Background: Factors associated with hospital mortality are usually identified and their effects are quantified through statistical modeling. To guide the choice of the best statistical model, we first quantify the pre...
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Background: Factors associated with hospital mortality are usually identified and their effects are quantified through statistical modeling. To guide the choice of the best statistical model, we first quantify the predictive ability of each model and then use the CIHI index to see if the hospital policy needs any change. Objectives: The main purpose of this study compared three statistical models in the evaluation of the association between hospital mortality and two risk factors, namely subject’s age at admission and the length of stay, adjusting for the effect of Diagnostic Related Groups (DRG). Methods: We use several SAS procedures to quantify the effect of DRG on the variability in hospital mortality. These procedures are the Logistic Regression model (ignoring the DRG effect), the Generalized Estimating Equation (GEE) that takes into account the within DRG clustering effect (but the within cluster correlation is treated as nuisance parameter), and the Generalized Linear Mixed Model (GLIMMIX). We showed that the GLIMMIX is superior to other models as it properly accounts for the clustering effect of “Diagnostic Related Groups” denoted by DRG. Results: The GLM procedure showed that the proportional contribution of DRG is 16%. All three models showed significant and increasing trend in mortality (P < 0.0001) with respect to the two risk factors (age at admission, and hospital length of stay). It was also clear that the CIHI index was not different under the three models. We re-estimated the models parameters after dichotomizing the risk factors at the optimal cut-off points, using the ROC curve. The parameters estimates and their significance did not change.
Obesity is due to the combined effects of genes, environment, lifestyle, and the interactions of these factors. Leptin receptor (LEPR) gene has been intensively evaluated in the search of variants that could be relate...
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Obesity is due to the combined effects of genes, environment, lifestyle, and the interactions of these factors. Leptin receptor (LEPR) gene has been intensively evaluated in the search of variants that could be related to obesity. The results of most of these studies have been controversial. We investigated the effects of leptin receptor gene 3’-untranslated region (3’-UTR) polymorphism on phenotype, metabolic parameters and anthropometric measurements of obese Saudi females. 122 healthy women aged 19 to 36 years. The subjects were divided into 3 groups according to their body mass index BMI;lean (BMI 18 - 24), overweight (BMI 25 - 29) and obese (BMI ≥ 30). There were 13 homozygotes and 34 heterozygotes for the 3’-UTR insertion allele amongst all 122 women. The results of this study show that the allele frequency of the insertion allele (I) of 3’UTR was significantly higher in overweight (35.3) and obese females (32.2) compared to the frequency in lean females (15.6). The insertion allele was associated with increased BMI in obese groups. The results obtained from this study indicated that in the obese subjects most variable values increased in I/I homozygote but the significant high value recorded among BMI (40.9 ± 7.11 kg/m2, P = 0.042). Our findings indicated that, the obesity in Saudi females is influenced by alteration in the leptin receptor gene 3’-UTR polymorphism.
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