A simple clinical model for planning transfusion quantities in heart surgery

<p>Abstract</p> <p>Background</p> <p>Patients undergoing heart surgery continue to be the largest demand on blood transfusions. The need for transfusion is based on the risk of complications due to poor cell oxygenation, however large transfusions are associated with increased morbidity and risk of mortality in heart surgery patients. The aim of this study was to identify preoperative and intraoperative risk factors for transfusion and create a reliable model for planning transfusion quantities in heart surgery procedures.</p> <p>Methods</p> <p>We performed an observational study on 3315 consecutive patients who underwent cardiac surgery between January 2000 and December 2007. To estimate the number of packs of red blood cells (PRBC) transfused during heart surgery, we developed a multivariate regression model with discrete coefficients by selecting dummy variables as regressors in a stepwise manner. Model performance was assessed statistically by splitting cases into training and testing sets of the same size, and clinically by investigating the clinical course details of about one quarter of the patients in whom the difference between model estimates and actual number of PRBC transfused was higher than the root mean squared error.</p> <p>Results</p> <p>Ten preoperative and intraoperative dichotomous variables were entered in the model. Approximating the regression coefficients to the nearest half unit, each dummy regressor equal to one gave a number of half PRBC. The model assigned 4 units for kidney failure requiring preoperative dialysis, 2.5 units for cardiogenic shock, 2 units for minimum hematocrit at cardiopulmonary bypass less than or equal to 20%, 1.5 units for emergency operation, 1 unit for preoperative hematocrit less than or equal to 40%, cardiopulmonary bypass time greater than 130 minutes and type of surgery different from isolated artery bypass grafting, and 0.5 units for urgent operation, age over 70 years and systemic arterial hypertension.</p> <p>Conclusions</p> <p>The regression model proved reliable for quantitative planning of number of PRBC in patients undergoing heart surgery. Besides enabling more rational resource allocation of costly blood-conservation strategies and blood bank resources, the results indicated a strong association between some essential postoperative variables and differences between the model estimate and the actual number of packs transfused.</p

Evaluation of metals that are potentially toxic to agricultural surface soils, using statistical analysis, in northwestern Saudi Arabia

© 2015, Springer-Verlag Berlin Heidelberg. Heavy metals in agricultural soils enter the food chain when taken up by plants. The main purpose of this work is to determine metal contamination in agricultural farms in northwestern Saudi Arabia. Fifty surface soil samples were collected from agricultural areas. The study focuses on the geochemical behavior of As, Cd, Co, Cr, Cu, Hg, Pb and Zn, and determines the enrichment factor and geoaccumulation index. Multivariate statistical analysis, including principle component analysis and cluster analysis, is also applied to the acquired data. The study shows considerable variation in the concentrations of the analyzed metals in the studied soil samples. This variation in concentration is attributed to the intensity of agricultural activities and, possibly, to nearby fossil fuel combustion activities, as well as to traffic flows from highways and local roads. Multivariate analysis suggests that As, Cd, Hg and Pb are associated with anthropogenic activities, whereas Co, Cr, Cu and Zn are mainly controlled by geogenic activities. Hg and Pb show the maximum concentration in the analyzed samples as compared to the background concentration

Coastal Scenery Assessment by Means of a Fuzzy Logic Approach

Landscape is a major element affecting people's life quality and coastal landscape evaluation is strongly rooted in the man-environment tradition. Coastal areas, all over the world, are under threat due to the conflicting requirements that rely on natural scenery of such as habitation, recreation, and industry. Since 'coastal scenery' is a natural resource, it has to be evaluated in an objective and quantitative way to provide a means of comparison against coastal activities and for environmental impact assessments. This chapter presents an evidence-based methodology called 'Coastal Scenic Evaluation System ( CSES)'. It is a technique that can be used not only for landscape preservation and protection, but also as scientific tool for envisaged coastal management and future development based upon plans formulated by an evidence-based approach. The results provide base-line information for a sound coastal management decision especially regarding intensive urban and industrial developments. CSES uses fuzzy logic to reduce subjectivity on decisions and obtain a quantitative evaluation of public survey research on 26 coastal scenic parameters having both physical and human perceptual characteristics. The weights of the scenic parameters were estimated by public survey questionnaires for Turkey, UK, Malta and Croatia and via consultations with coastal experts from the above mentioned four countries and Australia, Ireland, USA and Japan. Fuzzy logic mathematics was used to calculate a coastal scenic evaluation index ( D) from the checklist of 26 scenic parameters by using the attributed weights of the parameters which enabled to categorize scenic values of the coastal areas into five distinct classes