Statistical analysis
inflammation, infection, cancer, renal failure, malnutrition, diabetes
INFLAMMATION, CHRONIC OBSTRUCTIVE PULMONARY DISEASE, INFECTION, METABOLIC DISEASES, MALNUTRITION, REGRESSION, DIABETES
Continuous variables were expressed as mean and standard deviation (SD), and binary and categorial variables as counts and percentages. Pearson’s χSecond, we investigated the prognostic value of cortisol and outcomes. We used Cox regression models to analyse mortality and reported hazard ratios (HR) with 95% confidence intervals (95% CI). Kaplan Meier Curves were implemented for graphical display. Linear and logistic regression were calculated for all other prognostic endpoints; reporting odds ratio (OR) or coefficient (Coeff) with 95% CI. We adjusted for predefined covariates including sex, age, baseline nutritional risk (based on NRS 2002), main diagnosis (i.e., infection, metabolic diseases), comorbidities (i.e., cancer, renal failure, diabetes, chronic obstructive pulmonary disease) (Model 1), as well as C reactive protein (CRP) (Model 2).Third, to explore the predictive value of cortisol regarding response to nutritional support, we calculated the association between individual nutritional support and clinical outcomes and stratified the cohort according to cortisol levels as mentioned above. To additionally explore for effects regarding inflammation and cortisol in a subgroup analysis, we divided patients into two groups according to their admission CRP level, degree of malnutrition risk, and age. Based on results from a former analysis [