Effect of Fluid Intake on Hydration Status and Skin Barrier Characteristics in Geriatric Patients: An Explorative Study

Background/Aim: Inadequate fluid intake is assumed to be a trigger of water-loss dehydration, which is a major health risk in aged and geriatric populations. Thus, there is a need to search for easy to use diagnostic tests to identify dehydration. Our overall aim was to investigate whether skin barrier parameters could be used for predicting fluid intake and/or hydration status in geriatric patients. Methods: An explorative observational comparative study was conducted in a geriatric hospital including patients aged 65 years and older. We measured 3-day fluid intake, skin barrier parameters, Overall Dry Skin Score, serum osmolality, cognitive and functional health, and medications. Results: Forty patients were included (mean age 78.45 years and 65% women) with a mean fluid intake of 1,747 mL/day. 20% of the patients were dehydrated and 22.5% had an impending dehydration according to serum osmolality. Multivariate analysis suggested that skin surface pH and epidermal hydration at the face were associated with fluid intake. Serum osmolality was associated with epidermal hydration at the leg and skin surface pH at the face. Fluid intake was not correlated with serum osmolality. Diuretics were associated with high serum osmolality. Conclusions: Approximately half of the patients were diagnosed as being dehydrated according to osmolality, which is the current reference standard. However, there was no association with fluid intake, questioning the clinical relevance of this measure. Results indicate that single skin barrier parameters are poor markers for fluid intake or osmolality. Epidermal hydration might play a role but most probably in combination with other tests

Knowledge and Attitudes about Helsinki Declaration on Patient Safety among Anaesthesiologists in Turkey: A Questionnaire Study

Objective: The Helsinki Declaration on Patient Safety in Anaesthesiology is an important document for anaesthesiologists. This study aimed to evaluate the knowledge and experiences of anaesthesiologists in Mickey on the "Helsinki Declaration on Patient Satitty.

Frailty is characterized by biomarker patterns reflecting inflammation or muscle catabolism in multi-morbid patients

BACKGROUND: Frailty development is partly dependent on multiple factors like low levels of nutrients and high levels of oxidative stress (OS) and inflammation potentially leading to a muscle-catabolic state. Measures of specific biomarker patterns including nutrients, OS and inflammatory biomarkers as well as muscle related biomarkers like 3-methylhistidine (3MH) may improve evaluation of mechanisms and the complex networks leading to frailty. METHODS: In 220 multi-morbid patients (≥ 60 years), classified as non-frail (n = 104) and frail (n = 116) according to Fried's frailty criteria, we measured serum concentrations of fat-soluble micronutrients, amino acids (AA), OS, interleukins (IL) 6 and 10, 3MH (biomarker for muscle protein turnover) and serum spectra of fatty acids (FA). We evaluated biomarker patterns by principal component analysis (PCA) and their cross-sectional associations with frailty by multivariate logistic regression analysis. RESULTS: Two biomarker patterns [principal components (PC)] were identified by PCA. PC1 was characterized by high positive factor loadings (FL) of carotenoids, anti-inflammatory FA and vitamin D3 together with high negative FL of pro-inflammatory FA, IL6 and IL6/IL10, reflecting an inflammation-related pattern. PC2 was characterized by high positive FL of AA together with high negative FL of 3MH-based biomarkers, reflecting a muscle-related pattern. Frail patients had significantly lower factor scores than non-frail patients for both PC1 [median: −0.27 (interquartile range: 1.15) vs. 0.27 (1.23); P = 0.001] and PC2 [median: −0.15 (interquartile range: 1.13) vs. 0.21 (1.38); P = 0.002]. Patients with higher PC1 or PC2 factor scores were less likely to be frail [odds ratio (OR): 0.62, 95% CI: 0.46–0.83, P = 0.001 for PC1; OR: 0.64, 95% CI: 0.48–0.86, P = 0.003 for PC2] compared with patients with lower PC1 or PC2 factor scores. This indicates that increasing levels of anti-inflammatory biomarkers and increasing levels of muscle-anabolic biomarkers are associated with a reduced likelihood (38% and 36%, respectively) for frailty. Significant associations remained after adjusting the regression models for potential confounders. CONCLUSIONS: We conclude that two specific patterns reflecting either inflammation-related or muscle-related biomarkers are both significantly associated with frailty among multi-morbid patients and that these specific biomarker patterns are more informative than single biomarker analyses considering frailty identification