leili faraji gavgani

Acknowledging the considerable influence of undernutrition on health outcomes and HRQOL, this study sought to appraise the nutritional risk status of elderly patients with cardiovascular diseases (CVD) through the utilization of the Nutritional Risk Screening (NRS). Additionally, the investigation aimed to evaluate the correlation between NRS status and HRQOL within the context of patients referred to a cardiac hospital in Tabriz, Iran.

This cross-sectional study was conducted in Tabriz, Iran. The participants were selected randomly from patients referring to Shahid Madani Heart Hospital, a comprehensive university hospital during July to December 2018. A linear regression was used for control of confounding variables (age, gender, education level, marital status, and income levels) and predict the relationship between nutrition risk status and HQRL.

Of the 200 patients with CVD participated in this study, 68 (34%) of participants had normal nutrition status, 108 (54%) were at risk for undernutrition, and 24 (12%) had undernutrition. A total of 24 aging patients with undernutrition, 13 (54%) were divorced or widowed. 86% of patients with diabetes were at risk for undernutrition and 13.9% had undernutrition. There were statistically significant relationship between undernutrition and HRQOL dimensions, age, gender, and marital status.

The study revealed a correlation between elevated undernutrition scores in patients and factors such as older age, female gender, and marital status of being divorced or widowed. Furthermore, the results imply that a notable elevation in the risk score for undernutrition in patients is significantly linked to impaired HRQOL among elderly individuals with CVD.

Functional limitation is one of the most important health - related concerns of diabetic patients. This study aimed to identify the factors associated with functional limitation among diabetic patients using generalized additive model (GAM) as a flexible technique to reveal the non - linear and non - monotonic association between the response and a set of independent variables.
The source data belonged to two cross - sectional studies conducted in 2014. A total of 694 people with type 2 diabetes in the age range of 31 - 70 years were selected via convenience sampling from diabetes clinics in Ardabil and Tabriz. The data were collected by interviewers using structured questionnaires and checklists. The functional capacity was measured using the physical functioning subscale of the Medical Outcomes Study Short Form 36 - Item Health Survey (SF36). Participants with a total functional capacity of less than 90 were considered to have “moderate or high level of functional limitation.” To identify the factors associated with functional limitation and reveal the shape of associations, the GAM procedure with “logit” link function was applied to the dataset of 378 diabetic patients without any missing data by smoothening of the effect of underlying factors. The Akaike information criterion (AIC) as the relative quality of the model’s criterion was computed for GAM and compared with AIC of the simple logistic regression.
Sex (P = 0.029), age (P In our sample, GAM could identify some linear and nonlinear associations between underlying factors and functional limitation in diabetic patients. These complex associations could relatively increase the fit quality of the GAM when compared to logistic regression.

The area under the ROC curve (AUC) is a common criterion to assess the overall classification performance of the markers. In practice, due to the limited classification ability of a single marker, we are interested in combining markers linearly or nonlinearly to improve classification performance. Ramp AUC (RAUC) is a new statistical AUC-based method which can find such optimal combinations of markers. In this study, RAUC was used to find the optimal combinations of care indicators related to functional limitation as a complication of diabetes and accurately discriminate this outcome based on its underlying markers.
This cross-sectional study was conducted on 378 diabetic patients referred to diabetic centers in Ardebil and Tabriz during 2014 and 2015. To have an accurate classification of diabetic patients according to their functional limitation status, RAUC method with RBF kernel was employed to look for an optimal combination of care indicators. Classification performance of the model was evaluated by AUC and compared with logistic regression, support vector machine (SVM) and generalized additive model (GAM) via training and test validation method.
Out of 378 diabetics, 67.46% had functional limitation. RAUC had an AUC of 1 for the test dataset and outperformed logistic (AUC = 0.079), GAM (AUC = 0.082), SVM with linear kernel (AUC = 0.67) and was slightly better than SVM with RBF kernel (AUC = 0.98).
There was a strong nonlinearity in data and RAUC with RBF kernel which is a nonlinear combination of markers could detect this pattern.