a cohort of 2801 participants from two cancer malnutrition point prevalence studies, we applied the GLIM criteria with and without muscle mass. Phenotypic criteria were assessed using ≥5% unintentional weight loss, body mass index, subj

SUBMITTER: Kiss N 

PROVIDER: S-EPMC10401541 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature