A Novel Method of Evaluating Ureteropelvic Junction Obstruction: Dynamic Near Infrared Fluorescence Imaging Compared to Standard Modalities to Assess Urinary Obstruction in a Swine Model

Purpose: Dynamic near infrared fluorescence imaging of the urinary tract provides a promising way to diagnose ureteropelvic junction obstruction. Initial studies demonstrated the ability to visualize urine flow and peristalsis in great detail. We analyzed the efficacy of near infrared imaging in evaluating ureteropelvic junction obstruction, renal involvement and the anatomical detail provided compared to conventional imaging modalities. Materials and Methods: Ten swine underwent partial or complete unilateral ureteral obstruction. Groups were survived for the short or the long term. Imaging was performed with mercaptoacetyltriglycine diuretic renogram, magnetic resonance urogram, excretory urogram, ultrasound and near infrared imaging. Scoring systems for ureteropelvic junction obstruction were developed for magnetic resonance urogram and near infrared imaging. Physicians and medical students graded ureteropelvic junction obstruction based on magnetic resonance urogram and near infrared imaging results. Results: Markers of vascular and urinary dynamics were quantitatively consistent among control renal units. The same markers were abnormal in obstructed renal units with significantly different times of renal phase peak, start of pelvic phase and start of renal uptake. Such parameters were consistent with those obtained with mercaptoacetyltriglycine diuretic renography. Near infrared imaging provided live imaging of urinary flow, which was helpful in identifying the area of obstruction for surgical planning. Physicians and medical students categorized the degree of obstruction appropriately for fluorescence imaging and magnetic resonance urogram. Conclusions: Near infrared imaging offers a feasible way to obtain live, dynamic images of urine flow and ureteral peristalsis. Qualitative and quantitative parameters were comparable to those of conventional imaging. Findings support fluorescence imaging as an accurate, easy to use method of diagnosing ureteropelvic junction obstruction.

The role of pneumonia scores in the emergency room in patients infected by 2009 H1N1 infection

Despite the severity of pneumonia in patients with pandemic influenza A infection (H1N1), no validated risk scores associated with H1N1 pneumonia were tested. In this prospective observational study, we analyzed data of consecutive patients in our emergency room, hospitalized because of pneumonia between July and August 2009 in a public hospital in Brazil. The following pneumonia scoring systems were applied: the SMART-COP rule; the Pneumonia Severity Index; and the CURB-65 rule. Of 105 patients with pneumonia, 53 had H1N1 infection. Among them, only 9.5% that had a low risk according to SMART-COP were admitted to ICU, compared with 36.8% of those with the Pneumonia Severity Index score of 1-2 and 49% of those with CURB-65 score of 0-1. The SMART-COP had an accuracy of 83% to predict ICU admission. The SMART-COP rule presented the best performance to indicate ICU admission in patients with H1N1 pneumonia. European Journal of Emergency Medicine 19: 200-202 (C) 2012 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkins.

On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data

Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model (Hosmer & Lemeshow, 1989) and a logistic regression with state-dependent sample selection model (Cramer, 2004) applied to simulated data. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian bank portfolio. Our simulation results so far revealed that there is no statistically significant difference in terms of predictive capacity between the naive logistic regression models and the logistic regression with state-dependent sample selection models. However, there is strong difference between the distributions of the estimated default probabilities from these two statistical modeling techniques, with the naive logistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. (C) 2012 Elsevier Ltd. All rights reserved.