Identifying older diabetic patients at risk of poor glycemic control

BACKGROUND: Optimal glycemic control prevents the onset of diabetes complications. Identifying diabetic patients at risk of poor glycemic control could help promoting dedicated interventions. The purpose of this study was to identify predictors of poor short-term and long-term glycemic control in older diabetic in-patients. METHODS: A total of 1354 older diabetic in-patients consecutively enrolled in a multicenter study formed the training population (retrospective arm); 264 patients consecutively admitted to a ward of general medicine formed the testing population (prospective arm). Glycated hemoglobin (HbA1c) was measured on admission and one year after the discharge in the testing population. Independent correlates of a discharge glycemia ≥ 140 mg/dl in the training population were assessed by logistic regression analysis and a clinical prediction rule was developed. The ability of the prediction rule and that of admission HbA1c to predict discharge glycemia ≥ 140 mg/dl and HbA1c > 7% one year after discharge was assessed in the testing population. RESULTS: Selected admission variables (diastolic arterial pressure < 80 mmHg, glycemia = 143–218 mg/dl, glycemia > 218 mg/dl, history of insulinic or combined hypoglycemic therapy, Charlson's index > 2) were combined to obtain a score predicting a discharge fasting glycemia ≥ 140 mg/dl in the training population. A modified score was obtained by adding 1 if admission HbA1c exceeded 7.8%. The modified score was the best predictor of both discharge glycemia ≥ 140 mg/dl (sensitivity = 79%, specificity = 63%) and 1 year HbA1c > 7% (sensitivity = 72%, specificity = 71%) in the testing population. CONCLUSION: A simple clinical prediction rule might help identify older diabetic in-patients at risk of both short and long term poor glycemic control

Estimation of the Effective Permeability of Heterogeneous Porous Media by Using Percolation Concepts

In this paper we present new methods to estimate the effective permeability (k_eff) of heterogeneous porous media with a wide distribution of permeabilities and various underlying structures, using percolation concepts. We first set a threshold permeability (k_th) on the permeability density function (pdf) and use standard algorithms from percolation theory to check whether the high permeable grid blocks (i.e. those with permeability higher than k_th) with occupied fraction of “p” first forms a cluster connecting two opposite sides of the system in the direction of the flow (high permeability flow pathway). Then we estimate the effective permeability of the heterogeneous porous media in different ways: a power law (k_eff=k_th p^m), a weighted power average (k_eff=[p.k_th^m+(1-p).k_g^m ]^(1/m) with k_g the geometric average of the permeability distribution) and a characteristic shape factor multiplied by the permeability threshold value. We found that the characteristic parameters (i.e. the exponent “m”) can be inferred either from the statistics and properties of percolation sub-networks at the threshold point (i.e. high and low permeable regions corresponding to those permeabilities above and below the threshold permeability value) or by comparing the system properties with an uncorrelated random field having the same permeability distribution. These physically based approaches do not need fitting to the experimental data of effective permeability measurements to estimate the model parameter (i.e. exponent m) as is usually necessary in empirical methods. We examine the order of accuracy of these methods on different layers of 10th SPE model and found very good estimates as compared to the values determined from the commercial flow simulators

Quantifying the effects of soil and climate on aboveground biomass production of Salix miyabeana SX67 in Quebec

Soil and climatic conditions for optimizing aboveground biomass yields of bioenergy short rotation coppices (SRCs) of Salix are not well elucidated. The objective of this study was to identify and quantify the limitations induced by soil and climate, and compare the magnitude of their effects, on annual aboveground yields across ten SRCs of Salix miyabeana SX67 in Quebec, Canada. The effects of weather variation between years on yields were also tested within locations. In five plots per SRC, soil bulk density, particle size, exchangeable cations and bulk composition were analysed, and moisture deficits were estimated using leaf δ13C. For each location, numerous weather variables were simulated for spring, summer and the whole growing season. Climate was calculated by averaging weather variables for growing seasons for which annual yields were available. Annual aboveground biomass yields were modelled using linear regression, partitioning of the variance and mixed models with soil, weather and climate variables as predictors. Across SRCs, silt content, soil organic matter, pH, exchangeable Ca and Mg, and total N and Zn were significantly and positively related to aboveground yields (adj. R2 ranging from 0.38 to 0.79). Generally, annual yields were negatively related to summer temperature within SRCs (adj. R2 = 0.92) and drought across SRCs (adj. R2 = 0.54). Partitioning of the variance revealed that soil variables (~80%) had a greater effect on productivity than did climate variables (~10%). In fact, soil properties buffered or exacerbated water shortages and, thus, had a preponderant effect on yield