Receiver Operating Characteristic Method Research Articles

Identifying regional hotspots of heatwaves, droughts, floods, and their co-occurrences

AbstractIn this paper we present a framework to aid in the selection of optimal environmental indicators for detecting and mapping extreme events and analyzing trends in heatwaves, meteorological and hydrological droughts, floods, and their compound occurrence. The framework uses temperature, precipitation, river discharge, and derived climate indices to characterize the spatial distribution of hazard intensity, frequency, duration, co-occurrence, and dependence. The relevant climate indices applied are Standardized Precipitation Index, Standardized Precipitation and Evapotranspiration Index (SPEI), Standardized Streamflow Index, heatwave indices based on fixed (HWI$$_\textrm{S}$$ S ) and anomalous temperatures (HWI$$_\textrm{E}$$ E ), and Daily Flood Index (DFI). We selected suitable environmental indicators and corresponding thresholds for each hazard based on estimated extreme event detection performance using receiver operating characteristics (ROC), area under curve (AUC), and accuracy, which is defined as the proportion of correct detections. We assessed compound hazard dependence using a Likelihood Multiplication Factor (LMF). We tested the framework for the case of Sweden, using daily data for the period 1922–2021. The ROC results showed that HWI$$_\textrm{S}$$ S , SPEI12 and DFI are suitable indices for representing heatwaves, droughts, and floods, respectively (AUC > 0.83). Application of these indices revealed increasing heatwave and flood occurrence in large areas of Sweden, but no significant change trend for droughts. Hotspots with LMF > 1, mostly concentrated in Northern Sweden from June to August, indicated that compound drought-heatwave and drought-flood events are positively correlated in those areas, which can exacerbate their impacts. The novel framework presented here adds to existing hydroclimatic hazard research by (1) using local data and historical records of extremes to validate indicator-based hazard hotspots, (2) evaluating compound hazards at regional scale, (3) being transferable and streamlined, (4) attaining satisfactory performance for indicator-based hazard detection as demonstrated by the ROC method, and (5) being generalizable to various hazard types.

Erosion Prediction and Soil and Water Conservation Scenario Using The SWAT Model

Increasing population and development has resulted in inappropriate land use within watersheds that increase soil erosion. One of the hydrologic models that can be used to predict erosion and soil and water conservation in a watershed is SWAT. This research was conducted to predict erosion and the guidance of soil and water conservation in the Yeh Empas Watershed. This research was carried out in several stages, including data collection, data analysis and map validation using the ROC (receiver operating characteristic) and AUC (area under curve) methods. Results revealed that Yeh Empas Watershed had a very light EHL (erosion hazard level) category was found in the downstream area with an annual erosion value of 0 – 14.68 t/ha. In the middle area, the EHL was categorized as light with an erosion value of 15.44 – 57.22 t.ha-1.y-1. The upstream area has moderate EHL with a value of 65.20 – 178.64 t.ha-1.y-1. The validation of the EHL map using the ROC method showed a result of 0.83. This value was classified as very good. It can be concluded that SWAT predictions can be used in estimating erosion and soil and water conservation scenario to reduce 85% erosion. Keywords: Erosion prediction, Soil and water conservation scenario, SWAT models, Yeh Empas Watershed.

A Step-by-Step Guide to Selecting an Optimal Cut-Off Value Based on the Receiver Operating Characteristic and Youden Index in Methods Designed to Diagnose Lyme Disease.

Detection tools designed to diagnose complex diseases such as Lyme Borreliosis require an optimal cutoff point to distinguish the healthy from the diseased. The chapter will provide a practical guide to selecting an optimal cutoff mark by creating the receiver operating characteristic (ROC) in Microsoft Excel. To guide the creation of a ROC graphical plot, we will use example data from an enzyme-linked immunosorbent assay (ELISA) measuring anti-human immunoglobulin G (IgG) against whole-cell Borrelia lysates. Herein, the ROC method will demonstrate that an optical density (OD) value from ELISA with the highest Youden Index (J) is an optimal cutoff value to differentiate positive and negative IgG immune responses in human serum samples.

The minimal important difference for the Postural Assessment Scale for Stroke Patients in the subacute stage

BackgroundThe minimal important difference (MID) of the Postural Assessment Scale for Stroke Patients (PASS) remains unknown, limiting the interpretation of change scores. ObjectivesTo estimate the MID of the PASS in patients with subacute stroke. MethodsData at admission and discharge for 240 participants were retrieved from a longitudinal study. The “mobility” item of the Barthel Index was used as the anchor for indicating the improvement of posture control. Receiver operating characteristic (ROC) method was used to estimate the anchor-based MID of the PASS. ResultsThe ROC method identified a MID of 3.0 points, with a sensitivity of 81.0 % and a specificity of 75.6 %. ConclusionThe MID of the PASS was 3.0 points, indicating that if a patient achieves an improvement of 3.0 or more points on the PASS, they have a clinically important improvement in posture control. Our results can help in interpreting change scores and aid in understanding the clinical values of treatment outcomes.

Minimal clinically important difference of the Michigan Hand Outcomes Questionnaire score and the pain visual analogue scale in conservative treatment of trigger finger.

We investigated the minimal clinically important difference (MCID) of the Michigan Hand Outcomes Questionnaire (MHQ) and the pain visual analogue scale (VAS-pain) after conservative treatment of trigger finger. This secondary analysis of a randomized controlled trial compared pain reduction, symptoms and functional improvement at 12 weeks. Patients included were at least 18 years old and able to complete MHQ and VAS-pain at enrolment and 12 weeks after treatment. The MCIDs of MHQ and VAS-pain were evaluated using a distribution-based, anchor-based and receiver operating characteristic (ROC) curve-based approach. Of the 117 patients, the MCIDs of MHQ and VAS-pain using a distribution-based approach were 5.3 and 0.6, respectively; applying ROC method were 23.5 and 2.5, respectively; and using anchor questions were 15 and 2, respectively. These MCID values by anchor-based method with a minimal difference of 15 for MHQ and 2 for VAS-pain are recommended as primary evidence to determine clinically significant improvement after conservative treatment of trigger finger.Level of evidence: I.

Estimating cutpoints of gait speed and sit-to-stand test values for self-reported mobility limitations in a cohort of community-dwelling older adults from Singapore: comparing receiver operating characteristic (ROC) analysis with adjusted predictive modelling

ObjectivesClinical interpretability of the gait speed and 5-times sit-to-stand (5-STS) tests is commonly established by comparing older adults with and without self-reported mobility limitations (SRML) on gait speed and 5-STS performance, and estimating clinical cutpoints for SRML using the receiver operating characteristics (ROC) method. Accumulating evidence, however, suggests that the adjusted predictive modeling (APM) method may be more appropriate to estimate these interpretational cutpoints. Thus, we aimed to compare, in community-dwelling older adults, gait speed and 5-STS cutpoints estimated using the ROC and APM methods. DesignCross-sectional study. Setting and ParticipantsThis study analyzed data from 955 community-dwelling independently walking older adults (73%women) aged ≥60 years (mean, 68; range, 60–88). MethodsParticipants completed the 10-metre gait speed and 5-STS tests. Participants were classified as having SRML if they responded "Yes" to either of the 2 questions regarding walking and stair climbing difficulty. Cutpoints for SRML and its component questions were estimated using ROC analysis with Youden criterion and the APM method. ResultsThe proportions of participants with self-reported walking difficulty, self-reported stair climbing difficulty, and SRML were 10%, 19%, and 22%, respectively. Gait speed and 5-STS time were moderately correlated with each other (r=-0.56) and with the self-reported measures (absolute r-values, 0.39–0.44). ROC-based gait speed cutpoints were 0.14 to 0.16 m/s greater than APM-based cutpoints (P < 0.05) whilst ROC-based 5-STS time cutpoints were 0.8 to 3.3 s lower than APM-based cutpoints (P < 0.05 for walking difficulty). Compared with ROC-based cutpoints, APM-based cutptoints were more precise and they varied monotonically with self-reported walking difficulty, self-reported stair climbing difficulty, and SRML. Conclusions and implicationsIn a sample of 955 older adults, our findings of precise and biologically plausible gait speed and 5-STS cutpoints for SRML estimated using the APM method indicate that this promising method could potentially complement or even replace traditional ROC methods.

High-Resolution Lidar-Derived DEM for Landslide Susceptibility Assessment Using AHP and Fuzzy Logic in Serdang, Malaysia

Landslide impact is potentially hazardous to an urban environment. Landslides occur at certain slope levels over time and require practical slope analysis to assess the nature of the slope where a landslide is likely to occur. Thus, acquiring very high-resolution remote sensing data plays a significant role in determining the slope surface. For this study, 12 landslide conditioning parameters with 10 × 10 cell sizes that have never been previously collectively applied were created. These factors were created directly from the LiDAR (Light Detection and Ranging) DEM (digital elevation model)using their layer toolboxes, which include slope, aspect, elevation, curvature, and hill shade. Stream power index (SPI), topographic wetness index (TWI), and terrain roughness index (TRI) were created from spatial layers such as slope, flow direction, and flow accumulation. Shapefiles of distances to roads, lakes, trees, and build-up were digitized as land use/cover from the LiDAR image and produced using the Euclidean distance method in ArcGIS. The parameters were selected based on expert knowledge, previous landslide literature, and the study area characteristics. Moreover, multicriteria decision-making analysis, which includes the analytic hierarchy process (AHP) and fuzzy logic approaches not previously utilized with a LiDAR DEM, was used in this study to predict the possibility of a landslide. The receiver operating characteristics (ROC) were used for the validation of results. The area under the curve (AUC) values obtained from the ROC method for the AHP and fuzzy were 0.859 and 0.802, respectively. The final susceptibility results will be helpful to urban developers in Malaysia and for sustainable landslide hazard mitigation.

Time-Dependent Receiver Operating Characteristic Analysis and Applications in the Field of Medicine

When there is a time-dependency between the biomarker and the event of interest (death, disease, relapse etc.), classical receiver operating characteristic (ROC) analysis may not be able to estimate the true performance of the biomarker. For such cases, time-dependent ROC, an extended version of the standard ROC, is developed. In this study, the aim is to demonstrate applications of this modified ROC method on medical datasets and find out if it should be preferred over classical ROC for time-dependent situations. Comparison between classical ROC and Kaplan-Meier (KM) estimator, which is one of the two time-dependent ROC estimators, has been made using datasets in this study. Nearest Neighbor Estimator (NNE), the alternative of KM estimator, is also applied on all datasets. Then the findings of these two approaches are compared. It is concluded that time-dependent ROC method is superior to the standard ROC analysis. In general, the closer to the event time, the higher performance is observed. Especially, biomarkers measured at last 12 or 6 months before the event are determined to be better at classification than the earlier measurements. Though in all applications KM and NNE yielded very similar results, the latter is considered to be more appropriate to evaluate the performance of a biomarker when a time dependent data is also censored. Results of this study show that time-dependent ROC analysis performs more accurately when there is a time dependency between the biomarker and the event of interest; therefore, it is recommended.

Tube-Iris Distance and Corneal Endothelial Cell Damage Following Ahmed Glaucoma Valve Implantation.

The most significant factor for endothelial cell loss should be readily identified, since prevention is the most crucial treatment. Here, we investigate risk factors for corneal endothelial cell density (ECD) decline following Ahmed glaucoma valve (AGV) implantation and determine the optimal cut-off values. This study included 103 eyes (95 patients) with glaucoma that underwent AGV implantation between January 2006 and January 2021 at a single medical center (Severance Hospital). We conducted consecutive t-tests between two groups separated by the ECD change rate to determine the survival state of the enrolled patients. Associations were evaluated using univariable and multivariable linear regressions. Optimal cut-off values for identified risk factors were analyzed using a Cox proportional hazards model and a receiver operating characteristic (ROC) curve based on logistic regression. Mean follow-up duration was 4.09 ± 2.20 years. After implementing consecutive t-tests, only patients with an ECD change rate greater than −6.1%/year were considered to have survived. Tube–iris distance (TID) was the only statistically significant factor identified in both the univariable and multivariable linear regressions. The cut-off value determined from the consecutive Cox regression method was 0.33 mm (smallest p-value of 0.0087), and the cut-off value determined from the ROC method was 0.371 mm (area under the receiver operating characteristic curve [AUC], 0.662). Patients with short TIDs showed a better ECD prognosis following AGV surgery; we suggest optimal TID cut-off values of 0.33 mm and 0.371 mm based on the implemented Cox regression and ROC methodology, respectively.

P3. How to make ends meet: a risk assessment for pseudarthrosis and cost benefit analysis of BMP-2 in adult spinal deformity surgery

<h3>BACKGROUND CONTEXT</h3> Bone Morphogenetic Protein-2 (BMP-2) has not shown superior benefit in terms of overall cost-effectiveness after implementation during adult spinal deformity (ASD) surgery. However, it remains to be shown whether certain discrete populations do obtain cost-utility from use of BMP-2. <h3>PURPOSE</h3> Generate a score to determine usage of BMP-2 and correlate with rates of pseudarthrosis. <h3>STUDY DESIGN/SETTING</h3> Retrospective cohort study of a single-center ASD database. <h3>PATIENT SAMPLE</h3> A total of 689 ASD patients. <h3>OUTCOME MEASURES</h3> Cost utility, pseudarthrosis. <h3>METHODS</h3> ASD patients with BL and 2-year(2Y) data included. BMP-2 kit size and cost: small - 4.2 mg ($21,800), medium - 8.4 mg ($23,667), large - 12 mg ($25,617). Published methods converted ODI to SF-6D. QALYs utilized a 3% discount rate for residual decline. Cost was calculated using the PearlDiver database and assessed for Complications and Comorbidities and Major Complications and Comorbidities according to CMS.gov definitions. Binary logistic regression analyses determined significant predictors for development of pseudarthrosis. Established weights were generated for predictive variables via back-step logistic regression for a risk score to predict development of pseudarthrosis. Risk score was then validated via Receiver Operating Characteristic (ROC) curve method analysis. Categories via conditional inference tree (CIT) analysis-derived thresholds were tested for cost-utility of BMP-2 usage. Marginalized means for utility gained and Cost per QALY were calculated within each risk score category, controlling for age, history of prior fusion, and baseline deformity and disability. <h3>RESULTS</h3> Included: 387 ASD patients. Of 387, 64% received BMP-2 (1% small, 4% medium, 59% large). There were 17 (4.4%) of patients that developed pseudarthrosis by two years, 9 (2.3%) of which underwent reoperation. BMP-2 use, regardless of kit size, did not significantly lower pseudarthrosis rates overall (OR: 0.4, [0.2-1.04]). A predictive risk score for development of pseudarthrosis was formed by the following preoperative variables: age, frailty, history of diabetes, osteoporosis, depression, ASA grade, and baseline L4-S1 and T1PA. Via ROC method, this predictive risk score generated an AUC of 0.87. Following CIT machine learning, thresholds for the BMP Risk Score were derived: >5 No Risk (NoR), 3-5 Low Risk (LowR), 2-3 Moderate Risk (ModR), and <2 High Risk (HighR). The rates of pseudarthrosis for each category were: NoR - 0%; LowR - 1.6%; ModR - 9.3%; HighR - 24.3%. When assessing BMP-2 use and its cost-utility within each group, patients receiving BMP-2 had similar QALYs to those that did not receive BMP-2 (0.163 vs 0.171, p=.65). BMP-2 usage had significantly worse cost-utility in both NoR and LowR cohorts (both p<.05). In ModR patients, BMP-2 usage had equivocal cost-utility ($680,532.35 vs $580,380.21, p=.14). In the HighR cohort, the cost-utility difference narrowed even further (BMP-2 use: $743,155.21 vs $719,628.79, p=.82). <h3>CONCLUSIONS</h3> Our study shows BMP-2 has equivocal cost-utility within those at moderate and high risk for developing pseudarthrosis within two years following spinal deformity correction. The generated predictive score can better aid spine surgeons assess risk and enhance justification for the use of BMP-2 during surgical intervention for adult spinal deformity. <h3>FDA DEVICE/DRUG STATUS</h3> This abstract does not discuss or include any applicable devices or drugs.

Ensemble hybrid machine learning methods for gully erosion susceptibility mapping: K-fold cross validation approach

Gully erosion is one of the important problems creating barrier to agricultural development. The present research used the radial basis function neural network (RBFnn) and its ensemble with random sub-space (RSS) and rotation forest (RTF) ensemble Meta classifiers for the spatial mapping of gully erosion susceptibility (GES) in Hinglo river basin. 120 gullies were marked and grouped into four-fold. A total of 23 factors including topographical, hydrological, lithological, and soil physio-chemical properties were effectively used. GES maps were built by RBFnn, RSS-RBFnn, and RTF-RBFnn models. The very high susceptibility zone of RBFnn, RTF-RBFnn and RSS-RBFnn models covered 6.75%, 6.72% and 6.57% in Fold-1, 6.21%, 6.10% and 6.09% in Fold-2, 6.26%, 6.13% and 6.05% in Fold-3 and 7%, 6.975% and 6.42% in Fold-4 of the basin. Receiver operating characteristics (ROC) curve and statistical techniques such as mean-absolute-error (MAE), root-mean-absolute-error (RMSE) and relative gully density area (R-index) methods were used for evaluating the GES maps. The results of the ROC, MAE, RMSE and R-index methods showed that the models of susceptibility to gully erosion have excellent predictive efficiency. The simulation results based on machine learning are satisfactory and outstanding and could be used to forecast the areas vulnerable to gully erosion.

Application of ROC Curve Analysis for Predicting Students’ Passing Grade in a Course Based on Prerequisite Grades

Determining prerequisite requirements is vital for successful curriculum development and student on-schedule completion of the course of study. This study adapts the Receiver Operating Characteristic (ROC) curve analysis to determine a threshold grade in a prerequisite course necessary for passing the next course in a sequence. This method was tested on a dataset of Calculus 1 and Calculus 2 grades of 164 undergraduate students majoring in mathematics at a private university in Kazakhstan. The results showed that while the currently used practice of setting prerequisite grade requirements is accurately identifying successful completions of Calculus 2, the ROC method is more accurate in identifying possible failures in Calculus 2. The findings also indicate that prior completion of Calculus 1 is positively associated with success in a Calculus 2 course. Thus, this study contributes to the field of mathematics education by providing a new data-driven methodology for determining the optimal threshold grade for mathematics prerequisite courses.

Serum LDH: a potential surrogate to chest radiograph in pediatric Covid-19 patients to reduce radiation exposure

BackgroundChest radiographs are frequently used to evaluate pediatric patients with COVID-19 infection during the current pandemic. Despite the minimal radiation dose associated with chest radiography, children are far more sensitive to ionizing radiation's carcinogenic effects than adults. This study aimed to examine whether serum biochemical markers could be potentially used as a surrogate for imaging findings to reduce radiation exposure.MethodsThe retrospective posthoc analysis of 187 pediatric patients who underwent initial chest radiographs and serum biochemical parameters on the first day of emergency department admission. The cohort was separated into two groups according to whether or not the initial chest radiograph revealed evidence of pneumonia. Spearman's rank correlation was used to connect serum biochemical markers with observations on chest radiographs. The Student's t-test was employed for normally distributed data, and for non-normally distributed data, the Mann–Whitney U test was used. A simple binary logistic regression was used to determine the importance of LDH in predicting chest radiographs. The discriminating ability of LDH in predicting chest radiographs was determined using receiver operating characteristics (ROC) analysis. The cut-off value was determined using Youden's test. Interobserver agreement was quantified using the Cohen k coefficient.Results187 chest radiographs from 187 individual pediatric patients (95 boys and 92 girls; mean age ± SD, 10.1 ± 6.0 years; range, nine months–18 years) were evaluated. The first group has 103 patients who did not have pneumonia on chest radiographs, while the second group contains 84 patients who had evidence of pneumonia on chest radiographs. GGO, GGO with consolidation, consolidation, and peri-bronchial thickening were deemed radiographic evidence of pneumonia in group 2 patients. Individuals in group 2 with radiological indications of pneumonia had significantly higher LDH levels (p = 0.001) than patients in group 1. The Spearman's rank correlation coefficient between LDH and chest radiography score is 0.425, showing a significant link. With a p-value of < 0.001, the simple binary logistic regression analysis result validated the relevance of LDH in predicting chest radiography. An abnormal chest radiograph was related to LDH > 200.50 U/L (AUC = 0.75), according to the ROC method. Interobserver agreement between the two reviewers was almost perfect for chest radiography results in both groups (k = 0.96, p = 0.001).ConclusionThis study results show that, compared to other biochemical indicators, LDH has an 80.6% sensitivity and a 62% specificity for predicting abnormal chest radiographs in a pediatric patient with confirmed COVID-19 infection. It also emphasizes that biochemical measures, rather than chest radiological imaging, can detect the pathogenic response to COVID-19 infection in the chest earlier. As a result, we hypothesized LDH levels might be potentially used instead of chest radiography in children with COVID-19, reducing radiation exposure.

FullROC: An R package for generating and analyzing eyewitness-lineup ROC curves.

A police lineup is a procedure in which a suspect is surrounded by known-innocent persons (fillers) and presented to the witness for an identification attempt. The purpose of a lineup is to test the investigator's hypothesis that the suspect is the culprit, and the investigator uses the witness' identification decision and the associated confidence level to inform this hypothesis. Whereas suspect identifications provide evidence of guilt, filler identifications and rejections provide evidence of innocence. Despite the capacity of lineups to provide exculpatory information, past research has focused, almost exclusively, on inculpatory behaviors. We recently developed a method for incorporating all lineup outcomes in a single receiver operator characteristic (ROC) curve. The area under the full lineup ROC curve reflects the total capacity of a lineup procedure to discriminate guilty suspects from innocent suspects. Here, we introduce a Comprehensive R Archive Network (CRAN) package, fullROC, to support eyewitness researchers in using the full ROC approach to analyze lineup data. The fullROC package provides functions for adjusting identification rates, generating full ROC curves for lineup data, computing the area under the ROC curves (AUC), and statistically comparing the AUCs of different lineups. Using both simulated and empirical data, we illustrate the functionality of the fullROC CRAN package. In brief, the fullROC package provides a useful tool for eyewitness researchers to analyze lineup data using the full ROC method, which incorporates both the inculpatory and exculpatory information of eyewitness behaviors.

Sensitivity of Three Patient-Specific Quality Assurance Systems to MLC Aperture Errors With Volumetric Modulated Arc Therapy.

Purpose: To compare the sensitivity of ArcCHECK (AC), portal dosimetry (PD), and an in-house logfile-based system (LF) to multileaf collimators (MLC) aperture errors and the ability to identify these errors. Methods and Materials: For 12 retrospective original head and neck volumetric modulated arc therapy (VMAT) plans, MLC aperture errors of ± 0.4mm, ± 1.2mm, ± 2mm, and ± 3mm were introduced for each plan, resulting in 96 plans with errors. AC, PD, and LF were used for the gamma evaluation at 3%/3mm, 3%/2mm, and 2%/2mm criteria. Gradient analysis was used to evaluate the sensitivity to MLC aperture errors. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve was used to evaluate the ability to identify MLC aperture errors and dose errors, and the optimal cut-off value to identify the error was obtained. Results: The gamma pass rate (%GP) of LF had the smallest descent gradient as the MLC error increases in any case. The descent gradient of PD was larger than AC, except for the case at the 2%/2mm criteria. For the 3%/3mm criteria, the MLC aperture errors that can be perfectly identified by AC, PD, and LF were ± 3mm, ± 2mm, and ± 1.2mm, respectively, and the average percent dose error (%DEs) of dose metrics in targets that can be perfectly identified were 4% to 5%, 3% to 4%, and 2% to 3%, respectively. For the 3%/2mm criteria, the errors that AC, PD, and LF can perfectly identify were the same as the 3%/3mm criteria. For the 2%/2mm criteria, AC can perfectly identify the MLC error of ± 2mm and the %DE of 3% to 4%. PD and LF can identify the MLC error of ± 1.2mm and the %DE of 2% to 3%. Conclusion: Different patient-specific quality assurance (PSQA) systems have different sensitivity and recognition abilities to MLC aperture errors. Institutions should formulate their own customized %GP limits based on their PSQA process through ROC or other methods.

Rapid Detection of Coronavirus (COVID-19) Using Microwave Immunosensor Cavity Resonator.

This paper presents a rapid diagnostic device for the detection of the pandemic coronavirus (COVID-19) using a micro-immunosensor cavity resonator. Coronavirus has been declared an international public health crisis, so it is important to design quick diagnostic methods for the detection of infected cases, especially in rural areas, to limit the spread of the virus. Herein, a proof-of-concept is presented for a portable laboratory device for the detection of the SARS-CoV-2 virus using electromagnetic biosensors. This device is a microwave cavity resonator (MCR) composed of a sensor operating at industrial, scientific and medical (ISM) 2.45 GHz inserted in 3D housing. The changes of electrical properties of measured serum samples after passing the sensor surface are presented. The three change parameters of the sensor are resonating frequency value, amplitude and phase of the reflection coefficient |S11|. This immune-sensor offers a portable, rapid and accurate diagnostic method for the SARS-CoV-2 virus, which can enable on-site diagnosis of infection. Medical validation for the device is performed through biostatistical analysis using the ROC (Receiver Operating Characteristic) method. The predictive accuracy of the device is 63.3% and 60.6% for reflection and phase, respectively. The device has advantages of low cost, low size and weight and rapid response. It does need a trained technician to operate it since a software program operates automatically. The device can be used at ports’ quarantine units, hospitals, etc.

A Way to Increase the Sensitivity and Specificity of the Hamilton Depression and Anxiety Scales.

The Hamilton Depression Rating Scale (HDRS-17) and the Hamilton Anxiety Rating Scale (HARS-14) have been acknowledged as gold standards in evaluating the severity of depression and anxiety. The specificity and sensitivity of these scales in predicting somatic complaints of depression and anxiety are issues in both clinical and research areas. The present study proposes a new model to enhance the sensitivity and specificity of HDRS-17 and HARS-14 for predicting symptoms of insomnia, inappetence, and loss of libido in psychiatric patients. This study included 1507 patients diagnosed withbipolar disorder, depression, panic disorder, obsessive-compulsive disorder, and generalized anxiety disorder. The HDRS-17 and the HARS-14 were utilized as predictive scales for the prediction of patients' sleep, appetite, and libido. The sensitivity and specificity were computed using the receiver operating characteristic (ROC). Logistic regression was performed to enhance the predictive values. The predictive value of the logistic regression model was not satisfactory, and a conversion table was therefore designed for each symptom-diagnosis subgroup. The new joint ROC model was then used to recalculate the sensitivity and specificity of the 2 scales for each symptom-diagnosis subgroup. The outcome is a prediction table, presented for use by clinicians. It was observed that the new statistical model, the joint ROC, increased the sensitivity and specificity of the HDRS-17 and the HARS-14. : Based on the results of the evaluations with the HDRS and the HARS, the joint ROC method was developed to better predict the presence of symptoms.

Evaluation of the Effect of Hydroseeded Vegetation for Slope Reinforcement

A landslide is a significant environmental hazard that results in an enormous loss of lives and properties. Studies have revealed that rainfall, soil characteristics, and human errors, such as deforestation, are the leading causes of landslides, reducing soil water infiltration and increasing the water runoff of a slope. This paper introduces vegetation establishment as a low-cost, practical measure for slope reinforcement through the ground cover and the root of the vegetation. This study reveals the level of complexity of the terrain with regards to the evaluation of high and low stability areas and has produced a landslide susceptibility map. For this purpose, 12 conditioning factors, namely slope, aspect, elevation, curvature, hill shade, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distances to roads, distance to lakes, distance to trees, and build-up, were used through the analytic hierarchy process (AHP) model to produce landslide susceptibility map. Receiver operating characteristics (ROC) was used for validation of the results. The area under the curve (AUC) values obtained from the ROC method for the AHP model was 0.865. Four seed samples, namely ryegrass, rye corn, signal grass, and couch, were hydroseeded to determine the vegetation root and ground cover’s effectiveness on stabilization and reinforcement on a high-risk susceptible 65° slope between August and December 2020. The observed monthly vegetation root of couch grass gave the most acceptable result. With a spreading and creeping vegetation ground cover characteristic, ryegrass showed the most acceptable monthly result for vegetation ground cover effectiveness. The findings suggest that the selection of couch species over other species is justified based on landslide control benefits.

Minimal Clinically Important Differences for Patient-Reported Outcomes After TKA Depend on Central Sensitization.

To our knowledge, it is still unknown if central sensitization (CS) influences the magnitude of the minimal clinically important difference (MCID) for patient-reported outcome measures after total knee arthroplasty (TKA). The purpose of this study was to determine the influence of CS on the MCID for the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score in patients who underwent TKA for knee osteoarthritis. A total of 422 patients who underwent unilateral TKA and completed a 2-year follow-up were enrolled in this study. CS was measured using the Central Sensitization Inventory (CSI). The WOMAC score was used to evaluate preoperative and postoperative patient-reported outcomes. The measurement of the MCID was performed separately for patients with and without CS using both the anchor-based method and the distribution method. The change difference method defined the MCID as the difference in preoperative-to-postoperative change between the minimal-improvement group and the no-change group. In addition, the MCID was calculated using receiver operating characteristic (ROC) curve analysis. The percentage of MCID achievement in each group was also compared. According to the change difference method, the MCID for the WOMAC total score was 23.4 points for patients with CS and 14.7 points for patients without CS. The MCID using the ROC cutoff value for the WOMAC total score was 29.5 points for the patients with CS and 26.5 points for the patients without CS. MCID achievement rates in WOMAC pain, function, and total scores were all found to be significantly higher in the patients without CS through the change difference method and the ROC method (all p < 0.05). The MCID for the WOMAC score of patients with CS after TKA was greater than that for patients without CS. Furthermore, by applying the calculated MCID to the group to which the patients belonged (with or without CS), we determined that patients with CS showed a lower MCID achievement rate than patients without CS. Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Concordance measures and time-dependent ROC methods

The receiver operating characteristic (ROC) curve displays sensitivity versus 1-specificity over a set of thresholds. The area under the ROC curve (AUC) is a global scalar summary of this curve. In the context of time-dependent ROC methods, we are interested in global scalar measures that summarize sequences of time-dependent AUCs over time. The concordance probability is a candidate for such purposes. The concordance probability can provide a global assessment of the discrimination ability of a test for an event that occurs at random times and may be right censored. If the test adequately differentiates between subjects who survive longer times and those who survive shorter times, this will assist clinical decisions. In this context, the concordance probability may support the assessment of precision medicine tools based on prognostic biomarkers models for overall survival. Definitions of time-dependent sensitivity and specificity are reviewed. Some connections between such definitions and concordance measures are also reviewed and we establish new connections via new measures of global concordance. We explore the relationship between such measures and their corresponding time-dependent AUC. To illustrate these concepts, an application in the context of Alzheimer's disease is presented.