Summary
Waist‐to‐height ratio (WHtR) is superior to body mass index and waist circumference for measuring adult cardio‐metabolic risk factors. However, there is no meta‐analysis to evaluate its ...discriminatory power in children and adolescents. A meta‐analysis was conducted using multiple databases, including Embase and Medline. Studies were included that utilized receiver‐operating characteristics curve analysis and published area under the receiver‐operating characteristics curves (AUC) for adiposity indicators with hyperglycaemia, elevated blood pressure, dyslipidemia, metabolic syndrome and other cardio‐metabolic outcomes. Thirty‐four studies met the inclusion criteria. AUC values were extracted and pooled using a random‐effects model and were weighted using the inverse variance method. The mean AUC values for each index were greater than 0.6 for most outcomes including hypertension. The values were the highest when screening for metabolic syndrome (AUC > 0.8). WHtR did not have significantly better screening power than other two indexes in most outcomes, except for elevated triglycerides when compared with body mass index and high metabolic risk score when compared with waist circumference. Although not being superior in discriminatory power, WHtR is convenient in terms of measurement and interpretation, which is advantageous in practice and allows for the quick identification of children with cardio‐metabolic risk factors at an early age.
The area under the receiver operating characteristic curve (AUC) is widely used in evaluating diagnostic performance for many clinical tasks. It is still challenging to evaluate the reading ...performance of distinguishing between positive and negative regions of interest (ROIs) in the nested-data problem, where multiple ROIs are nested within the cases. To address this issue, we identify two kinds of AUC estimators, within-cases AUC and between-cases AUC. We focus on the between-cases AUC estimator, since our main research interest is in patient-level diagnostic performance rather than location-level performance (the ability to separate ROIs with and without disease within each patient). Another reason is that as the case number increases, the number of between-cases paired ROIs is much larger than the number of within-cases ROIs. We provide estimators for the variance of the between-cases AUC and for the covariance when there are two readers. We derive and prove the above estimators’ theoretical values based on a simulation model and characterize their behavior using Monte Carlo simulation results. We also provide a real-data example. Moreover, we connect the distribution-based simulation model with the simulation model based on the linear mixed-effect model, which helps better understand the sources of variation in the simulated dataset.
The area under the receiver operating characteristic (ROC) curve (AUC) is commonly used for assessing the discriminative ability of prediction models even though the measure is criticized for being ...clinically irrelevant and lacking an intuitive interpretation. Every tutorial explains how the coordinates of the ROC curve are obtained from the risk distributions of diseased and non-diseased individuals, but it has not become common sense that therewith the ROC plot is just another way of presenting these risk distributions. We show how the ROC curve is an alternative way to present risk distributions of diseased and non-diseased individuals and how the shape of the ROC curve informs about the overlap of the risk distributions. For example, ROC curves are rounded when the prediction model included variables with similar effect on disease risk and have an angle when, for example, one binary risk factor has a stronger effect; and ROC curves are stepped rather than smooth when the sample size or incidence is low, when the prediction model is based on a relatively small set of categorical predictors. This alternative perspective on the ROC plot invalidates most purported limitations of the AUC and attributes others to the underlying risk distributions. AUC is a measure of the discriminative ability of prediction models. The assessment of prediction models should be supplemented with other metrics to assess their clinical utility.
As the literature review suggests, most professional voice users, such as teachers and singers, are prone to vocal abuse or misuse and frequently experience vocal fatigue. Therefore, validating the ...Vocal Fatigue Handicap Questionnaire among professional voice users with and without the symptoms of vocal fatigue might provide appropriate external validity of the questionnaire.
The objective of the study was to validate the Kannada version of the Vocal Fatigue Handicap Questionnaire (VFHQ-K) among a cohort of Kannada-speaking primary and secondary school teachers with and without self-reported vocal fatigue symptoms.
This was a validation study.
The study consisted of two groups of participants. Group 1 included 40 teachers with self-reported vocal fatigue symptoms, and Group 2 included 57 teachers without self-reported vocal fatigue symptoms. The VFHQ-K was administered to each participant after obtaining informed consent. The questionnaire was again readministered between 1 and 2 weeks to assess the test-retest reliability. All the responses that were obtained were tabulated for analysis.
The VFHQ-K demonstrated good test-retest reliability, internal consistency, and acceptable discriminant validity. The cutoff value of VFHQ-K obtained in the present study between the teachers with and without self-reported symptoms of vocal fatigue was much less than the cutoff values reported by the earlier version of VFHQ-K.
The VFHQ-K can be a helpful tool in the early identification of teachers with vocal fatigue and in improving the vocal health of professional voice users.
It is unclear whether salivary iodine concentration (SIC) can assess iodine status in females from different water iodine regions.
Through a cross-sectional study, we explored the feasibility of SIC ...as a biomarker to assess iodine status in females and develop optimal cutoff values.
A total of 1991 females were analyzed in this cross-sectional study from the coastal iodine-deficient areas (CIDAs), inland iodine-deficient areas (IIDAs), iodine-adequate areas (IAAs), iodine-excess areas (IEAs), and iodine extra-high areas (IEHAs). SIC, spot urine iodine concentration (SUIC), and daily total iodine intake (TII) were assessed, and ultrasonography was performed in all subjects.
There was a positive correlation between SIC and SUIC (r = 0.67; 95% CI: 0.64, 0.69; P < 0.001), and TII (r = 0.47; 95% CI: 0.43, 0.50; P < 0.001). The prevalence of thyroid nodules (TN) showed an upward trend with SIC increasing (Z = −2.83; P-trend = 0.005). The area under the receiver-operating characteristic (ROC) curve for SIC to assess iodine deficiency was 0.62 (95% CI: 0.60, 0.65; P < 0.001) and 0.75 (95% CI: 0.73, 0.77; P < 0.001) for iodine excess. The cutoff values were as follows: SIC < 93.32 μg/L, iodine deficiency; 93.32–224.60 μg/L, iodine adequacy; and >224.60 μg/L, iodine excess. When SIC > 224.60 μg/L, the odds ratio (OR) for UIC > 300 μg/L, excessive TII, and the prevalence of TN were 6.44, 3.68, and 1.27 (95% CI: 4.98, 8.31; 2.83, 4.79; and 1.02, 1.56, respectively; P < 0.05); when SIC < 93.32 μg/L, the OR for UIC < 100 μg/L and insufficient TII were 2.34 and 1.94 (95% CI: 1.73, 3.14 and 1.33, 2.83, respectively; P < 0.05).
Using SIC as a biomarker, females in CIDA exhibited mild iodine deficiency, those in IIDA and IAA demonstrated moderate iodine deficiency, and those in IEA and IEHA exhibited an excess of iodine, consistent with SUIC to assess iodine status. SIC can be used as a good biomarker to evaluate the iodine status in population.
As civil engineering structures are growing in dimension and longevity, there is an associated increase in concern regarding the maintenance of such structures. Bridges, in particular, are critical ...links in today’s transportation networks and hence fundamental for the development of society. In this context, the demand for novel damage detection techniques and reliable structural health monitoring systems is currently high. This paper presents a model-free damage detection approach based on machine learning techniques. The method is applied to data on the structural condition of a fictitious railway bridge gathered in a numerical experiment using a three-dimensional finite element model. Data are collected from the dynamic response of the structure, which is simulated in the course of the passage of a train, considering the bridge in healthy and two different damaged scenarios. In the first stage of the proposed method, artificial neural networks are trained with an unsupervised learning approach with input data composed of accelerations gathered on the healthy bridge. Based on the acceleration values at previous instants in time, the networks are able to predict future accelerations. In the second stage, the prediction errors of each network are statistically characterized by a Gaussian process that supports the choice of a damage detection threshold. Subsequent to this, by comparing damage indices with said threshold, it is possible to discriminate between different structural conditions, namely between healthy and damaged. From here and for each damage case scenario, receiver operating characteristic curves that illustrate the trade-off between true and false positives can be obtained. Lastly, based on the Bayes’ Theorem, a simplified method for the calculation of the expected total cost of the proposed strategy, as a function of the chosen threshold, is suggested.
To predict the optimal cut-off values for screening and predicting metabolic syndrome(MetS) in a middle-aged and elderly Chinese population using 13 obesity and lipid-related indicators, and to ...identify the most suitable predictors.
The data for this cross-sectional investigation came from the China Health and Retirement Longitudinal Study (CHARLS), including 9457 middle-aged and elderly people aged 45-98 years old. We examined 13 indicators, including waist circumference (WC), body mass index (BMI), waist-height ratio (WHtR), visceral adiposity index (VAI), a body shape index (ABSI), body roundness index (BRI), lipid accumulation product index (LAP), conicity index (CI), Chinese visceral adiposity index (CVAI), triglyceride-glucose index (TyG-index) and their combined indices (TyG-BMI, TyG-WC, TyG-WHtR). The receiver operating characteristic curve (ROC) was used to determine the usefulness of indicators for screening for MetS in the elderly and to determine their cut-off values, sensitivity, specificity, and area under the curve (AUC). Association analysis of 13 obesity-related indicators with MetS was performed using binary logistic regression analysis.
A total of 9457 middle-aged and elderly Chinese were included in this study, and the overall prevalence of the study population was 41.87% according to the diagnostic criteria of NCEP ATP III. According to age and gender, the percentage of males diagnosed with MetS was 30.67% (45-54 years old: 30.95%, 55-64 years old: 41.02%, 65-74 years old: 21.19%, ≥ 75 years old: 6.84%). The percentage of females diagnosed with MetS was 51.38% (45-54 years old: 31.95%, 55-64 years old: 39.52%, 65-74 years old: 20.43%, ≥ 75 years old: 8.10%). The predictive power of Tyg-related parameters was more prominent in both sexes. In addition, LAP and CVAI are also good at predicting MetS. ABSI had a poor prediction ability.
Among the middle-aged and elderly population in China, after adjusting for confounding factors, all the indicators except ABSI had good predictive power. The predictive power of Tyg-related parameters was more prominent in both sexes. In addition, LAP and CVAI are also good at predicting MetS.
Inspired by the powerful feature extraction and the data reconstruction ability of autoencoder, a stacked sparse denoising autoencoder is developed for electricity theft detection in this paper. The ...technical route is to employ the electricity data from honest users as the training samples, and the autoencoder can learn the effective features from the data and then reconstruct the inputs as much as possible. For the anomalous behavior, since it contributes little to the autoencoder, the detector returns to a comparatively higher reconstruction error; hence the theft users can be recognized by setting an appropriate error threshold. To improve the feature extraction ability and the robustness, the sparsity and noise are introduced into the autoencoder, and the particle swarm optimization algorithm is applied to optimize these hyper-parameters. Moreover, the receiver operating characteristic curve is put forward to estimate the optimal error threshold. Finally, the proposed approach is evaluated and verified using the electricity dataset in Fujian, China.
Purpose
The Hospital Anxiety and Depression Scale (HADS) is a self-report questionnaire designed to screen anxious and depressive states in patients in non-psychiatric settings. In spite of its large ...use, no agreement exists in literature on HADS accuracy in case finding. The present research addresses the issue of HADS accuracy in cancer patients, comparing its two subscales (HADS-A and HADS-D) against tools not in use in psychiatry, which are able to detect prolonged negative emotional states.
Methods
2121 consecutive adult cancer inpatients were administered the HADS together with the State Anxiety subscale of State-Trait Anxiety Inventory and the Center for Epidemiologic Studies Scale on Depression. Receiver operating characteristic (ROC) curves were computed to identify a cut-off for anxious and depressive states in cancer patients. All indicators were computed together with their corresponding 95% confidence interval (95% CI).
Results
Data of 1628 and 1035 participants were used to assess the accuracy in case finding of HADS-A and HADS-D, respectively. According to the ROC analysis, the optimal cut-off was > 9 units for the HADS-A and > 7 units for the HADS-D. The area under the ROC curve was 0.90 for HADS-A (95% CI 0.88–0.91) and 0.84 for HADS-D (95% CI 0.81–0.86).
Conclusions
This study suggested that risk scores of anxious and depressive states above specific HADS cut-offs are useful in identifying anxious and depressive states in cancer patients, and they may thus be applicable in clinical practice.
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