Performance of Brazilian long and short IQCODE on the screening of dementia in elderly people with low education

Background: Dementia screening in elderly people with low education can be difficult to implement. For these subjects, informant reports using the long (L) (26 items) and short (C) (16 items) versions of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) can be useful. The objective of the present study was to investigate the performance of Brazilian versions of the IQCODE L, S and a new short version (SBr) (15 items) in comparison with the Mini-mental State Examination (MMSE) for dementia screening in elderly people with low education. Methods: Thirty-four patients with mild to moderate dementia, diagnosed according to ICD-10 criteria, and 57 controls were evaluated and divided into three groups based on their socioeconomic status and level of education. Patients were evaluated using the MMSE and the informants were interviewed using the IQCODE by interviewers blind to the clinical diagnosis. Results: Education was correlated with MMSE results (r = 0.280, p = 0.031), but not with the versions of the IQCODE. The performance of the instruments, evaluated by the ROC curves, was very similar, with good internal consistency (Cronbach`s alpha = 0.97). MMSE correctly classified 85.7% of the subjects while the three IQCODE versions (L, S and SBr) correctly classified 91.2% of the subjects. Conclusions: The long, short and the new short Brazilian IQCODE versions can be useful as a screening tool for mild and moderate patients with dementia in Brazil. The IQCODE is not biased by schooling, and it seems to be an adequate instrument for samples with low levels of education.

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.