A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.

Rationale and design of a randomized placebo-controlled trial assessing the effects of etiologic treatment in Chagas` cardiomyopathy: The BENznidazole Evaluation For Interrupting Trypanosomiasis (BENEFIT)

Background Benznidazole is effective for treating acute and chronic (recently acquired) Tryponosoma cruzi infection (Chagas` disease). Recent data indicate that parasite persistence plays a pivotal role in the pathogenesis of chronic Chagas` cardiomyopathy. However, the efficacy of trypanocidal therapy in preventing clinical complications in patients with preexisting cardiac disease is unknown. Study Design BENEFIT is a multicenter, randomized, double-blind, placebo-controlled clinical trial of 3,000 patients with Chagas` cardiomyopathy in Latin America. Patients are randomized to receive benznidazole (5 mg/kg per day) or matched placebo, for 60 days. The primary outcome is the composite of death; resuscitated cardiac arrest; sustained ventricular tachycardia; insertion of pacemaker or cardiac defibrillator; cardiac transplantation; and development of new heart failure, stroke, or systemic or pulmonary thromboembolic events. The average follow-up time will be 5 years, and the trial has a 90% power to detect a 25% relative risk reduction. The BENEFIT program also comprises a substudy evaluating the effects of benznidazole on parasite clearance and an echo substudy exploring the impact of etiologic treatment on left ventricular function. Recruitment started in November 2004, and >1,000 patients have been enrolled in 35 centers from Argentina, Brazil, and Colombia to date. Conclusion This is the largest trial yet conducted in Chagas` disease. BENEFIT will clarify the role of trypanocidal therapy in preventing cardiac disease progression and death.

Plant-antivenom: Database of anti-venom medicinal plants

Plant-antivenom is a computational Websystem about medicinal plants with anti-venom properties. The system consists of a database of these plants, including scientific publications on this subject and amino acid sequences of active principles from venomous animals. The system relates these data allowing their integration through different search applications. For the development of the system, the first surveys were conducted in scientific literature, allowing the creation of a publication database in a library for reading and user interaction. Then, classes of categories were created, allowing the use of tags and the organization of content. This database on medicinal plants has information such as family, species, isolated compounds, activity, inhibited animal venoms, among others. Provision is made for submission of new information by registered users, by the use of wiki tools. Content submitted is released in accordance to permission rules defined by the system. The database on biological venom protein amino acid sequences was structured from the essential information from National Center for Biotechnology Information (NCBI). Plant-antivenom`s interface is simple, contributing to a fast and functional access to the system and the integration of different data registered on it. Plant-antivenom system is available on the Internet at http://gbi.fmrp.usp.br/plantantivenom.