Perssonsonne2261

The aim of the study is to show the differences between the measured and estimated values of resting energy expenditure and any changes occurring after the 6-month weight loss intervention program.

We included 33 healthy adults aged 25-49 years with an average body mass index 29.1±2.7 kg/m 2 for female and 29.8±2.8 kg/m

for male. The measured resting energy expenditure was obtained by indirect calorimeter MedGem® Microlife and estimated resting energy expenditure by the Harris-Benedict equation, the Mifflin-St Jeor equation, the Owen equation, the Wright equation, and by the Tanita body composition analyser. All measurements and calculations were carried out before and after the 6-month intervention. Results were compared using paired t-tests. P value less than 0.05 was considered statistically significant.

A comparison of the measured resting energy expenditure of female subjects with the estimated resting energy expenditure using the Harris-Benedict equation, the Mifflin-St Jeor equation and the Wright equation showed a statistically significant difference. A comparison of the measured resting energy expenditure of male subjects with the estimated resting energy expenditure using the Harris-Benedict equation and the Wright equation showed a statistically significant difference. There was a significant difference in the measured resting energy expenditure and estimated resting energy expenditure using Tanita.

We concluded that the most comparable equation for our sample was the Owen's equation. After losing weight, the measured resting energy expenditure has decreased, which must be taken into account in further diet therapy.

We concluded that the most comparable equation for our sample was the Owen's equation. After losing weight, the measured resting energy expenditure has decreased, which must be taken into account in further diet therapy.

Family history (FH) is an important part of the patients' medical history during preventive management at model family medicine practices (MFMP). It currently includes a one (or two) generational inquiry, predominately in terms of cardiovascular diseases, arterial hypertension, and diabetes, but not of other diseases with a probable genetic aetiology. Beside family history, no application-based algorithm is available to determine the risk level for specific chronic diseases in Slovenia.

A web application-based algorithm aimed at determining the risk level for selected monogenic and polygenic diseases will be developed. The data will be collected in MFMP; approximately 40 overall with a sample including healthy preventive examination attendees (approximately 1,000). Demographic data, a three-generational FH, a medical history of acquired and congenital risk factors for the selected diseases, and other important clinical factors will be documented.

The results will be validated by a clinical genetic approach based on family pedigrees and the next-generation genetic sequencing method. After the risk of genetic diseases in the Slovenian population has been determined, clinical pathways for acting according to the assessed risk level will be prepared.

By means of a public health tool providing an assessment of family predisposition, a contribution to the effective identification of people at increased risk of the selected monogenic and polygenic diseases is expected, lessening a significant public health burden.

By means of a public health tool providing an assessment of family predisposition, a contribution to the effective identification of people at increased risk of the selected monogenic and polygenic diseases is expected, lessening a significant public health burden.

AR-DRG system for classification hospital episodes was implemented in Serbia to improve efficiency and transparency in the health system.

L3H3, IQR, and 10th-95th percentile methods were used to identify outlier episodes in the classification. Classification efficiency and within-group homogeneity were measured by an adjusted reduction in variance (R2) and a coefficient of variation (CV).

There were 246,131 hospital episodes with a total 1,651,913 bed days from 14 hospitals. All episodes were classified into 652 groups of which 441 had CV lower than 100%. "Medical groups" accounted for 51% of groups and for 72% of episodes. Chemotherapy and vaginal delivery were the highest volume groups, with 5% and 4% of total episodes. Major diagnostic category 6 (MDC 6, Diseases of the digestive system) was the highest volume MDC, accounting for 11% of episodes. "Day-cases" and "prolonged hospitalisation" accounted for 21% and 3% of episodes, respectively. The average length of stay varied from 5.6 to 8.2 days. Adjusted R2 was 0.3 for untrimmed data. Trimming by L3H3, IQR, and 10th-95th percentile method improved the value of adjusted R2 to 0.61, 0.49, and 0.51, identifying 24%, 7%, and 7% of total cases as outliers, respectively. Mental diseases (MDC 19) remained the lowest adjusted R2 in untrimmed and trimmed datasets.

A long length of stay and a small percentage of "day-cases" characterized hospital activity in Vojvodina. Trimming methods significantly improved DRG efficiency. Future studies should consider cost data.

A long length of stay and a small percentage of "day-cases" characterized hospital activity in Vojvodina. Trimming methods significantly improved DRG efficiency. Future studies should consider cost data.

The two primary objectives of this paper were (a) to develop first logically consistent TTO based EQ-5D-3L value sets for Slovenia and (b) to revisit earlier developed VAS based EQ-5D-3L value sets.

Between September 2005 and April 2006, face-to-face interviews with 225 individuals in Slovenia were conducted. Protocols from the Measurement and Value of Health study were followed closely. Each respondent valued 15 health states out of a total of 23. Model selection was informed by the criteria monotonicity/logical consistency. https://www.selleckchem.com/TGF-beta.html Predictive accuracy was assessed in terms of mean square difference between out-of-sample predictions and corresponding observed means, as well as Lin's Concordance Correlation Coefficient.

Modelling was based on 2,717 VAS and 2,831 TTO values elicited from 225 respondents. A 6-parameter constrained regression model with a supplementary power term was selected for VAS and TTO value sets, as it produces monotonic values, and proved superior in terms of out-of-sample predictive accuracy over the tested alternatives.