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Youden Index (youden + index)
Selected AbstractsYouden Index and Optimal Cut-Point Estimated from Observations Affected by a Lower Limit of DetectionBIOMETRICAL JOURNAL, Issue 3 2008Marcus D. Ruopp Abstract The receiver operating characteristic (ROC) curve is used to evaluate a biomarker's ability for classifying disease status. The Youden Index (J), the maximum potential effectiveness of a biomarker, is a common summary measure of the ROC curve. In biomarker development, levels may be unquantifiable below a limit of detection (LOD) and missing from the overall dataset. Disregarding these observations may negatively bias the ROC curve and thus J. Several correction methods have been suggested for mean estimation and testing; however, little has been written about the ROC curve or its summary measures. We adapt non-parametric (empirical) and semi-parametric (ROC-GLM [generalized linear model]) methods and propose parametric methods (maximum likelihood (ML)) to estimate J and the optimal cut-point (c *) for a biomarker affected by a LOD. We develop unbiased estimators of J and c * via ML for normally and gamma distributed biomarkers. Alpha level confidence intervals are proposed using delta and bootstrap methods for the ML, semi-parametric, and non-parametric approaches respectively. Simulation studies are conducted over a range of distributional scenarios and sample sizes evaluating estimators' bias, root-mean square error, and coverage probability; the average bias was less than one percent for ML and GLM methods across scenarios and decreases with increased sample size. An example using polychlorinated biphenyl levels to classify women with and without endometriosis illustrates the potential benefits of these methods. We address the limitations and usefulness of each method in order to give researchers guidance in constructing appropriate estimates of biomarkers' true discriminating capabilities. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Bedside screening for executive dysfunction in patients with subcortical ischemic vascular diseaseINTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY, Issue 9 2009Nils Margraf Objective We investigated several executive bedside tests for their effectiveness in the routine clinical diagnostics of dysexecutive syndrome in subcortical ischemic vascular disease (SIVD). Methods Five executive tests, CLOX, the Tower of London (ToL), a cognitive estimation test (CET), a verbal fluency test, and the Five-Point Test, were examined in 17 patients with marked cerebral microangiopathy in cranial MRI and clinical symptoms of SIVD. The test accuracy for discriminating the patients from 17 healthy comparison subjects closely matched for age, gender and level of education was determined. Results Aside from the CET we found a significant lower performance of the patients with SIVD in four of the five used executive tests. In receiver operating characteristic (ROC) analyses the accuracy of CLOX 1 showed excellent results for distinguishing between patients and comparison subjects (area under the curve (AUC) 0.901), while the ToL (AUC up to 0.845) and the productivity in the phonemic verbal fluency test (AUC 0.829) achieved a good accuracy. Differently the accuracy of the figural fluency was only poor to fair (AUC 0.706). However, the Youden Indices of the significant executive variables showed a wide range from 0.25 to 0.82. Conclusions Based on our data we consider CLOX, the ToL and the verbal fluency test promising executive bedside test concepts for diagnosing the dysexecutive syndrome in SIVD in clinical routine. Particularly for CLOX and the ToL a further psychometric evaluation is required. Copyright © 2009 John Wiley & Sons, Ltd. [source] Prospective cohort study comparing sequential organ failure assessment and acute physiology, age, chronic health evaluation III scoring systems for hospital mortality prediction in critically ill cirrhotic patientsINTERNATIONAL JOURNAL OF CLINICAL PRACTICE, Issue 2 2006Y-C Chen Summary The aim of the study was to evaluate the usefulness of sequential organ failure assessment (SOFA) and acute physiology, age, chronic health evaluation III (APACHE III) scoring systems obtained on the first day of intensive care unit (ICU) admission in predicting hospital mortality in critically ill cirrhotic patients. The study enrolled 102 cirrhotic patients consecutively admitted to ICU during a 1-year period. Twenty-five demographic, clinical and laboratory variables were analysed as predicators of survival. Information considered necessary to calculate the Child,Pugh, SOFA and APACHE III scores on the first day of ICU admission was also gathered. Overall hospital mortality was 68.6%. Multiple logistic regression analysis revealed that mean arterial pressure, SOFA and APACHE III scores were significantly related to prognosis. Goodness-of-fit was good for the SOFA and APACHE III models. Both predictive models displayed a similar degree of the best Youden index (0.68) and overall correctness (84%) of prediction. The SOFA and APACHE III models displayed good areas under the receiver,operating characteristic curve (0.917 ± 0.028 and 0.912 ± 0.029, respectively). Finally, a strong and significant positive correlation exists between SOFA and APACHE III scores for individual patients (r2 = 0.628, p < 0.001). This investigation confirms the grave prognosis for cirrhotic patients admitted to ICU. Both SOFA and APACHE III scores are excellent tools to predict the hospital mortality in critically ill cirrhotic patients. The overall predictive accuracy of SOFA and APACHE III is superior to that of Child,Pugh system. The role of these scoring systems in describing the dynamic aspects of clinical courses and allocating ICU resources needs to be clarified. [source] In silico prediction and screening of ,-secretase inhibitors by molecular descriptors and machine learning methodsJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 6 2010Xue-Gang Yang Abstract ,-Secretase inhibitors have been explored for the prevention and treatment of Alzheimer's disease (AD). Methods for prediction and screening of ,-secretase inhibitors are highly desired for facilitating the design of novel therapeutic agents against AD, especially when incomplete knowledge about the mechanism and three-dimensional structure of ,-secretase. We explored two machine learning methods, support vector machine (SVM) and random forest (RF), to develop models for predicting ,-secretase inhibitors of diverse structures. Quantitative analysis of the receiver operating characteristic (ROC) curve was performed to further examine and optimize the models. Especially, the Youden index (YI) was initially introduced into the ROC curve of RF so as to obtain an optimal threshold of probability for prediction. The developed models were validated by an external testing set with the prediction accuracies of SVM and RF 96.48 and 98.83% for ,-secretase inhibitors and 98.18 and 99.27% for noninhibitors, respectively. The different feature selection methods were used to extract the physicochemical features most relevant to ,-secretase inhibition. To the best of our knowledge, the RF model developed in this work is the first model with a broad applicability domain, based on which the virtual screening of ,-secretase inhibitors against the ZINC database was performed, resulting in 368 potential hit candidates. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010 [source] Understanding diagnostic tests 3: receiver operating characteristic curvesACTA PAEDIATRICA, Issue 5 2007Anthony K Akobeng Abstract The results of many clinical tests are quantitative and are provided on a continuous scale. To help decide the presence or absence of disease, a cut-off point for ,normal' or ,abnormal' is chosen. The sensitivity and specificity of a test vary according to the level that is chosen as the cut-off point. The receiver operating characteristic (ROC) curve, a graphical technique for describing and comparing the accuracy of diagnostic tests, is obtained by plotting the sensitivity of a test on the y axis against 1-specificity on the x axis. Two methods commonly used to establish the optimal cut-off point include the point on the ROC curve closest to (0, 1) and the Youden index. The area under the ROC curve provides a measure of the overall performance of a diagnostic test. In this paper, the author explains how the ROC curve can be used to select optimal cut-off points for a test result, to assess the diagnostic accuracy of a test, and to compare the usefulness of tests. Conclusion: The ROC curve is obtained by calculating the sensitivity and specificity of a test at every possible cut-off point, and plotting sensitivity against 1-specificity. The curve may be used to select optimal cut-off values for a test result, to assess the diagnostic accuracy of a test, and to compare the usefulness of different tests. [source] Screening for diabetes in Indigenous populations using glycated haemoglobin: sensitivity, specificity, post-test likelihood and risk of diseaseDIABETIC MEDICINE, Issue 7 2005K. G. Rowley Abstract Aims Screening for diabetes using glycated haemoglobin (HbA1c) offers potential advantages over fasting glucose or oral glucose tolerance testing. Current recommendations advise against the use of HbA1c for screening but test properties may vary systematically across populations, according to the diabetes prevalence and risk. We aimed to: (i) characterize the properties of test cut-offs of HbA1c for diagnosis of diabetes relative to a diagnosis based on a fasting plasma glucose concentration of 7.0 mmol/l for high-risk Indigenous populations; and (ii) examine test properties across a range of diabetes prevalence from 5 to 30%. Methods Data were collected from Aboriginal and Torres Strait Islander communities in Australia and a Canadian First Nations community (diabetes prevalence 12,22%) in the course of diabetes diagnostic and risk factor screening programmes (n = 431). Screening test properties were analyzed for the range of HbA1c observed (3,12.9%). Results In separate and pooled analyses, a HbA1c cut point of 7.0% proved the optimal limit for classifying diabetes, with summary analysis results of sensitivity = 73 (56,86)%, specificity = 98 (96,99)%, overall agreement (Youden's index) = 0.71, and positive predictive value (for an overall prevalence of 18%) = 88%. For diabetes prevalence from 5 to 30% the post-test likelihood of having diabetes given HbA1c = 7.0% (positive predictive value) ranged from 62.7 to 93.2%; for HbA1c < 7.0%, the post-test likelihood of having diabetes ranged from 4.5 to 27.7%. Conclusions The results converge with research on the likelihood of diabetes complications in supporting a HbA1c cut-off of 7.0% in screening for diabetes in epidemiological research. Glycated haemoglobin has potential utility in screening for diabetes in high-risk populations. [source] | ||||||||||