Use of the mini nutritional assessment tool in elderly people from long-term institutions of southeast of Brazil

Background: the Mini Nutritional Assessment (MNA) is a multidimensional method of nutritional evaluation that allows the diagnosis of malnutrition and risk of malnutrition in elderly people, it is important to mention that this method has not been well studied in Brazil. Objective: to verify the use of the MNA in elderly people that has been living in long term institutions for elderly people. Design: transversal study. Participants: 89 people (>= 60 years), being 64.0% men. The average of age for both genders was 73.7 +/- 9.1 years old, being 72.8 +/- 8.9 years old for men, and 75.3 +/- 9.3 years old for women. Setting: long-term institutions for elderly people located in the Southeast of Brazil. Methods: it was calculated the sensibility, specificity, and positive and negative predictive values. It was data to set up a ROC curve to verify the accuracy of the MNA. The variable used as a ""standard"" for the nutritional diagnosis of the elderly people was the corrected arm muscle area because it is able to provide information or an estimative of the muscle reserve of a person being considered a good indicator of malnutrition in elderly people. Results: the sensibility was 84.0%, the specificity was 36.0%, the positive predictive value was 77.0%, and the negative predictive value was 47.0%; the area of the ROC curve was 0.71 (71.0%). Conclusion: the MNA method has showed accuracy, and sensibility when dealing with the diagnosis of malnutrition and risk of malnutrition in institutionalized elderly groups of the Southeastern region of Brazil, however, it presented a low specificity.

Use of a predictive model for food insecurity estimates in Brazil

In 2004 the National Household Survey (Pesquisa Nacional par Amostras de Domicilios - PNAD) estimated the prevalence of food and nutrition insecurity in Brazil. However, PNAD data cannot be disaggregated at the municipal level. The objective of this study was to build a statistical model to predict severe food insecurity for Brazilian municipalities based on the PNAD dataset. Exclusion criteria were: incomplete food security data (19.30%); informants younger than 18 years old (0.07%); collective households (0.05%); households headed by indigenous persons (0.19%). The modeling was carried out in three stages, beginning with the selection of variables related to food insecurity using univariate logistic regression. The variables chosen to construct the municipal estimates were selected from those included in PNAD as well as the 2000 Census. Multivariate logistic regression was then initiated, removing the non-significant variables with odds ratios adjusted by multiple logistic regression. The Wald Test was applied to check the significance of the coefficients in the logistic equation. The final model included the variables: per capita income; years of schooling; race and gender of the household head; urban or rural residence; access to public water supply; presence of children; total number of household inhabitants and state of residence. The adequacy of the model was tested using the Hosmer-Lemeshow test (p=0.561) and ROC curve (area=0.823). Tests indicated that the model has strong predictive power and can be used to determine household food insecurity in Brazilian municipalities, suggesting that similar predictive models may be useful tools in other Latin American countries.

A Survey on Graphical Methods for Classification Predictive Performance Evaluation

Predictive performance evaluation is a fundamental issue in design, development, and deployment of classification systems. As predictive performance evaluation is a multidimensional problem, single scalar summaries such as error rate, although quite convenient due to its simplicity, can seldom evaluate all the aspects that a complete and reliable evaluation must consider. Due to this, various graphical performance evaluation methods are increasingly drawing the attention of machine learning, data mining, and pattern recognition communities. The main advantage of these types of methods resides in their ability to depict the trade-offs between evaluation aspects in a multidimensional space rather than reducing these aspects to an arbitrarily chosen (and often biased) single scalar measure. Furthermore, to appropriately select a suitable graphical method for a given task, it is crucial to identify its strengths and weaknesses. This paper surveys various graphical methods often used for predictive performance evaluation. By presenting these methods in the same framework, we hope this paper may shed some light on deciding which methods are more suitable to use in different situations.

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

PAIN IN FIBROMYALGIA AND DISCRIMINATIVE POWER OF THE INSTRUMENTS: VISUAL ANALOG SCALE, DOLORIMETRY AND THE MCGILL PAIN QUESTIONNAIRE

Objective: The aim of this study was to verify the discriminative power of the most widely used pain assessment instruments. Methods: The sample consisted of 279 subjects divided into Fibromyalgia Group (FM- 205 patients with fibromyalgia) and Control Group (CG-74 healthy subjects), mean age 49.29 +/- 10.76 years. Only 9 subjects were male, 6 in FM and 3 in CG. FM were outpatients from the Rheumatology Clinic of the University of Sao Paulo - Hospital das Clinicas (HCFMUSP); the CG included people accompanying patients and hospital staff with similar socio-demographic characteristics. Three instruments were used to assess pain: the McGill Pain Questionnaire (MPQ), the Visual Analog Scale (VAS), and the Dolorimetry, to measure pain threshold on tender points (generating the TP index). In order to assess the discriminative power of the instruments, the measurements obtained were submitted to descriptive analysis and inferential analysis using ROC Curve - sensibility (S), specificity (S I) and area under the curve (AUC) - and Contingence tables with Chi-square Test and odds ratio. Significance level was 0.05. Results: Higher sensibility, specificity and area under the curve was obtained by VAS (80%, 80% and 0.864, respectively), followed by Dolorimetry (S 77%, S177% and AUC 0.851), McGill Sensory (S 72%, S167% and AUC 0.765) and McGill Affective (S 69%, S1 67% and AUC 0.753). Conclusions: VAS presented the higher sensibility, specificity and AUC, showing the greatest discriminative power among the instruments. However, these values are considerably similar to those of Dolorimetry.

Integration of Ligand- and Target-Based Virtual Screening for the Discovery of Cruzain Inhibitors

A myriad of methods are available for virtual screening of small organic compound databases. In this study we have successfully applied a quantitative model of consensus measurements, using a combination of 3D similarity searches (ROCS and EON), Hologram Quantitative Structure Activity Relationships (HQSAR) and docking (FRED, FlexX, Glide and AutoDock Vina), to retrieve cruzain inhibitors from collected databases. All methods were assessed individually and then combined in a Ligand-Based Virtual Screening (LBVS) and Target-Based Virtual Screening (TBVS) consensus scoring, using Receiving Operating Characteristic (ROC) curves to evaluate their performance. Three consensus strategies were used: scaled-rank-by-number, rank-by-rank and rank-by-vote, with the most thriving the scaled-rank-by-number strategy, considering that the stiff ROC curve appeared to be satisfactory in every way to indicate a higher enrichment power at early retrieval of active compounds from the database. The ligand-based method provided access to a robust and predictive HQSAR model that was developed to show superior discrimination between active and inactive compounds, which was also better than ROCS and EON procedures. Overall, the integration of fast computational techniques based on ligand and target structures resulted in a more efficient retrieval of cruzain inhibitors with desired pharmacological profiles that may be useful to advance the discovery of new trypanocidal agents.

Coronary artery stenoses: detection with calcium scoring, CT angiography, and both methods combined

PURPOSE: To investigate prospectively the relative accuracy of computed tomographic (CT) angiography, calcium scoring (CS), and both methods combined in demonstrating coronary artery stenoses by using conventional angiography as the reference standard. MATERIALS AND METHODS: The study was approved by the institutional review board Human Research Ethics Committee, and all patients completed written informed consent. Fifty patients (40 men, 10 women) aged 62 years ± 11 (± standard deviation) who were suspected of having coronary artery disease underwent both conventional coronary angiography and multisection coronary CT angiography with CS. Sensitivity and specificity of CS, CT angiography, and both methods combined in demonstrating luminal stenosis greater than or equal to 50% were determined for each arterial segment, coronary vessel, and patient. Receiver operating characteristic (ROC) curves were generated for CS prediction of significant stenosis, and the Mann-Whitney U test was used for comparison of CS between groups. RESULTS: When used with segment-specific electrocardiographic phase reconstructions, CT angiography demonstrated stenosed segments with 79% sensitivity and 95% specificity. Mean calcium score was greater in segments, vessels, and patients with stenoses than in segments, vessels, and patients without stenoses (P < .001 for all); nine (16%) of 56 stenosed segments, however, had a calcium score of 0. The patient calcium score correlated strongly with the number of stenosed arteries (Spearman {rho} = 0.75, P < .001). CS was more accurate in demonstrating stenosis in patients than in segments (areas under ROC curve were 0.88 and 0.74, respectively). CT angiography, however, was more accurate than CS in demonstrating stenosis in patients, vessels, and segments. The sensitivity and specificity of CS varied according to the threshold used, but when the calcium score cutoff (ie, >150) matched the specificity of CT angiography (95%), the sensitivity of CS in demonstrating stenosed segments was 29% (compared with 79% for CT angiography). Combining CT angiography with CS (at threshold of 400) improved the sensitivity of CT angiography (from 93% to 100%) in demonstrating significant coronary disease in patients, without a loss of specificity (85%); this finding, however, was not statistically significant. CONCLUSION: CT angiography is more accurate than CS in demonstrating coronary stenoses. A patient calcium score of greater than or equal to 400, however, can be used to potentially identify patients with significant coronary stenoses not detected at CT angiography.

Subjective well-being as an indicator for clinical depression

The Theory of Homeostasis posits that Subjective Well-being (SWB) is regulated by a dynamic biological mechanism, assisting to maintain a positive view of life. Further, the theory suggests that clinical depression is the loss of SWB due to the defeat of this homeostatic defence system. To test this hypothesis it was predicted that people who were diagnosed as clinically depressed with the Semi-structured Clinical Interview (SCID-1/NP) based on the DSM-IV-TR Axis 1 would have a Personal Well-being Index-Adult (PWI-A) score below the normative range (70–80% of scale maximum). Following ethical approval a sample of 146 men was obtained and each was assessed on the SCID-1/NP and on the PWI-A. Subjects diagnosed as having one of several pathologies such as post traumatic stress disorder, panic disorder, social phobia and specific phobia were found to score significantly lower on the PWI-A compared to participants who received no diagnosis. However, as the data did not discriminate between currently depressed and persons with other non-depressive psychopathologies, a Receiver Operating Characteristics (ROC) curve analysis was used to explore this data further. Results indicated that the PWI-A was significantly better than guessing in discriminating clinically depressed cases, but only just so. Therefore, while this research found support for the proposition that the loss of SWB indicated clinical depression, the PWI-A is not sufficiently specific for diagnosis, nor can it be concluded that all instances of depression is the failure of SWB.

Cardiovascular risk prediction model development and assessment

The aim of this article is to review the development and assessment of cardiovascular risk prediction models and to discuss the predictive value of a risk factor as well as to introduce new assessment methods to evaluate a risk prediction model. Many cardiovascular risk prediction models have been developed during the past three decades. However, there has not been consistent agreement regarding how to appropriately assess a risk prediction model, especially when new markers are added to an existing model. The area under the receiver operating characteristic (ROC) curve has traditionally been used to assess the discriminatory ability of a risk prediction model. However, recent studies suggest that this method has its limitations and cannot be the sole approach to evaluate the usefulness of a new marker. New assessment methods are being developed to appropriately assess a risk prediction model and they will be gradually used in clinical and epidemiological studies.

Raising the profile of depression in the workplace

The prevalence of depression in the Australian workforce is unknown. Epidemiological surveys (e.g., the National Health Survey and National Survey of Mental Health and Wellbeing) do not routinely include a depression scale and within the mental health field, few studies focus on depression and employment groups specifically. Although the inclusion of a direct measure of depression in  national surveys is preferable, the prevalence of depression may be inferred from short screening scales of general mental health. In this paper, scores on the  General Health Questionnaire (GHQ-12) and the Kessler psychological distress scale (K10) for a sample of employed persons were mapped onto the CES-D (Iowa) measure of depression. The results of this study indicate that the  recommended GHQ-12 cut-off point is appropriate for estimating work-related depression prevalence. However, the cut-off point on the K10 (the short-scale  currently used in Australian national surveys) may need to be substantially  reduced if scores on the K10 are to be used to identify workers at risk of  depression. The routine inclusion of a direct depression measure in national  surveys is recommended, particularly considering the number of employed persons in Australia and large proportion of the sample classified as depressed in this study.

Quality of life assessment in the community-dwelling elderly : validation of the Assessment of Quality of Life (AQoL) instrument and comparison with the SF-36

Measurement of Health-Related Quality of Life (HRQoL) of the elderly requires instruments with demonstrated sensitivity, reliability, and validity, particularly with the increasing proportion of older people entering the health care system. This article reports the psychometric properties of the 12-item Assessment of Quality of Life (AQoL) instrument in chronically ill community-dwelling elderly people with an 18-month follow-up. Comparator instruments included the SF-36 and the OARS. Construct validity of the AQoL was strong when examined via factor analysis and convergent and divergent validity against other scales. Receiver Operator Characteristic (ROC) curve analyses and relative efficiency estimates indicated the AQoL is sensitive, responsive, and had the strongest predicative validity for nursing home entry. It was also sensitive to economic prediction over the follow-up. Given these robust psychometric properties and the brevity of the scale, AQoL appears to be a suitable instrument for epidemiologic studies where HRQoL and utility data are required from elderly populations.

Waist circumference has heterogeneous impact on development of diabetes in different populations : Longitudinal comparative study between Australia and Iran

Aims : Comparing waist circumference (WC) role in diabetes risk prediction and diagnosis of metabolic syndrome (MS) in different populations.

Methods : Population-based samples from Australia (n = 9026) and Iran (n = 8259) were studied in 2000 and followed for 4 years. Follow-up attendance was 58% and mean age was 51 vs. 47. Pearson correlations calculated between WC and other MS components. ROC for the role of WC in the prediction of incident diabetes was used.

Results : Prevalences of MS (48% vs. 28%), an increased WC (58.5% vs. 54.5%), low HDL-C (35% vs. 11.2%), high triglyceride (52.2% vs. 29.6%) were significantly higher in Iran. Fasting glucose ≥5.6 mmol/L was higher in Australia (26% vs. 23%). Hypertension was no different (38%). Pearson correlations between WC and other MS components were stronger in Australians: FPG (0.32 vs. 0.2), HDL (0.47 vs. 0.16), TG (0.38 vs. 0.30) and SBP (0.38 vs. 0.36). Among women, area under ROC curve for WC as a predictor for diabetes was significantly higher for Australians (0.76 vs. 0.68, p < 0.001) with no difference among men (0.69 vs. 0.71, p = 0.4).

Conclusion : WC was more strongly related to other components of MS in Australia. Association between WC and MS or incident diabetes varies between ethnicities.

Sample subset optimization for classifying imbalanced biological data

Data in many biological problems are often compounded by imbalanced class distribution. That is, the positive examples may largely outnumbered by the negative examples. Many classification algorithms such as support vector machine (SVM) are sensitive to data with imbalanced class distribution, and result in a suboptimal classification. It is desirable to compensate the imbalance effect in model training for more accurate classification. In this study, we propose a sample subset optimization technique for classifying biological data with moderate and extremely high imbalanced class distributions. By using this optimization technique with an ensemble of SVMs, we build multiple roughly balanced SVM base classifiers, each trained on an optimized sample subset. The experimental results demonstrate that the ensemble of SVMs created by our sample subset optimization technique can achieve higher area under the ROC curve (AUC) value than popular sampling approaches such as random over-/under-sampling; SMOTE sampling, and those in widely used ensemble approaches such as bagging and boosting.

A calibration protocol for population-specific accelerometer cut-points in children

Purpose

To test a field-based protocol using intermittent activities representative of children's physical activity behaviours, to generate behaviourally valid, population-specific accelerometer cut-points for sedentary behaviour, moderate, and vigorous physical activity.
Methods

Twenty-eight children (46% boys) aged 10–11 years wore a hip-mounted uniaxial GT1M ActiGraph and engaged in 6 activities representative of children's play. A validated direct observation protocol was used as the criterion measure of physical activity. Receiver Operating Characteristics (ROC) curve analyses were conducted with four semi-structured activities to determine the accelerometer cut-points. To examine classification differences, cut-points were cross-validated with free-play and DVD viewing activities.
Results

Cut-points of ≤372, >2160 and >4806 counts•min−1 representing sedentary, moderate and vigorous intensity thresholds, respectively, provided the optimal balance between the related needs for sensitivity (accurately detecting activity) and specificity (limiting misclassification of the activity). Cross-validation data demonstrated that these values yielded the best overall kappa scores (0.97; 0.71; 0.62), and a high classification agreement (98.6%; 89.0%; 87.2%), respectively. Specificity values of 96–97% showed that the developed cut-points accurately detected physical activity, and sensitivity values (89–99%) indicated that minutes of activity were seldom incorrectly classified as inactivity.
Conclusion

The development of an inexpensive and replicable field-based protocol to generate behaviourally valid and population-specific accelerometer cut-points may improve the classification of physical activity levels in children, which could enhance subsequent intervention and observational studies.

Are we predicting the actual or apparent distribution of temperate marine fishes?

Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change – particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km2 study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions.