Predicting the energy consumption of heated plastic greenhouses in south-eastern Spain

[ENG] Measurements of heat consumption in a parral type greenhouse, equipped with an air-heating system, were carried out in south-eastern Spain (Almería) during the 1998/99 winter. From the daily values of heat consumption (Qd, MJ m-2 d-1) recorded in five identical greenhouses heated to different night temperature set-points (Tc), and data of minimum outside air temperature (Te,min), relationships between Qd and the temperature difference (ΔTmin = Tc – Te,min) were established. Linear regressions between Qd and ΔTmin gave satisfactory fits (R2 ranging from 0.75 to 0.83), considering that Te,min was the only input data for the model. When all data were pooled, the correlation was curvilinear, the best fit to a 2nd order polynomial being Qd = 0.049 ΔTmin 2 – 0.001 ΔTmin + 1.107 (R2 = 0.89). Validation of this model was performed using data obtained during other years, giving a fair agreement at the daily (R2 = 0.86), 10-day (R2 = 0.95) and yearly (R2 = 0.99) time scales. This simple model could be of interest to growers for decision-making related to the choice of set-point temperature and crop planning in heated greenhouses.[ESP] Se realizaron medidas de consumo de energía de la calefacción por aire caliente en invernaderos tipo parral durante la campaña 1998/99 en el sureste de España (Almería). Se determinaron relaciones adecuadas, para cinco invernaderos calentados a diferentes temperaturas nocturnas de consigna (Tc), entre los valores de consumos diarios de energía (Qd, MJ m-2 d-1) y la diferencia (ΔTmin) entre la temperatura de consigna de calefacción y la temperatura mínima exterior (Te,min). La regresión lineal entre Qd y ΔTmin fue satisfactoria (R2 varió entre 0,75 y 0,83), considerando que Te,min fue la única variable de entrada para el modelo. Cuando se analizaron todos los datos en conjunto, la correlación fue curvilínea, siendo el mejor ajuste para un polinomio de 2º orden, Qd = 0,049 ΔTmin 2 – 0,001 ΔTmin + 1,107 (R2 = 0,89). La validación de este modelo fue realizada utilizando datos de otros años, mostrando un ajuste adecuado para los periodos diarios (R2 = 0,86), 10-días (R2 = 0,95) y anuales (R2 = 0,99). Este sencillo modelo puede ser de interés para los agricultores a la hora de tomar decisiones sobre el mercado, escoger la temperatura de consigna y programar el periodo de calefacción del invernadero

Reduced costs with bisoprolol treatment for heart failure - An economic analysis of the second Cardiac Insufficiency Bisoprolol Study (CIBIS-II)

Background Beta-blockers, used as an adjunctive to diuretics, digoxin and angiotensin converting enzyme inhibitors, improve survival in chronic heart failure. We report a prospectively planned economic analysis of the cost of adjunctive beta-blocker therapy in the second Cardiac Insufficiency BIsoprolol Study (CIBIS II). Methods Resource utilization data (drug therapy, number of hospital admissions, length of hospital stay, ward type) were collected prospectively in all patients in CIBIS . These data were used to determine the additional direct costs incurred, and savings made, with bisoprolol therapy. As well as the cost of the drug, additional costs related to bisoprolol therapy were added to cover the supervision of treatment initiation and titration (four outpatient clinic/office visits). Per them (hospital bed day) costings were carried out for France, Germany and the U.K. Diagnosis related group costings were performed for France and the U.K. Our analyses took the perspective of a third party payer in France and Germany and the National Health Service in the U.K. Results Overall, fewer patients were hospitalized in the bisoprolol group, there were fewer hospital admissions perpatient hospitalized, fewer hospital admissions overall, fewer days spent in hospital and fewer days spent in the most expensive type of ward. As a consequence the cost of care in the bisoprolol group was 5-10% less in all three countries, in the per them analysis, even taking into account the cost of bisoprolol and the extra initiation/up-titration visits. The cost per patient treated in the placebo and bisoprolol groups was FF35 009 vs FF31 762 in France, DM11 563 vs DM10 784 in Germany and pound 4987 vs pound 4722 in the U.K. The diagnosis related group analysis gave similar results. Interpretation Not only did bisoprolol increase survival and reduce hospital admissions in CIBIS II, it also cut the cost of care in so doing. This `win-win' situation of positive health benefits associated with cost savings is Favourable from the point of view of both the patient and health care systems. These findings add further support for the use of beta-blockers in chronic heart failure

Simple scoring system to predict in-hospital mortality after surgery for infective endocarditis

BACKGROUND: Aspecific scoring systems are used to predict the risk of death postsurgery in patients with infective endocarditis (IE). The purpose of the present study was both to analyze the risk factors for in-hospital death, which complicates surgery for IE, and to create a mortality risk score based on the results of this analysis. METHODS AND RESULTS: Outcomes of 361 consecutive patients (mean age, 59.1\ub115.4 years) who had undergone surgery for IE in 8 European centers of cardiac surgery were recorded prospectively, and a risk factor analysis (multivariable logistic regression) for in-hospital death was performed. The discriminatory power of a new predictive scoring system was assessed with the receiver operating characteristic curve analysis. Score validation procedures were carried out. Fifty-six (15.5%) patients died postsurgery. BMI >27 kg/m2 (odds ratio [OR], 1.79; P=0.049), estimated glomerular filtration rate 55 mm Hg (OR, 1.78; P=0.032), and critical state (OR, 2.37; P=0.017) were independent predictors of in-hospital death. A scoring system was devised to predict in-hospital death postsurgery for IE (area under the receiver operating characteristic curve, 0.780; 95% CI, 0.734-0.822). The score performed better than 5 of 6 scoring systems for in-hospital death after cardiac surgery that were considered. CONCLUSIONS: A simple scoring system based on risk factors for in-hospital death was specifically created to predict mortality risk postsurgery in patients with IE