Ability of different adiposity indicators to identify children with elevated blood pressure.

OBJECTIVE: Body composition measured by dual-energy X-ray absorptiometry (DXA) is believed to be superior to crude measures such as BMI or waist circumference (WC) to assess health risks associated with adiposity in adults. We compared the ability of BMI, WC, waist-to-height ratio (WHtR), percentage body fat from skinfold thickness, and measures of total and central fat assessed by DXA to identify children with elevated blood pressure (BP). STUDY DESIGN: The QUALITY Study follows 630 Caucasian families (father, mother, and child originally aged 8-10 years). BP, height, weight, WC, and skinfold thickness were measured according to standardized protocols. Elevated BP was defined as systolic or diastolic BP at least 90th age, sex, and height-specific percentile. Total and central fat were determined with DXA. The area under the receiver operating characteristic (ROC) curve (AUC) statistic was computed from logistic models that adjusted for age, sex, height, Tanner stage, and physical activity. RESULTS: All adiposity indicators were highly correlated. WC and WHtR did not show superior ability over BMI to identify children with elevated SBP (P = 0.421 and 0.473). Measures of total and central fat from DXA did not show an improved ability over BMI or WC to identify children with elevated SBP (P = 0.325-0.662). CONCLUSION: Results support the use of BMI in clinical and public health settings, at least in this age group. As all indicators had a limited ability to identify children with elevated BP, results also support measurement of BP in all children of this age independent of a weight status.

A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder.

BACKGROUND: Obesity is strongly associated with major depressive disorder (MDD) and various other diseases. Genome-wide association studies have identified multiple risk loci robustly associated with body mass index (BMI). In this study, we aimed to investigate whether a genetic risk score (GRS) combining multiple BMI risk loci might have utility in prediction of obesity in patients with MDD. METHODS: Linear and logistic regression models were conducted to predict BMI and obesity, respectively, in three independent large case-control studies of major depression (Radiant, GSK-Munich, PsyCoLaus). The analyses were first performed in the whole sample and then separately in depressed cases and controls. An unweighted GRS was calculated by summation of the number of risk alleles. A weighted GRS was calculated as the sum of risk alleles at each locus multiplied by their effect sizes. Receiver operating characteristic (ROC) analysis was used to compare the discriminatory ability of predictors of obesity. RESULTS: In the discovery phase, a total of 2,521 participants (1,895 depressed patients and 626 controls) were included from the Radiant study. Both unweighted and weighted GRS were highly associated with BMI (P <0.001) but explained only a modest amount of variance. Adding 'traditional' risk factors to GRS significantly improved the predictive ability with the area under the curve (AUC) in the ROC analysis, increasing from 0.58 to 0.66 (95% CI, 0.62-0.68; χ(2) = 27.68; P <0.0001). Although there was no formal evidence of interaction between depression status and GRS, there was further improvement in AUC in the ROC analysis when depression status was added to the model (AUC = 0.71; 95% CI, 0.68-0.73; χ(2) = 28.64; P <0.0001). We further found that the GRS accounted for more variance of BMI in depressed patients than in healthy controls. Again, GRS discriminated obesity better in depressed patients compared to healthy controls. We later replicated these analyses in two independent samples (GSK-Munich and PsyCoLaus) and found similar results. CONCLUSIONS: A GRS proved to be a highly significant predictor of obesity in people with MDD but accounted for only modest amount of variance. Nevertheless, as more risk loci are identified, combining a GRS approach with information on non-genetic risk factors could become a useful strategy in identifying MDD patients at higher risk of developing obesity.

The Atherosclerosis Burden Score (ABS): a Convenient Ultrasound-Based Score of Peripheral Atherosclerosis for Coronary Artery Disease Prediction.

Ultrasonographic detection of subclinical atherosclerosis improves cardiovascular risk stratification, but uncertainty persists about the most discriminative method to apply. In this study, we found that the "atherosclerosis burden score (ABS)", a novel straightforward ultrasonographic score that sums the number of carotid and femoral arterial bifurcations with plaques, significantly outperformed common carotid intima-media thickness, carotid mean/maximal thickness, and carotid/femoral plaque scores for the detection of coronary artery disease (CAD) (receiver operating characteristic (ROC) curve area under the curve (AUC) = 0.79; P = 0.027 to <0.001 with the other five US endpoints) in 203 patients undergoing coronary angiography. ABS was also more correlated with CAD extension (R = 0.55; P < 0.001). Furthermore, in a second group of 1128 patients without cardiovascular disease, ABS was weakly correlated with the European Society of Cardiology chart risk categories (R (2) = 0.21), indicating that ABS provided information beyond usual cardiovascular risk factor-based risk stratification. Pending prospective studies on hard cardiovascular endpoints, ABS appears as a promising tool in primary prevention.

Multislice CT for assessing in-stent dimensions after left main coronary artery stenting: a comparison with three dimensional intravascular ultrasound

Objective: To evaluate the agreement between multislice CT (MSCT) and intravascular ultrasound (IVUS) to assess the in-stent lumen diameters and lumen areas of left main coronary artery (LMCA) stents. Design: Prospective, observational single centre study. Setting: A single tertiary referral centre. Patients: Consecutive patients with LMCA stenting excluding patients with atrial fibrillation and chronic renal failure. Interventions: MSCT and IVUS imaging at 9-12 months follow-up were performed for all patients. Main outcome measures: Agreement between MSCT and IVUS minimum luminal area (MLA) and minimum luminal diameter (MLD). A receiver operating characteristic (ROC) curve was plotted to find the MSCT cut-off point to diagnose binary restenosis equivalent to 6 mm2 by IVUS. Results: 52 patients were analysed. Passing-Bablok regression analysis obtained a β coefficient of 0.786 (0.586 to 1.071) for MLA and 1.250 (0.936 to 1.667) for MLD, ruling out proportional bias. The α coefficient was −3.588 (−8.686 to −0.178) for MLA and −1.713 (−3.583 to −0.257) for MLD, indicating an underestimation trend of MSCT. The ROC curve identified an MLA ≤4.7 mm2 as the best threshold to assess in-stent restenosis by MSCT. Conclusions: Agreement between MSCT and IVUS to assess in-stent MLA and MLD for LMCA stenting is good. An MLA of 4.7 mm2 by MSCT is the best threshold to assess binary restenosis. MSCT imaging can be considered in selected patients to assess LMCA in-stent restenosis

CSF lactate for accurate diagnosis of community-acquired bacterial meningitis.

CSF lactate measurement is recommended when nosocomial meningitis is suspected, but its value in community-acquired bacterial meningitis is controversial. We evaluated the diagnostic performance of lactate and other CSF parameters in a prospective cohort of adult patients with acute meningitis. Diagnostic accuracy of lactate and other CSF parameters in patients with microbiologically documented episodes was assessed by receiver operating characteristic (ROC) curves. The cut-offs with the best diagnostic performance were determined. Forty-five of 61 patients (74%) had a documented bacterial (n = 18; S. pneumoniae, 11; N. meningitidis, 5; other, 2) or viral (n = 27 enterovirus, 21; VZV, 3; other, 3) etiology. CSF parameters were significantly different in bacterial vs. viral meningitis, respectively (p < 0.001 for all comparisons): white cell count (median 1333 vs. 143/mm(3)), proteins (median 4115 vs. 829 mg/l), CSF/blood glucose ratio (median 0.1 vs. 0.52), lactate (median 13 vs. 2.3 mmol/l). ROC curve analysis showed that CSF lactate had the highest accuracy for discriminating bacterial from viral meningitis, with a cutoff set at 3.5 mmol/l providing 100% sensitivity, specificity, PPV, NPV, and efficiency. CSF lactate had the best accuracy for discriminating bacterial from viral meningitis and should be included in the initial diagnostic workup of this condition.

External Validation of the Prestroke Independence, Sex, Age, National Institutes of Health Stroke Scale (ISAN) Score for Predicting Stroke-Associated Pneumonia in the Athens Stroke Registry.

BACKGROUND AND PURPOSE: The Prestroke Independence, Sex, Age, National Institutes of Health Stroke Scale (ISAN) score was developed recently for predicting stroke-associated pneumonia (SAP), one of the most common complications after stroke. The aim of the present study was to externally validate the ISAN score. METHODS: Data included in the Athens Stroke Registry between June 1992 and December 2011 were used for this analysis. Inclusion criteria were the availability of all ISAN score variables (prestroke independence, sex, age, National Institutes of Health Stroke Scale score). Receiver operating characteristic curves and linear regression analyses were used to determine the discriminatory power of the score and to assess the correlation between actual and predicted pneumonia in the study population. Separate analyses were performed for patients with acute ischemic stroke (AIS) and intracerebral hemorrhage (ICH). RESULTS: The analysis included 3204 patients (AIS: 2732, ICH: 472). The ISAN score demonstrated excellent discrimination in patients with AIS (area under the curve [AUC]: .83 [95% confidence interval {CI}: .81-.85]). In the ICH group, the score was less effective (AUC: .69 [95% CI: .63-.74]). Higher-risk groups of ISAN score were associated with an increased relative risk of SAP; risk increase was more prominent in the AIS population. Predicted pneumonia correlated very well with actual pneumonia (AIS group: R(2) = .885; β-coefficient = .941, P < .001; ICH group: R(2) = .880, β-coefficient = .938, P < .001). CONCLUSIONS: In our external validation in the Athens Stroke Registry cohort, the ISAN score predicted SAP very accurately in AIS patients and demonstrated good discriminatory power in the ICH group. Further validation and assessment of clinical usefulness would strengthen the score's utility further.

Symptoms Have Modest Accuracy in Detecting Endoscopic and Histologic Remission in Adults With Eosinophilic Esophagitis.

BACKGROUND & AIMS: It is not clear whether symptoms alone can be used to estimate the biologic activity of eosinophilic esophagitis (EoE). We aimed to evaluate whether symptoms can be used to identify patients with endoscopic and histologic features of remission. METHODS: Between April 2011 and June 2014, we performed a prospective, observational study and recruited 269 consecutive adults with EoE (67% male; median age, 39 years old) in Switzerland and the United States. Patients first completed the validated symptom-based EoE activity index patient-reported outcome instrument and then underwent esophagogastroduodenoscopy with esophageal biopsy collection. Endoscopic and histologic findings were evaluated with a validated grading system and standardized instrument, respectively. Clinical remission was defined as symptom score <20 (range, 0-100); histologic remission was defined as a peak count of <20 eosinophils/mm(2) in a high-power field (corresponds to approximately <5 eosinophils/median high-power field); and endoscopic remission as absence of white exudates, moderate or severe rings, strictures, or combination of furrows and edema. We used receiver operating characteristic analysis to determine the best symptom score cutoff values for detection of remission. RESULTS: Of the study subjects, 111 were in clinical remission (41.3%), 79 were in endoscopic remission (29.7%), and 75 were in histologic remission (27.9%). When the symptom score was used as a continuous variable, patients in endoscopic, histologic, and combined (endoscopic and histologic remission) remission were detected with area under the curve values of 0.67, 0.60, and 0.67, respectively. A symptom score of 20 identified patients in endoscopic remission with 65.1% accuracy and histologic remission with 62.1% accuracy; a symptom score of 15 identified patients with both types of remission with 67.7% accuracy. CONCLUSIONS: In patients with EoE, endoscopic or histologic remission can be identified with only modest accuracy based on symptoms alone. At any given time, physicians cannot rely on lack of symptoms to make assumptions about lack of biologic disease activity in adults with EoE. ClinicalTrials.gov, Number: NCT00939263.

Screening of breast lesions: a comparative study between mammography, B-mode ultrasonography, sonoelastography and histological results

Objective To compare the capacity of mammography, sonoelastography, B-mode ultrasonography and histological analysis to differentiate benign from malignant breast lesions. Materials and Methods A total of 12 histopathologically confirmed breast lesions were documented. The lesions were assessed by means of mammography, B-mode ultrasonography and sonoelastography, and histopathological analysis was utilized as a gold standard. Sensitivity and specificity were calculated. A receiver operating characteristic (ROC) curve was constructed to evaluate the diagnostic performance of the mentioned techniques. Results Sensitivity and specificity in the differentiation between benign and malignant lesions were respectively 100% and 50% for mammography, 100% and 71% for B-mode ultrasonography, and 67% and 83% for sonoelastography. The area under the ROC curve was calculated for the three imaging modalities and corresponded to 0.792 for mammography, 0.847 for B-mode ultrasonography, and 0.806 for sonoelastography. Conclusion Sonoelastography demonstrated higher specificity and lower sensitivity as compared with mammography and B-mode ultrasonography. On the other hand, B-mode ultrasonography had the largest area under the ROC curve. Sonoelastography has demonstrated to be a promising technique to detect and evaluate breast lesions, and could potentially reduce the number of unnecessary biopsies.

Accuracy of computer-aided ultrasound as compared with magnetic resonance imaging in the evaluation of nonalcoholic fatty liver disease in obese and eutrophic adolescents

AbstractObjective:To compare the accuracy of computer-aided ultrasound (US) and magnetic resonance imaging (MRI) by means of hepatorenal gradient analysis in the evaluation of nonalcoholic fatty liver disease (NAFLD) in adolescents.Materials and Methods:This prospective, cross-sectional study evaluated 50 adolescents (aged 11–17 years), including 24 obese and 26 eutrophic individuals. All adolescents underwent computer-aided US, MRI, laboratory tests, and anthropometric evaluation. Sensitivity, specificity, positive and negative predictive values and accuracy were evaluated for both imaging methods, with subsequent generation of the receiver operating characteristic (ROC) curve and calculation of the area under the ROC curve to determine the most appropriate cutoff point for the hepatorenal gradient in order to predict the degree of steatosis, utilizing MRI results as the gold-standard.Results:The obese group included 29.2% girls and 70.8% boys, and the eutrophic group, 69.2% girls and 30.8% boys. The prevalence of NAFLD corresponded to 19.2% for the eutrophic group and 83% for the obese group. The ROC curve generated for the hepatorenal gradient with a cutoff point of 13 presented 100% sensitivity and 100% specificity. As the same cutoff point was considered for the eutrophic group, false-positive results were observed in 9.5% of cases (90.5% specificity) and false-negative results in 0% (100% sensitivity).Conclusion:Computer-aided US with hepatorenal gradient calculation is a simple and noninvasive technique for semiquantitative evaluation of hepatic echogenicity and could be useful in the follow-up of adolescents with NAFLD, population screening for this disease as well as for clinical studies.

A Quantitative method for the characterization of lytic metastases of the bone from radiographic images

The aim of our study was to assess the diagnostic usefulness of the gray level parameters to distinguish osteolytic lesions using radiological images. Materials and Methods: A retrospective study was carried out. A total of 76 skeletal radiographs of osteolytic metastases and 67 radiographs of multiple myeloma were used. The cases were classified into nonflat (MM1 and OL1) and flat bones (MM2 and OL2). These radiological images were analyzed by using a computerized method. The parameters calculated were mean, standard deviation, and coefficient of variation (MGL, SDGL, and CVGL) based on gray level histogram analysis of a region-of-interest.Diagnostic utility was quantified bymeasurement of parameters on osteolyticmetastases andmultiplemyeloma, yielding quantification of area under the receiver operating characteristic (ROC) curve (AUC). Results: Flat bone groups (MM2 and OL2) showed significant differences in mean values of MGL ( = 0.048) and SDGL ( = 0.003). Their corresponding values of AUC were 0.758 for MGL and 0.883 for SDGL in flat bones. In nonflat bones these gray level parameters do not show diagnostic ability. Conclusion: The gray level parametersMGL and SDGL show a good discriminatory diagnostic ability to distinguish between multiple myeloma and lytic metastases in flat bones.

Accuracy of sonography and hysteroscopy in the diagnosis of premalignant and malignant polyps in postmenopausal women

PURPOSE: To evaluate the accuracy of sonographic endometrial thickness and hysteroscopic characteristics in predicting malignancy in postmenopausal women undergoing surgical resection of endometrial polyps. METHODS: Five hundred twenty-one (521) postmenopausal women undergoing hysteroscopic resection of endometrial polyps between January 1998 and December 2008 were studied. For each value of sonographic endometrial thickness and polyp size on hysteroscopy, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated in relation to the histologic diagnosis of malignancy. The best values of sensitivity and specificity for the diagnosis of malignancy were determined by the Receiver Operating Characteristic (ROC) curve. RESULTS: Histologic diagnosis identified the presence of premalignancy or malignancy in 4.1% of cases. Sonographic measurement revealed a greater endometrial thickness in cases of malignant polyps when compared to benign and premalignant polyps. On surgical hysteroscopy, malignant endometrial polyps were also larger. An endometrial thickness of 13 mm showed a sensitivity of 69.6%, specificity of 68.5%, PPV of 9.3%, and NPV of 98% in predicting malignancy in endometrial polyps. Polyp measurement by hysteroscopy showed that for polyps 30 mm in size, the sensitivity was 47.8%, specificity was 66.1%, PPV was 6.1%, and NPV was 96.5% for predicting cancer. CONCLUSIONS: Sonographic endometrial thickness showed a higher level of accuracy than hysteroscopic measurement in predicting malignancy in endometrial polyps. Despite this, both techniques showed low accuracy for predicting malignancy in endometrial polyps in postmenopausal women. In suspected cases, histologic evaluation is necessary to exclude malignancy.

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach

Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing

Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

Risco de abandono de relacionamento

This paper presents a study carried out with customers with credit card of a large retailer to measure the risk of abandonment of a relationship, when this has already purchase history. Two activities are the most important in this study: the theoretical and methodological procedures. The first step was to the understanding of the problem, the importance of theme and the definition of search methods. The study brings a bibliographic survey comprising several authors and shows that the loyalty of customers is the basis that gives sustainability and profitability for organizations of various market segments, examines the satisfaction as the key to success for achievement and specially for the loyalty of customers. To perform this study were adjusted logistic-linear models and through the test Kolmogorov - Smirnov (KS) and the curve Receiver Operating Characteristic (ROC) selected the best model. Had been used cadastral and transactional data of 100,000 customers of credit card issuer, the software used was SPSS which is a modern system of data manipulation, statistical analysis and presentation graphics. In research, we identify the risk of each customer leave the product through a score.