2.7. Statistical Analyses
stroke, tumor, COPD, diabetes
TUMOR, STROKE, COPD, REGRESSION, HYPERTENSION, DIABETES
For the assessment of the predictive factors of SMI, we used a linear regression model. For the multivariate regression, we adjusted for the confounders C-reactive protein (CRP) and serum albumin, as well as for handgrip strength. Pearson correlation was used to compare thoracic and lumbar SMI. To examine associations of SMI at T12 with clinical outcome, we used a logistic regression model for binary outcomes and linear regression models for continuous variables. The multivariate regression calculations were adjusted for age, BMI, nutritional support intervention, contributing center, and presence of major comorbidities, i.e., stroke, COPD, hypertension, diabetes and chronic heart. Furthermore, an analysis with subgroups to investigate differences in specific patient groups (age ≥ 80 years, NRS score ≥ 4 points, male gender and the presence of a tumor or frailty) was conducted. We used logistic regression to investigate associations with stratification by SMI. We used Stata 15.1 Software (Stata Corp, College Station, TX, USA). Statistical significance for two-sided tests was set for 