Socioeconomic inequalities and temporomandibular joint disorders in adolescents: contributions from a Maranhão cohort.

AIM: To assess the prevalence of temporomandibular disorder (TMD) in adolescents and estimate possible associations with poverty. BASIC RESEARCH DESIGN: A cross-sectional study nested within a prospective birth cohort study conducted in São Luís, Maranhão, Brazil. PARTICIPANTS: 2,412 adolescents aged 18-19 years. MATERIAL AND METHODS: The presence of TMD, classified according to the Fonseca Anamnestic Index, was used as the outcome. The following explanatory variables were assessed: gender, household head, paved/asphalted street, piped water, and socioeconomic background, based on the Brazilian Association of Market Research criteria and the poverty income ratio (PIR). Logistic regression analysis was performed with the estimation of odds ratios (OR) and 95% confidence intervals. RESULTS: TMD was common (51.4%) and was associated with poverty, as it was more frequent among adolescents from social classes D-E (OR=2.60; 95% CI: 1.48-4.55) and C (OR=1.82; 95% CI: 1.12-2.99) compared to A/B, and among poor adolescents using the PIR (OR=1.50; 95% CI: 1.02-2.33). CONCLUSIONS: The prevalence of TMD in socioeconomically disadvantaged adolescents in São Luís is high, and these data allow the early identification of at-risk groups. We recommend carrying out other population-based studies, using diagnostic strategies with greater accuracy.

Application of the intelligent techniques in transplantation databases: a review of articles published in 2009 and 2010.

The replacement of defective organs with healthy ones is an old problem, but only a few years ago was this issue put into practice. Improvements in the whole transplantation process have been increasingly important in clinical practice. In this context are clinical decision support systems (CDSSs), which have reflected a significant amount of work to use mathematical and intelligent techniques. The aim of this article was to present consideration of intelligent techniques used in recent years (2009 and 2010) to analyze organ transplant databases. To this end, we performed a search of the PubMed and Institute for Scientific Information (ISI) Web of Knowledge databases to find articles published in 2009 and 2010 about intelligent techniques applied to transplantation databases. Among 69 retrieved articles, we chose according to inclusion and exclusion criteria. The main techniques were: Artificial Neural Networks (ANN), Logistic Regression (LR), Decision Trees (DT), Markov Models (MM), and Bayesian Networks (BN). Most articles used ANN. Some publications described comparisons between techniques or the use of various techniques together. The use of intelligent techniques to extract knowledge from databases of healthcare is increasingly common. Although authors preferred to use ANN, statistical techniques were equally effective for this enterprise.

Artificial intelligence techniques: predicting necessity for biopsy in renal transplant recipients suspected of acute cellular rejection or nephrotoxicity.

The gold standard for nephrotoxicity and acute cellular rejection (ACR) is a biopsy, an invasive and expensive procedure. More efficient strategies to screen patients for biopsy are important from the clinical and financial points of view. The aim of this study was to evaluate various artificial intelligence techniques to screen for the need for a biopsy among patients suspected of nephrotoxicity or ACR during the first year after renal transplantation. We used classifiers like artificial neural networks (ANN), support vector machines (SVM), and Bayesian inference (BI) to indicate if the clinical course of the event suggestive of the need for a biopsy. Each classifier was evaluated by values of sensitivity and area under the ROC curve (AUC) for each of the classifiers. The technique that showed the best sensitivity value as an indicator for biopsy was SVM with an AUC of 0.79 and an accuracy rate of 79.86%. The results were better than those described in previous works. The accuracy for an indication of biopsy screening was efficient enough to become useful in clinical practice.

Classification of postural profiles among mouth-breathing children by learning vector quantization.

BACKGROUND: Mouth breathing is a chronic syndrome that may bring about postural changes. Finding characteristic patterns of changes occurring in the complex musculoskeletal system of mouth-breathing children has been a challenge. Learning vector quantization (LVQ) is an artificial neural network model that can be applied for this purpose. OBJECTIVES: The aim of the present study was to apply LVQ to determine the characteristic postural profiles shown by mouth-breathing children, in order to further understand abnormal posture among mouth breathers. METHODS: Postural training data on 52 children (30 mouth breathers and 22 nose breathers) and postural validation data on 32 children (22 mouth breathers and 10 nose breathers) were used. The performance of LVQ and other classification models was compared in relation to self-organizing maps, back-propagation applied to multilayer perceptrons, Bayesian networks, naive Bayes, J48 decision trees, k, and k-nearest-neighbor classifiers. Classifier accuracy was assessed by means of leave-one-out cross-validation, area under ROC curve (AUC), and inter-rater agreement (Kappa statistics). RESULTS: By using the LVQ model, five postural profiles for mouth-breathing children could be determined. LVQ showed satisfactory results for mouth-breathing and nose-breathing classification: sensitivity and specificity rates of 0.90 and 0.95, respectively, when using the training dataset, and 0.95 and 0.90, respectively, when using the validation dataset. CONCLUSIONS: The five postural profiles for mouth-breathing children suggested by LVQ were incorporated into application software for classifying the severity of mouth breathers' abnormal posture.

Pulse wave velocity and coronary risk stratification.

BACKGROUND: Arterial compliance or stiffness is an important determinant of cardiovascular disease and there is considerable interest in its noninvasive measurement. Pulse wave velocity (PWV) is widely used as an index of arterial stiffness. AIM: To determine whether PWV is useful for risk stratification in both healthy individuals and coronary patients. METHODS: Control subjects, n=510, aged 46.1 +/- 11 years, with no history of coronary disease, were selected from electoral rolls, and coronary patients, n=301, aged 53.7 +/- 10 years, were selected from hospital patients with a history of coronary artery disease (CAD) confirmed by coronary angiogram (at least 75% obstruction of one of the main coronary vessels). The asymptomatic subjects without CAD formed Group A, and were subdivided into A1 (without hypertension, dyslipidemia and/or diabetes) and A2 (with hypertension, dyslipidemia and/or diabetes). The coronary patients formed Group B, who were also subdivided into B1, without these classic risk factors, and B2 with hypertension, dyslipidemia and/or diabetes. We used the Student's t test to compare continuous variables and the chi-square test to compare categorical data. The strength of correlation between continuous variables was tested by Pearson's linear correlation. Independent variables predictive of CAD were determined by backward logistic regression analysis. The statistical analysis was performed using SPSS for Windows version 11.0 and data were expressed as means +/- SD; a p value of 0.05 was considered significant. RESULTS: Comparing the two groups A1 and A2, mean PWV was significantly lower in group A1. Comparing B1 and B2, mean PWV was also significantly lower in group B1. In group A1, PWV was significantly and positively correlated with age, body mass index, waist-to-hip ratio, alcohol consumption, total/HDL cholesterol ratio, systolic, diastolic and mean blood pressure (BP), blood glucose, apo B, triglycerides, and high-sensitivity C-reactive protein, unlike HDL which was inversely correlated (Pearson's coefficient). In group A2, PWV was significantly and positively correlated with age, alcohol consumption, total/HDL cholesterol ratio, systolic, diastolic and mean BP, blood glucose and pulse pressure (PP), but not HDL, which was inversely correlated with PWV. In group B1, PWV was only significantly and positively correlated with age, systolic, mean, and diastolic BP and PP, and presented a significant inverse correlation with ejection fraction. However, in the high-risk coronary population (group B2), there was a positive correlation with age, waist-to-hip ratio, systolic and mean BP, PP and homocysteine. After stepwise logistic regression, PWV remained in the model and proved to be a significant and independent risk factor for CAD. CONCLUSION: The results of our study show that PWV is higher in high-risk groups and significantly correlated with many classic and new CAD risk markers, suggesting that there is a cumulative influence of risk factors in the development of arterial stiffness. We believe that PWV is a useful index of vascular status and hence cardiovascular risk and that it may be useful for risk stratification in both asymptomatic and coronary patients.