Statistical methodology: III. Receiver operating characteristic (ROC) curves

A Polygenic Risk Score for Predicting Racial and Genetic Susceptibility to Prurigo Nodularis

Prurigo nodularis (PN) is an understudied inflammatory skin disease characterized by pruritic, hyperkeratotic nodules. Identifying the genetic factors underlying PN could help to better understand its etiology and guide the development of therapies. In this study, we developed a polygenic risk score that predicts a diagnosis of PN (OR = 1.41, P = 1.6 × 10-5) in two independent and continentally distinct populations. We also performed GWASs, which uncovered genetic variants associated with PN, including one near PLCB4 (rs6039266: OR = 3.15, P = 4.8 × 10-8) and others near TXNRD1 (rs34217906: OR = 1.71, P = 6.4 × 10-7; rs7134193: OR = 1.57, P = 1.1 × 10-6). Finally, we discovered that Black patients have over a two-times greater genetic risk of developing PN (OR = 2.63, P = 7.8 × 10-4). Combining the polygenic risk score and self-reported race together was significantly predictive of PN (OR = 1.32, P = 4.7 × 10-3). Strikingly, this association was more significant with race than after adjusting for genetic ancestry. Because race is a sociocultural construct and not a genetically bound category, our findings suggest that genetics, environmental influence, and social determinants of health likely affect the development of PN and may contribute to clinically observed racial disparities.

Identification of Escitalopram Metabolic Ratios as Potential Biomarkers for Predicting CYP2C19 Poor Metabolizers

BACKGROUND

Escitalopram is metabolized by CYP2C19 to N-desmethyl escitalopram and escitalopram propionic acid. The primary aims of this study were to investigate the impact of the CYP2C19 phenotype on metabolic ratios of escitalopram in vivo and propose a biomarker for the CYP2C19 phenotype in patients treated with escitalopram.

METHODS

Median steady-state serum metabolite/parent drug ratio of N-desmethyl escitalopram and escitalopram propionic acid was investigated across CYP2C19 genotype-translated phenotype groups. The receiver operator characteristics method and the area-under-the-receiver-operator-characteristics curve was used to determine the best suited metabolic ratio for detecting CYP2C19 poor metabolizers (PMs).

RESULTS

A total of 441 patients were included in the study. The N-desmethyl escitalopram/escitalopram ratio was 67% and 44% lower in CYP2C19 PMs and intermediate metabolizers (IMs), respectively, than normal metabolizers. Furthermore, the ability of the ratio to predict CYP2C19 PMs was 92%. A metabolic ratio of <0.24 was detected in 8 of 8 PMs in the study, indicating that it is a promising biomarker of reduced CYP2C19 activity. The escitalopram propionic acid/escitalopram ratio was 77% and 48% lower in CYP2C19 PMs and IMs, respectively; however, the ability of the ratio to detect CYP2C19 PMs was only 87%.

CONCLUSIONS

These findings suggest that DECT/ECT reflects CYP2C19 activity, and a metabolic ratio of <0.24 strongly predicts CYP2C19 PM phenotype. The ratio could be a valuable alternative to genotyping in personalized dosing of escitalopram and possibly other CYP2C19 substrates. The escitalopram propionic acid/escitalopram ratio was also associated with CYP2C19 activity; however, the ratio was inferior to the DECT/ECT at predicting PMs.

Naive and memory T cells TCR-HLA-binding prediction

T cells recognize antigens through the interaction of their T cell receptor (TCR) with a peptide-major histocompatibility complex (pMHC) molecule. Following thymic-positive selection, TCRs in peripheral naive T cells are expected to bind MHC alleles of the host. Peripheral clonal selection is expected to further increase the frequency of antigen-specific TCRs that bind to the host MHC alleles. To check for a systematic preference for MHC-binding T cells in TCR repertoires, we developed Natural Language Processing-based methods to predict TCR-MHC binding independently of the peptide presented for Class I MHC alleles. We trained a classifier on published TCR-pMHC binding pairs and obtained a high area under curve (AUC) of over 0.90 on the test set. However, when applied to TCR repertoires, the accuracy of the classifier dropped. We thus developed a two-stage prediction model, based on large-scale naive and memory TCR repertoires, denoted TCR HLA-binding predictor (CLAIRE). Since each host carries multiple human leukocyte antigen (HLA) alleles, we first computed whether a TCR on a CD8 T cell binds an MHC from any of the host Class-I HLA alleles. We then performed an iteration, where we predict the binding with the most probable allele from the first round. We show that this classifier is more precise for memory than for naïve cells. Moreover, it can be transferred between datasets. Finally, we developed a CD4-CD8 T cell classifier to apply CLAIRE to unsorted bulk sequencing datasets and showed a high AUC of 0.96 and 0.90 on large datasets. CLAIRE is available through a GitHub at: https://github.com/louzounlab/CLAIRE, and as a server at: https://claire.math.biu.ac.il/Home.

A Weighted Error Distance Metrics (WEDM) for Performance Evaluation on Multiple Change-Point (MCP) Detection in Synthetic Time Series

Change-point detection (CPD) is to find abrupt changes in time-series data. Various computational algorithms have been developed for CPD applications. To compare the different CPD models, many performance metrics have been introduced to evaluate the algorithms. Each of the previous evaluation methods measures the different aspects of the methods. Based on the existing weighted error distance (WED) method on single change-point (CP) detection, a novel WED metrics (WEDM) was proposed to evaluate the overall performance of a CPD model across not only repetitive tests on single CP detection, but also successive tests on multiple change-point (MCP) detection on synthetic time series under the random slide window (RSW) and fixed slide window (FSW) frameworks. In the proposed WEDM method, a concept of normalized error distance was introduced that allows comparisons of the distance between the estimated change-point (eCP) position and the target change point (tCP) in the synthetic time series. In the successive MCPs detection, the proposed WEDM method first divides the original time-series sample into a series of data segments in terms of the assigned tCPs set and then calculates a normalized error distance (NED) value for each segment. Next, our WEDM presents the frequency and WED distribution of the resultant eCPs from all data segments in the normalized positive-error distance (NPED) and the normalized negative-error distance (NNED) intervals in the same coordinates. Last, the mean WED (MWED) and MWTD (1-MWED) were obtained and then dealt with as important performance evaluation indexes. Based on the synthetic datasets in the Matlab platform, repetitive tests on single CP detection were executed by using different CPD models, including ternary search tree (TST), binary search tree (BST), Kolmogorov–Smirnov (KS) tests, t-tests (T), and singular spectrum analysis (SSA) algorithms. Meanwhile, successive tests on MCPs detection were implemented under the fixed slide window (FSW) and random slide window (RSW) frameworks. These CPD models mentioned above were evaluated in terms of our WED metrics, together with supplementary indexes for evaluating the convergence of different CPD models, including rates of hit, miss, error, and computing time, respectively. The experimental results showed the value of this WEDM method.

C-reactive protein accurately predicts severity of acute pancreatitis in children

INTRODUCTION

Predicting severity of acute pancreatitis enables optimization of care, reducing morbidity and length of stay. Modified adult scoring systems have not been able to adequately predict severity in children.

METHODS

This was a retrospective study of children presenting with a first episode of acute pancreatitis from 2002 to 2020 in a single tertiary paediatric surgical centre. Serum markers including CRP at 48 h of admission were analysed. Promising biomarkers underwent ROC (Receiver Operating Curve) analysis, and these were compared to the modified Glasgow Pancreas Score. An AUC (Area Under Curve) > 0.90 was taken as an excellent predictor of severity.

RESULTS

Data of 59 children were analysed, median age 13 years. 22 patients (37%) had a severe episode. ROC analysis demonstrated CRP as the best predictor of severity giving an AUC of 0.92. Optimum cut off value for CRP was 107.5 mg/L (p < 0.0001) producing sensitivity of 91%, specificity of 84%. This was superior to the modified Glasgow Pancreas score, which produced a sensitivity of 36% and specificity of 100%.

CONCLUSION

We have shown that a CRP value of > 108 mg/L within 48 h of admission can be used to predict severity of acute pancreatitis in children with greater accuracy than current scoring systems.

TYPE OF STUDY

Diagnostic test.

LEVEL OF EVIDENCE

Level I.

Performance of Immunoglobulin G Serology on Finger Prick Capillary Dried Blood Spot Samples to Detect a SARS-CoV-2 Antibody Response

We investigate the diagnostic accuracy and predictive value of finger prick capillary dried blood spot (DBS) samples tested by a quantitative multiplex anti-immunoglobulin G (IgG) assay to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies after infection or vaccination. This cross-sectional study involved participants (n = 6,841) from several serological surveys conducted in nonhospitalized children and adults throughout 2020 and 2021 in British Columbia (BC), Canada. Analysis used paired DBS and serum samples from a subset of participants (n = 642) prior to vaccination to establish signal thresholds and calculate diagnostic accuracy by logistic regression. Discrimination of the logistic regression model was assessed by receiver operator curve (ROC) analysis in an n = 2,000 bootstrap of the paired sample (n = 642). The model was cross-validated in a subset of vaccinated persons (n = 90). Unpaired DBS samples (n = 6,723) were used to evaluate anti-IgG signal distributions. In comparison to paired serum, DBS samples from an unvaccinated population possessed a sensitivity of 79% (95% confidence interval [95% CI]: 58 to 91%) and specificity of 97% (95% CI: 95 to 98%). ROC analysis found that DBS samples accurately classify SARS-CoV-2 seroconversion at an 88% percent rate (area under the curve [AUC] = 88% [95% CI: 80 to 95%]). In coronavirus disease 2019 (COVID-19) vaccine dose one or two recipients, the sensitivity of DBS testing increased to 97% (95% CI: 83 to 99%) and 100% (95% CI: 88 to 100%). Modeling found that DBS testing possesses a high positive predictive value (98% [95% CI: 97 to 98%]) in a population with 75% seroprevalence. We demonstrate that DBS testing should be considered to reliably detect SARS-CoV-2 seropositivity from natural infection or vaccination.

IMPORTANCE Dried blood spot samples have comparable diagnostic accuracy to serum collected by venipuncture when tested by an electrochemiluminescent assay for antibodies and should be considered to reliably detect seropositivity following SARS-CoV-2 infection and/or vaccination.

Improvement Predictors in a Workplace Program Promoting Healthy Lifestyle Habits

BACKGROUND

The baseline characteristics of employees to predict improvements in healthy lifestyle habits (LHs) following workplace health promotion programs are underexplored. This study sought to identify predictors of improvements in physical activity (PA), eating habits, sleep habits, and stress management, and health risk factors resulting from healthy LHs.

METHODS

The Activate Your Health program included 3 packages of an increasing number of interventions: light, moderate, and high. Participating employees (n = 506) completed baseline and postintervention questionnaires that collected sociodemographic data, health- and LH-related variables, stress-related variables, and perceptions of general health and life satisfaction. Only those with the potential to improve were included in each improvement outcome analysis.

RESULTS

Being in high and intending to reduce alcohol consumption increased the odds of improving PA. Very good/excellent perceived general health, poor sleep habits, high alcohol consumption, and intending to improve stress management increased the odds of improving stress levels at work. Depression and intending to improve sleep habits increased the odds of improving stress management. Reporting feelings of pleasure increased the odds of improving body mass index.

CONCLUSIONS

Baseline characteristics predicting improvements differed for each LH. A targeted approach may be needed to help employees improve LHs and related outcomes.

A review of meibomian gland structure, function, and contact lens wear

PURPOSE

To provide a balanced literature review of the studies that have evaluated the effect of contact lenses on meibomian gland (MG) health.

METHODS

A PubMed.gov literature search was conducted on or before May 15, 2021. No other time constraints were applied. Search terms included the following: "meibomian gland(s)" plus "contact lens(es)" or "meibography" plus "contact lens(es)". Only full text articles written in English were considered. The reference lists of recovered papers were used to identify articles missed during the primary search. Included articles were required to discuss the impact of contact lenses on MG morphology or function and were graded according to the level of evidence presented.

RESULTS

The literature indicates that contact lenses impact MG function; however, the data are equivocal regarding contact lenses inducing MG structural changes. The literature likewise indicates that the mechanism(s) by which contact lenses impact the MGs are likely multifactorial. Recent data suggests that MGs may have some plasticity. Detected differences between studies likely stem from varied populations evaluated, study designs, and the duration of the evaluation periods.

CONCLUSIONS

With this literature review finding conflicting relationships between MG health and contact lens use, future longitudinal studies with standardized clinical MG assessments are needed to determine the true impact of contact lenses on MG health. Until these data are obtained, contact lens wearers should undergo a full MG evaluation, especially because recent data suggest that MG treatments may restore MG structure and function.

Health-Related and Lifestyle Factors as Predictors of Intentions to Improve Lifestyle Habits in Employees Participating in a Workplace Health Promotion Program

OBJECTIVES

To explore employees' intentions to improve lifestyle habits, investigate the health and lifestyle-related predictors of these intentions, and how it translated into behavioral improvement.

METHODS

Employees participating in the Activate Your Health WHPP completed a questionnaire of their demographics, health-related variables, as well as six lifestyle habits and intention to improve them.

RESULTS

At baseline (n = 2729), most employees wanted to focus on physical activity and eating habits. Many predictors were identified for each intention. Majority of intentions were associated with behavioral improvement post-program (n = 525), especially in High.

CONCLUSIONS

In the context of WHPPs, intention to improve may lead to actual behavioral improvement. Exploring employees' intentions to improve various lifestyle habits at the start of the program could improve the effectiveness of these programs.

Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics

MOTIVATION

Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell membrane mostly relies on the mean-squared displacement plots, much information is lost when producing these plots from the trajectories. Here we employ deep learning to classify breast cancer cell types based on the trajectories of epidermal growth factor receptor (EGFR). Our model is an artificial neural network trained on the EGFR motions acquired from six breast cancer cell lines of varying invasiveness and receptor status: MCF7 (hormone receptor positive), BT474 (HER2-positive), SKBR3 (HER2-positive), MDA-MB-468 (triple negative, TN), MDA-MB-231 (TN) and BT549 (TN).

RESULTS

The model successfully classified the trajectories within individual cell lines with 83% accuracy and predicted receptor status with 85% accuracy. To further validate the method, epithelial-mesenchymal transition (EMT) was induced in benign MCF10A cells, noninvasive MCF7 cancer cells and highly invasive MDA-MB-231 cancer cells, and EGFR trajectories from these cells were tested. As expected, after EMT induction, both MCF10A and MCF7 cells showed higher rates of classification as TN cells, but not the MDA-MB-231 cells. Whereas deep learning-based cancer cell classifications are primarily based on the optical transmission images of cell morphology and the fluorescence images of cell organelles or cytoskeletal structures, here we demonstrated an alternative way to classify cancer cells using a dynamic, biophysical feature that is readily accessible.

AVAILABILITY AND IMPLEMENTATION

A python implementation of deep learning-based classification can be found at https://github.com/soonwoohong/Deep-learning-for-EGFR-trajectory-classification.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

False Memory and Alzheimer's Disease Pathology in Patients with Amnestic Mild Cognitive Impairment: A Study with Amyloid PET

Introduction

False memory, observed as intrusion errors or false positives (FPs), is prevalent in patients with Alzheimer's disease, but has yet to be thoroughly investigated in patients with amnestic mild cognitive impairment (a-MCI) with Alzheimer's disease pathology (ADP). We analyzed false versus veridical memory in individuals with a-MCI and measured the utility of false memory for ADP discrimination.

Methods

Patients with a-MCI who received neuropsychological testing and amyloid PET were included. Patients were categorized into "with" and "without ADP" groups according to PET results. Memory tests assessed veridical and false memory, and the verity of patient responses was analyzed. A logistic regression model was used to evaluate false memory efficiency in discriminating ADP, and the sensitivity and specificity at the optimal level were estimated using the receiver-operating characteristic curve.

Results

Thirty-seven ADP and 46 non-ADP patients were enrolled. The ADP group made more FPs in the recognition tests, and their response verity was significantly lower in every delayed memory test. No group difference, however, was observed in the veridical memory. The logistic regression analysis demonstrated that as the FPs increased, the risk of ADP increased 1.31 and 1.36 times in the verbal and visual recognition tests, respectively. The discriminatory accuracy of the FPs was estimated "low" to "moderate" in the visual and verbal recognition, respectively, with an optimal cutoff above 2.5.

Conclusion

Increased false memory was the only feature to discriminate ADP from non-ADP in individuals with a-MCI. Further studies regarding false memory and its mechanism are warranted.

Development of cardiotoxicity model using ligand-centric and receptor-centric descriptors

Background:

Bioinformatics and statistical analysis have been employed to develop a classification model to distinguish toxic and non-toxic molecules.

Aims:

The primary objective of this study is to enumerate the cut-off values of various physico-chemical (ligand-centric) and target interaction (receptor-centric) descriptors which forms the basis for classifying cardiotoxic and non-toxic molecules. We also sought correlation of molecular docking, absorption, distribution, metabolism, excretion, and toxicology (ADMET) parameters, Lipinski rules, physico-chemical parameters, etc. of human cardiotoxicity drugs.

Methods:

A training and test set of 91 compounds were applied to linear discriminant analysis (LDA) using 2D and 3D descriptors as discriminating variables representing various molecular modeling parameters to identify which function of descriptor type is responsible for cardiotoxicity. Internal validation was performed using the leave-one-out cross-validation methodology ensuing in good results, assuring the stability of the discriminant function (DF).

Results:

The values of the statistical parameters Fisher Discriminant Analysis (FDA) and Wilk’s λ for the DF showed reliable statistical significance, as long as the success rate in the prediction for both the training and the test set attained more than 93% accuracy, 87.50% sensitivity and 94.74% specificity.

Conclusion:

The predictive model was built using a hybrid approach using organ-specific targets for docking and ADMET properties for the FDA (Food and Drug Administration) approved and withdrawn drugs. Classifiers were developed by linear discriminant analysis and the cut-off was enumerated by receiver operating characteristic curve (ROC) analysis to achieve reliable specificity and sensitivity.

Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network

BACKGROUND AND OBJECTIVES

Fungal keratitis is caused by inflammation of the cornea that results from infection by fungal organisms. The lack of an early effective diagnosis often results in serious complications even blindness. Confocal microscopy is one of the most effective methods in the diagnosis of fungal keratitis, but the diagnosis depends on the subjective judgment of medical experts.

METHODS

To address this problem, this paper proposes a novel convolutional neural network framework for the automatic diagnosis of fungal keratitis using data augmentation and image fusion. Firstly, a normal image is augmented by flipping to solve the problem of having a limited and imbalanced database. Secondly, a sub-area contrast stretching algorithm is proposed for image preprocessing to highlight the key structures in the images and to filter out irrelevant information. Thirdly, the histogram matching fusion algorithm is implemented, then the preprocessed image is fused with the original image to form a new algorithm framework and a new database. Finally, the traditional convolutional neural network is integrated into the novel algorithm framework to perform the experiments.

RESULTS

Experiments show that the accuracy of traditional AlexNet and VGGNet is 99.35% and 99.14%, that of AlexNet and VGGNet based on MF fusion is 99.80% and 99.83%, and that of AlexNet and VGGNet based on histogram matching fusion (HMF) is 99.95% and 99.89%. The experimental results show that the AlexNet framework using data augmentation and image fusion achieves a perfect trade-off between the diagnostic performance and the computational complexity, with a diagnostic accuracy of 99.95%.

CONCLUSIONS

These experimental results demonstrate the novel convolutional neural network framework perfectly balances the diagnostic performance and computational complexity, and can improve the effect and real-time performance in the diagnosis of fungal keratitis.

Ability of the Global Alignment and Proportion Score to Predict Mechanical Failure Following Adult Spinal Deformity Surgery-Validation in 149 Patients With Two-Year Follow-up

STUDY DESIGN

Retrospective analysis of prospectively collected data.

OBJECTIVES

To validate the Global Alignment and Proportion (GAP) score in a single-center cohort of adult spinal deformity (ASD) patients.

SUMMARY OF BACKGROUND DATA

Surgical treatment for ASD is associated with a high risk of mechanical failure and consequent revision surgery. To improve prediction of mechanical complications, the GAP score was developed with promising results. Development was based on the assumption that not all patients would benefit from the same fixed radiographic targets as pelvic incidence is an individual, morphological parameter that greatly influences the sagittal curves of the spine.

METHODS

In a validation study of the GAP score, patients undergoing ASD surgery with four or more levels of instrumentation were consecutively included at a tertiary spine unit. Patients were followed for a minimum of two years. Pre- and postoperative GAP score and categories were calculated for all patients, and the association with mechanical failure and revision surgery was analyzed.

RESULTS

A total of 149 patients with a mean age of 57.4 years were included. Overall, the rates of mechanical failure and revision surgery were 51% and 35% respectively. The area under the curve (AUC) using receiver operating characteristic was classified as "no or low discriminatory power" for the GAP score in predicting either outcome (AUC = 0.50 and 0.49, respectively). Similarly, there were no significant associations between GAP categories and the occurrence of mechanical failure or revision surgery when using Cochran-Armitage test of trend (p = .28 for mechanical failure and p = .58 for revision surgery).

CONCLUSIONS

In a consecutive series of surgically treated ASD patients, we found no significant association between postoperative GAP score and mechanical failure or revision surgery. Despite minor limitations in similarities to the original study cohort, further validation studies or adjustments to the original scoring system are proposed.

LEVEL OF EVIDENCE

Level II.

Determination of Threshold Scores for Treatment Success After Arthroscopic Rotator Cuff Repair Using Oxford, Constant, and University of California, Los Angeles Shoulder Scores

PURPOSE

To determine the threshold scores for the Constant-Murley score (CMS); University of California, Los Angeles (UCLA) shoulder score; and Oxford Shoulder Score (OSS) that determine treatment success after arthroscopic rotator cuff (RC) repair.

METHODS

Patients who underwent unilateral arthroscopic double-row RC repair by a single surgeon between 2010 and 2015 were prospectively followed up and assessed preoperatively and at 6, 12, and 24 months postoperatively. Perceived pain was measured with a visual analog scale, and functional outcome was assessed by the CMS, UCLA score, and OSS. Treatment success was defined as simultaneous fulfillment of 3 criteria: improvement in pain, expectations for surgery met, and patient satisfied with surgery. Threshold scores were determined by receiver operating characteristic (ROC) analyses, using the various scores as predictors and the defined treatment success as the criterion.

RESULTS

The study included a total of 214 patients (96 male and 118 female patients) with a mean age of 60.1 ± 10 years. Most patients showed improvement in pain (≥88%) and high satisfaction (≥93%) and expectation fulfillment (≥80%) postoperatively. Of the patients, 73% had treatment success at 6 months; 85%, at 12 months; and 80%, at 24 months. ROC analyses showed good prediction of treatment success using the CMS, UCLA score, and OSS at all 3 follow-up time points (area under the ROC curve [AUC] > 0.70), with excellent prediction using the UCLA score at 12 months and 24 months (AUC, 0.811 and 0.805, respectively) and the OSS at 12 months (AUC, 0.820). The following threshold scores were identified: CMS of 59, UCLA score of 21, and OSS of 42 at 6 months; CMS of 61, UCLA score of 26, and OSS of 43 at 12 months; and CMS of 65, UCLA score of 30, and OSS of 46 at 24 months.

CONCLUSIONS

The CMS, UCLA score, and OSS have good predictive value in defining treatment success after arthroscopic RC repair. The cutoff scores are time dependent.

LEVEL OF EVIDENCE

Level II, development of diagnostic criteria.

Validity of triglyceride-glucose index as an indicator for metabolic syndrome in children and adolescents: the CASPIAN-V study

PURPOSE

The purpose of this study was to determine the cut-off values of triglycerides and glucose (TyG) index as one of the indirect indices for metabolic syndrome (MetS) in a pediatric population.

METHODS

This national study was conducted in 2015 on 14400 students, aged 7-18 years. They were selected by random cluster sampling from 30 provinces of our country during the fifth survey of a national school-based surveillance program. MetS was defined based on the Adult Treatment Panel III (ATP III) criteria modified for the pediatric age group. The cut-off values of TyG index for MetS were obtained using the receiver operation characteristic (ROC) curve analysis by gender and age groups.

RESULTS

Totally, 3843 students (52.3% boys) with mean (SD) age of 12.45 (3.04) years were assessed. The area under the ROC curve of TyG index for MetS was 0.83 in total participants. According to the ATP III criteria the cut-off values of the TyG index were 8.33 (8.21-8.45) in total students, 8.47 (8.36-8.58) in boys, and 8.33 (8.18-8.48) in girls. In the 7-12 and 13-18 years' age groups, these values were 8.47 (8.32-8.63) and 8.34 (8.22-8.45) in total, 8.39 (8.26-8.52) and 8.47 (8.33-8.61) in boys, 8.33 (8.11-8.55) and 8.35 (8.22-8.47) in girls, respectively.

CONCLUSION

The findings of this study can be clinically helpful for screening MetS in children and adolescents but the effectiveness of these criteria needs to be evaluated by further longitudinal surveys.

LEVEL OF EVIDENCE

Level V, cross-sectional descriptive study (National surveillance study).

Satiety Factors Oleoylethanolamide, Stearoylethanolamide, and Palmitoylethanolamide in Mother's Milk Are Strongly Associated with Infant Weight at Four Months of Age-Data from the Odense Child Cohort

Regulation of appetite and food intake is partly regulated by N-acylethanolamine lipids oleoylethanolamide (OEA), stearoylethanolamide (SEA), and palmitoylethanolamide (PEA), which induce satiety through endogenous formation in the small intestine upon feeding, but also when orally or systemic administered. OEA, SEA, and PEA are present in human milk, and we hypothesized that the content of OEA, SEA, and PEA in mother’s milk differed for infants being heavy (high weight-for-age Z-score (WAZ)) or light (low WAZ) at time of milk sample collection. Ultra-high performance liquid chromatography-mass spectrometry was used to determine the concentration of OEA, SEA, and PEA in milk samples collected four months postpartum from mothers to high (n = 50) or low (n = 50) WAZ infants. Associations between OEA, SEA, and PEA concentration and infant anthropometry at four months of age as well as growth from birth were investigated using linear and logistic regression analyses, adjusted for birth weight, early infant formula supplementation, and maternal pre-pregnancy body mass index. Mean OEA, SEA, and PEA concentrations were lower in the high compared to the low WAZ group (all p < 0.02), and a higher concentration of SEA was associated with lower anthropometric measures, e.g., triceps skinfold thickness (mm) (β = −2.235, 95% CI = −4.04, −0.43, p = 0.016), and weight gain per day since birth (g) (β = −8.169, 95% CI = −15.26, −1.08, p = 0.024). This raises the possibility, that the content of satiety factors OEA, SEA, and PEA in human milk may affect infant growth.

Use of schizophrenia and bipolar disorder polygenic risk scores to identify psychotic disorders

BACKGROUND

There is increasing evidence for shared genetic susceptibility between schizophrenia and bipolar disorder. Although genetic variants only convey subtle increases in risk individually, their combination into a polygenic risk score constitutes a strong disease predictor.AimsTo investigate whether schizophrenia and bipolar disorder polygenic risk scores can distinguish people with broadly defined psychosis and their unaffected relatives from controls.

METHOD

Using the latest Psychiatric Genomics Consortium data, we calculated schizophrenia and bipolar disorder polygenic risk scores for 1168 people with psychosis, 552 unaffected relatives and 1472 controls.

RESULTS

Patients with broadly defined psychosis had dramatic increases in schizophrenia and bipolar polygenic risk scores, as did their relatives, albeit to a lesser degree. However, the accuracy of predictive models was modest.

CONCLUSIONS

Although polygenic risk scores are not ready for clinical use, it is hoped that as they are refined they could help towards risk reduction advice and early interventions for psychosis.Declaration of interestR.M.M. has received honoraria for lectures from Janssen, Lundbeck, Lilly, Otsuka and Sunovian.

Role of ambient light in the detection of contrast elements in digital dental radiography

OBJECTIVE

This study aimed to evaluate the effect of different ambient light levels on observer detection of small contrast differences in a contrast phantom by using a high-end liquid crystal display (LCD) monitor.

STUDY DESIGN

An aluminum step wedge was converted into a contrast phantom by the addition of bore holes. Radiographic images of the contrast elements were presented to 20 observers. Images were displayed in random order under different ambient light levels (0, 50, 200, and 500 lux) twice, and the observers had to determine if contrast elements were visible. Sensitivity and specificity were determined to calculate areas under receiver operating characteristic curves and Friedmann's test was applied to evaluate the influence of the ambient light level on observer performance.

RESULTS

Mean AZ values were moderate for each ambient light level at 0.715, 0.793, 0.764, and 0.722 for 0, 50, 200, and 500 lux, respectively. The influence of the ambient light level on observer performance was not statistically significant (P > .05).

CONCLUSIONS

There was no significant influence of ambient light between 0 and 500 lux on observer ability to detect small contrast details displayed on a high-end LCD monitor.

Determinants of Perceived Health Nonimprovement in Early Rheumatoid Arthritis Patients With Favorable Treatment Outcomes

OBJECTIVE

To explore the association between achieving favorable clinical outcomes and patients' perceived change in overall health status after 12 months of treat-to-target in patients with early rheumatoid arthritis (RA) and to identify determinants of subjective nonimprovement.

METHODS

Baseline and 12-month data of patients included in the Dutch Rheumatoid Arthritis Monitoring remission induction cohort study with at least a moderate response (by European League Against Rheumatism criteria) after 1 year were selected for analysis. Logistic regression analysis was used to identify factors associated with nonimproved perceived overall health status at 12 months.

RESULTS

At 12 months, 75 of 210 patients (35%) did not consider their health to have improved despite having achieved favorable clinical outcomes. Relative change from baseline in pain (Wald = 20.20; P < 0.01) and fatigue (Wald = 5.58; P = 0.02) was independently associated with nonimproved perceived overall health status. The results were similar when only patients with ≤1 swollen joint were analyzed. An improvement of 55% in pain measured on a visual analog scale was found to discriminate reasonably well between patients who considered their health to have improved versus patients who did not, with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval 0.61-0.78).

CONCLUSION

These results demonstrate that clinical improvements do not equate with improved subjective health for all patients. The association of nonimprovement with changes in pain and fatigue suggest that it might be worthwhile to monitor and address pain and fatigue in addition to and independently of disease activity in early RA.

Finite Element Based Pelvic Injury Metric Creation and Validation in Lateral Impact for a Human Body Model

Pelvic fractures are serious injuries resulting in high mortality and morbidity. The objective of this study is to develop and validate local pelvic anatomical, cross-section-based injury risk metrics for a finite element (FE) model of the human body. Cross-sectional instrumentation was implemented in the pelvic region of the Global Human Body Models Consortium (GHBMC M50-O) 50th percentile detailed male FE model (v4.3). In total, 25 lateral impact FE simulations were performed using input data from cadaveric lateral impact tests performed by Bouquet et al. The experimental force-time data was scaled using five normalization techniques, which were evaluated using log rank, Wilcoxon rank sum, and correlation and analysis (CORA) testing. Survival analyses with Weibull distribution were performed on the experimental peak force (scaled and unscaled) and the simulation test data to generate injury risk curves (IRCs) for total pelvic injury. Additionally, IRCs were developed for regional injury using cross-sectional forces from the simulation results and injuries documented in the experimental autopsies. These regional IRCs were also evaluated using the receiver operator characteristic (ROC) curve analysis. Based on the results of the all the evaluation methods, the Equal Stress Equal Velocity (ESEV) and ESEV using effective mass (ESEV-EM) scaling techniques performed best. The simulation IRC shows slight under prediction of injury in comparison to these scaled experimental data curves. However, this difference was determined to not be statistically significant. Additionally, the ROC curve analysis showed moderate predictive power for all regional IRCs.

Data Improvement Through Simplification: Implications for Low-Resource Settings

BACKGROUND

The focus of many data collection efforts centers on creation of more granular data. The assumption is that more complex data are better able to predict outcomes. We hypothesized that data are often needlessly complex. We sought to demonstrate this concept by examination of the American Society of Anesthesiologists (ASA) scoring system.

METHODS

First, we created every possible consecutive two, three and four category combinations of the current five category ASA score. This resulted in 14 combinations of simplified ASA. We compared the predictive ability of these simplified scores for postoperative outcomes for 2.3 million patients in the NSQIP database. Individual model performance was assessed by comparing receiver operator characteristic (ROC) curves for each model with the standard ASA.

RESULTS

Two of our 4-category models and one of our 3-category models had ability to predict all outcomes equivalent to standard ASA. These results held for all outcomes and on all subgroups tested. The performance of the three best performing simplified ASA scores were also equivalent to the standard ASA score in the univariate analysis and when included in a multivariate model.

CONCLUSIONS

It is assumed that the most granular data and use of the largest number of variables for risk-adjusted predictions will increase accuracy. This complexity is often at the expense of utility. Using the single best predictor in surgical outcomes research, we have shown this is not the case. In this example, we demonstrate that one can simplify ASA into a 3-category variable without losing any ability to predict outcomes.

Triglycerides/High density lipoprotein cholesterol ratio as a cardiometabolic risk marker in children and adolescents from Mérida city, Venezuela

OBJECTIVE

To determine the behavior of the triglycerides/HDL-cholesterol ratio (TG/HDL) as a cardiometabolic risk marker in children and adolescents from Mérida, Venezuela.

METHODS

A total of 1292 children and adolescents aged 7-18 years who attended educational institutions in the Libertador Municipality were enrolled into this study. Anthropometric measurements and blood pressure values were recorded. Fasting blood glucose, insulin and lipid levels were measured. The TG/HDL ratio, HOMA-IR, and QUICKI indexes were calculated. Subjects were categorized as with and without cardiometabolic risk based on the presence or absence of 2or more risk factors. Cut-off points for the TG/HDL ratio were determined by constructing ROC curves.

RESULTS

Significantly higher mean TG/HDL ratios were found in pubertal (2.2 ± 1.7) as compared to prepubertal subjects (1.8 ± 1.5; P=.001), with no sex differences. Two or more risk factors were found in 14.7% (n=192) of the participants, in whom TG/HDL ratios were significantly higher as compared to those with no risk (3.5±2.9 versus 1.6±0.8 in prepubertal and 4.1 ± 3.5 versus 1.8 ± 0.9 in pubertal subjects; P=.0001). According to cardiometabolic risk, cut-off points for the TG/HDL ratio of 1.8 and 2.5 were found for prepubertal and pubertal children respectively. These cut-off points showed risks (odds ratio) higher than 2.5 for conditions such as metabolic syndrome, elevated non-HDL-C, abdominal obesity, and elevated HOMA-IR.

CONCLUSION

In this sample of children and adolescents, an elevated TG/HDLc ratio was found to be a good marker for predicting cardiometabolic risk.

Evaluation of a Commercial Field Test to Detect African Swine Fever

African swine fever (ASF) is one of the most important and complex infectious diseases affecting pigs ( Sus scrofa ). The disease has been present in Sardinia, Italy, since 1978. Factors influencing the presence of the disease on the island are the presence of illegally bred pigs, uncontrolled movements of animals, and local traditions. Implementation of public health programs is essential for controlling ASF. The use of new diagnostic techniques on both wild boar (WB) and illegally bred pigs would provide tools for faster and more inexpensive control of the disease. We evaluated a commercial serological test kit (Pen-side [PS]) for use in the field. We sampled 113 hunter-harvested WB during the 2014-15 season, collecting blood and lung samples to conduct serological analyses and to screen for the ASF virus. Although the sensitivity (81.8%) and specificity (95.9%) of tests performed in the field were reduced compared to the same test in laboratory, they nevertheless allowed for rapid diagnosis and reduced unnecessary carcass destruction. The test, conducted in the field, was less expensive than in the laboratory and required less manpower. Therefore, we conclude that the combined use of antibody PS test and antigen PS test may be a valuable emergency management method during an outbreak as well as a useful tool for conducting regular monitoring activities as a preventive policy.

Comparison of Basic Science Knowledge Between DO and MD Students

Context: With the coming single accreditation system for graduate medical education, medical educators may wonder whether knowledge in basic sciences is equivalent for osteopathic and allopathic medical students.

Objective: To examine whether medical students’ basic science knowledge is the same among osteopathic and allopathic medical students.

Methods: A dataset of the Touro University College of Osteopathic Medicine-CA student records from the classes of 2013, 2014, and 2015 and the national cohort of National Board of Medical Examiners Comprehensive Basic Science Examination (NBME-CBSE) parameters for MD students were used. Models of the Comprehensive Osteopathic Medical Licensing Examination-USA (COMLEX-USA) Level 1 scores were fit using linear and logistic regression. The models included variables used in both osteopathic and allopathic medical professions to predict COMLEX-USA outcomes, such as Medical College Admission Test biology scores, preclinical grade point average, number of undergraduate science units, and scores on the NBME-CBSE. Regression statistics were studied to compare the effectiveness of models that included or excluded NBME-CBSE scores at predicting COMLEX-USA Level 1 scores. Variance inflation factor was used to investigate multicollinearity. Receiver operating characteristic curves were used to show the effectiveness of NBME-CBSE scores at predicting COMLEX-USA Level 1 pass/fail outcomes. A t test at 99% level was used to compare mean NBME-CBSE scores with the national cohort.

Results: A total of 390 student records were analyzed. Scores on the NBME-CBSE were found to be an effective predictor of COMLEX-USA Level 1 scores (P<.001). The pass/fail outcome on COMLEX-USA Level 1 was also well predicted by NBME-CBSE scores (P<.001). No significant difference was found in performance on the NBME-CBSE between osteopathic and allopathic medical students (P=.322).

Conclusion: As an examination constructed to assess the basic science knowledge of allopathic medical students, the NBME-CBSE is effective at predicting performance on COMLEX-USA Level 1. In addition, osteopathic medical students performed the same as allopathic medical students on the NBME-CBSE. The results imply that the same basic science knowledge is expected for DO and MD students.

The utility of 6-minute walk distance in predicting waitlist mortality for lung transplant candidates

BACKGROUND

The lung allocation score (LAS) has led to improved organ allocation for transplant candidates. At present, the 6-minute walk distance (6MWD) is treated as a binary categorical variable of whether or not a candidate can walk more than 150 feet in 6 minutes. In this study, we tested the hypothesis that 6MWD is presently under-utilized with respect to discriminatory power, and that, as a continuous variable, could better prognosticate risk of waitlist mortality.

METHODS

A retrospective cohort analysis was performed using the Organ Procurement and Transplantation Network/United Network for Organ Sharing (OPTN/UNOS) transplant database. Candidates listed for isolated lung transplant between May 2005 and December 2011 were included. The population was stratified by 6MWD quartiles and unadjusted survival rates were estimated. Multivariable Cox proportional hazards modeling was used to assess the effect of 6MWD on risk of death. The Scientific Registry of Transplant Recipients (SRTR) Waitlist Risk Model was used to adjust for confounders. The optimal 6MWD for discriminative accuracy in predicting waitlist mortality was assessed by receiver-operating characteristic (ROC) curves.

RESULTS

Analysis was performed on 12,298 recipients. Recipients were segregated into quartiles by distance walked. Waitlist mortality decreased as 6MWD increased. In the multivariable model, significant variables included 6MWD, male gender, non-white ethnicity and restrictive lung diseases. ROC curves discriminated 6-month mortality was best at 655 feet.

CONCLUSIONS

The 6MWD is a significant predictor of waitlist mortality. A cut-off of 150 feet sub-optimally identifies candidates with increased risk of mortality. A cut-off between 550 and 655 feet is more optimal if 6MWD is to be treated as a dichotomous variable. Utilization of the LAS as a continuous variable could further enhance predictive capabilities.

Associations among Habitat Characteristics and Meningeal Worm Prevalence in Eastern South Dakota, USA

Few studies have evaluated how wetland and forest characteristics influence the prevalence of meningeal worm ( Parelaphostrongylus tenuis ) infection of deer throughout the grassland biome of central North America. We used previously collected, county-level prevalence data to evaluate associations between habitat characteristics and probability of meningeal worm infection in white-tailed deer ( Odocoileus virginianus ) across eastern South Dakota, US. The highest-ranked binomial regression model for detecting probability of meningeal worm infection was spring temperature + summer precipitation + percent wetland; weight of evidence (w_i=0.71) favored this model over alternative models, though predictive capability was low (Receiver operating characteristic=0.62). Probability of meningeal worm infection increased by 1.3- and 1.6-fold for each 1-cm and 1-C increase in summer precipitation and spring temperature, respectively. Similarly, probability of infection increased 1.2-fold for each 1% increase in wetland habitat. Our findings highlight the importance of wetland habitat in predicting meningeal worm infection across eastern South Dakota. Future research is warranted to evaluate the relationships between climatic conditions (e.g., drought, wet cycles) and deer habitat selection in maintaining P. tenuis along the western boundary of the parasite.

Surgery versus Nerve Blocks for Lumbar Disc Herniation : Quantitative Analysis of Radiological Factors as a Predictor for Successful Outcomes

OBJECTIVE

To assess the clinical and radiological factors as predictors for successful outcomes in lumbar disc herniation (LDH) treatment.

METHODS

Two groups of patients with single level LDH (L4-5) requiring treatment were retrospectively studied. The surgery group (SG) included 34 patients, and 30 patients who initially refused the surgery were included in the nerve blocks group (NG). A visual analogue scale (VAS) for leg and back pain and motor deficit were initially evaluated before procedures, and repeated at 1, 6, and 12 months. Radiological factors including the disc herniation length, disc herniation area, canal length-occupying ratio, and canal area-occupying ratio were measured and compared. Predicting factors of successful outcomes were determined with multivariate logistic regression analysis after the optimal cut off values were established with a receiver operating characteristic curve.

RESULTS

There was no significant demographic difference between two groups. A multivariate logistic regression analysis with radiological and clinical (12 months follow-up) data revealed that the high disc herniation length with cutoff value 6.31 mm [odds ratio (OR) 2.35; confidence interval (CI) 1.21-3.98] was a predictor of successful outcomes of leg pain relief in the SG. The low disc herniation length with cutoff value 6.23 mm (OR 0.05; CI 0.003-0.89) and high baseline VAS leg (OR 12.63; CI 1.64-97.45) were identified as predictors of successful outcomes of leg pain relief in the NG.

CONCLUSION

The patients with the disc herniation length larger than 6.31 mm showed successful outcomes with surgery whereas the patients with the disc herniation length less than 6.23 mm showed successful outcomes with nerve block. These results could be considered as a radiological criteria in choosing optimal treatment options for LDH.

Early warning score predicts acute mortality in stroke patients

OBJECTIVES

Clinical deterioration and death among patients with acute stroke are often preceded by detrimental changes in physiological parameters. Systematic and effective tools to identify patients at risk of deterioration early enough to intervene are therefore needed. The aim of the study was to investigate whether the aggregate weighted track and trigger system early warning score (EWS) can be used as a simple observational tool to identify patients at risk and predict mortality in a population of patients with acute stroke.

MATERIALS AND METHODS

Patients admitted with acute stroke at the Copenhagen University Hospital, Nordsjaellands Hospital, Denmark, from May to September 2012 were enrolled in a retrospective cohort study (n = 274). Vital signs were measured immediately after admission and consistently during the hospitalization period. Based on the vital signs, a single composite EWS was calculated. Death within 30 days was used as outcome. Area under the receiver operating characteristics curve (AUROC) and a Kaplan-Meier curve were computed to examine the prognostic validity of EWS.

RESULTS

A total of 24 patients (8.8%) died within 30 days. The prognostic performance was high for both the EWS at admission (AUROC 0.856; 95% CI 0.760-0.951; P-value < 0.001) and the maximal EWS measured (AUROC 0.949; 95% CI 0.919-0.980; P-value < 0.001). Mortality rates were lowest for admission EWS 0-1 (2%) and highest for admission EWS ≥ 5 (63%).

CONCLUSIONS

Early warning score is a simple and valid tool for identifying patients at risk of dying after acute stroke. Readily available physiological parameters are converted to a single score, which can guide both nurses and physicians in clinical decision making and resource allocation.

A next-generation marker genotyping platform (AmpSeq) in heterozygous crops: a case study for marker-assisted selection in grapevine

Marker-assisted selection (MAS) is often employed in crop breeding programs to accelerate and enhance cultivar development, via selection during the juvenile phase and parental selection prior to crossing. Next-generation sequencing and its derivative technologies have been used for genome-wide molecular marker discovery. To bridge the gap between marker development and MAS implementation, this study developed a novel practical strategy with a semi-automated pipeline that incorporates trait-associated single nucleotide polymorphism marker discovery, low-cost genotyping through amplicon sequencing (AmpSeq) and decision making. The results document the development of a MAS package derived from genotyping-by-sequencing using three traits (flower sex, disease resistance and acylated anthocyanins) in grapevine breeding. The vast majority of sequence reads (⩾99%) were from the targeted regions. Across 380 individuals and up to 31 amplicons sequenced in each lane of MiSeq data, most amplicons (83 to 87%) had <10% missing data, and read depth had a median of 220-244×. Several strengths of the AmpSeq platform that make this approach of broad interest in diverse crop species include accuracy, flexibility, speed, high-throughput, low-cost and easily automated analysis.

Telephone-based cognitive screening for stroke patients in China

BACKGROUND

Valid telephone assessment for cognitive impairment is lacking in stroke settings. We investigated the feasibility and validity of the 5-minute National Institute of Neurological Disorders and Stroke and Canadian Stroke Network (NINDS-CSN) protocol and six-item screener (SIS) in stroke patients by telephone administration.

METHODS

Patients were assessed with a comprehensive face-to-face neuropsychological assessment after three months of stroke onset, followed by the 5-minute NINDS-CSN protocol (30 points) and SIS (6 points) at least one month later. Administration time was recorded for the telephone tests. Validity of both tests was determined using the area under the receiver operating characteristics curve (AUC).

RESULTS

Eighty-nine patients (age, 62.9 ± 8.6 years; male, 65.2%) received a face-to-face assessment and 80 completed telephone tests. The time required to administer the 5-minute NINDS-CSN protocol was 4.3 ± 1.0 minutes, and SIS 57.3 ± 17.7 seconds. Validity of detecting cognitive impairment as assessed by AUC was 0.86 (95% CI, 0.78-0.94) for 5-minute NINDS-CSN protocol, and 0.74 (95% CI, 0.63-0.85) for SIS. Sensitivity and specificity were optimal with the cut-off values of 23.5/24 for the 5-minute NINDS-CSN protocol, and 4/5 for SIS.

CONCLUSIONS

Both the telephone-based 5-minute NINDS-CSN protocol and SIS were feasible and valid in screening cognitive impairment after stroke in China.

Insulin sensitivity index (ISI0, 120) potentially linked to carbon isotopes of breath CO2 for pre-diabetes and type 2 diabetes

New strategies for an accurate and early detection of insulin resistance are important to delay or prevent the acute onset of type 2 diabetes (T2D). Currently, insulin sensitivity index (ISI_0,120) is considered to be a viable invasive method of whole-body insulin resistance for use in clinical settings in comparison with other invasive sensitivity indexes like homeostasis model assessment (HOMA), and quantitative insulin sensitivity check index (QUICKI). To investigate how these sensitivity indexes link the ¹³C/¹²C-carbon isotopes of exhaled breath CO₂ to pre-diabetes (PD) and type 2 diabetes in response to glucose ingestion, we studied excretion dynamics of ¹³C/¹²C-isotopic fractionations of breath CO₂. Here, we show that ¹³C/¹²C-isotope ratios of breath CO₂ were well correlated with blood glucose, insulin, glycosylated-hemoglobin as well as with HOMA-IR and 1/QUICKI. Conversely, the strongest correlation was observed between 1/ISI_0,120 and breath CO₂ isotopes. Consequently, we determined several optimal diagnostic cut-off points of 1/ISI_0,120 and ¹³CO₂/¹²CO₂-isotope ratios to distinctively track the evolution of PD prior to the onset of T2D. Our findings suggest that isotopic breath CO₂ is a novel method for accurate estimation of ISI_0,120 and thus may open new perspectives into the isotope-specific non-invasive evaluation of insulin resistance for large-scale real-time diabetes screening purposes.

Environmental Factors Influencing White-Tailed Deer (Odocoileus virginianus) Exposure to Livestock Pathogens in Wisconsin

White-tailed deer (Odocoileus virginianus) are commonly exposed to disease agents that affect livestock but environmental factors that predispose deer to exposure are unknown for many pathogens. We trapped deer during winter months on two study areas (Northern Forest and Eastern Farmland) in Wisconsin from 2010 to 2013. Deer were tested for exposure to six serovars of Leptospira interrogans (grippotyphosa, icterohaemorrhagiae, canicola, bratislava, pomona, and hardjo), bovine viral diarrhea virus (BVDV-1 and BVDV-2), infectious bovine rhinotracheitis virus (IBR), and parainfluenza 3 virus (PI3). We used logistic regression to model potential intrinsic (e.g., age, sex) and extrinsic (e.g., land type, study site, year, exposure to multiple pathogens) variables we considered biologically meaningful to exposure of deer to livestock pathogens. Deer sampled in 2010–2011 did not demonstrate exposure to BVDV, so we did not test for BVDV in subsequent years. Deer had evidence of exposure to PI3 (24.7%), IBR (7.9%), Leptospira interrogans serovar pomona (11.7%), L. i. bratislava (1.0%), L. i. grippotyphosa (2.5%) and L. i. hardjo (0.3%). Deer did not demonstrate exposure to L. interrogans serovars canicola and icterohaemorrhagiae. For PI3, we found that capture site and year influenced exposure. Fawns (n = 119) were not exposed to L. i. pomona, but land type was an important predictor of exposure to L. i. pomona for older deer. Our results serve as baseline exposure levels of Wisconsin white-tailed deer to livestock pathogens, and helped to identify important factors that explain deer exposure to livestock pathogens.

Diagnostic testing: an emergency medicine perspective

Emergency physicians use diagnostic tests extensively, and the ability to order and interpret test results appropriately is a critical skill. An understanding of sensitivity, specificity, predictive values and likelihood ratios, as well as an awareness of the importance of pre-test probability, is essential. The purpose of this article is to explain, in a straightforward and clinically applicable manner, the core concepts related to diagnostic testing.

Pan-Serotype Diagnostic for Foot-and-Mouth Disease Using the Consensus Antigen of Nonstructural Protein 3B

An amino acid consensus sequence for the seven serotypes of foot-and-mouth disease virus (FMDV) nonstructural protein 3B, including all three contiguous repeats, and its use in the development of a pan-serotype diagnostic test for all seven FMDV serotypes are described. The amino acid consensus sequence of the 3B protein was determined from a multiple-sequence alignment of 125 sequences of 3B. The consensus 3B (c3B) protein was expressed as a soluble recombinant fusion protein with maltose-binding protein (MBP) using a bacterial expression system and was affinity purified using amylose resin. The MBP-c3B protein was used as the antigen in the development of a competition enzyme-linked immunosorbent assay (cELISA) for detection of anti-3B antibodies in bovine sera. The comparative diagnostic sensitivity and specificity at 47% inhibition were estimated to be 87.22% and 93.15%, respectively. Reactivity of c3B with bovine sera representing the seven FMDV serotypes demonstrated the pan-serotype diagnostic capability of this bioreagent. The consensus antigen and competition ELISA are described here as candidates for a pan-serotype diagnostic test for FMDV infection.

Audible handheld Doppler ultrasound determines reliable and inexpensive exclusion of significant peripheral arterial disease

Objective

To determine the accuracy of audible arterial foot signals with an audible handheld Doppler ultrasound for identification of significant peripheral arterial disease as a simple, quick, and readily available bedside screening tool.

Methods

Two hundred consecutive patients referred to an interprofessional wound care clinic underwent audible handheld Doppler ultrasound of both legs. As a control and comparator, a formal bilateral lower leg vascular study including the calculation of Ankle Brachial Pressure Index and toe pressure (TP) was performed at the vascular lab. Diagnostic reliability of audible handheld Doppler ultrasound was calculated versus Ankle Brachial Pressure Index as the gold standard test.

Results

A sensitivity of 42.8%, a specificity of 97.5%, negative predictive value of 94.10%, positive predictive value of 65.22%, positive likelihood ratio of 17.52, and negative likelihood ratio of 0.59. The univariable logistic regression model had an area under the curve of 0.78. There was a statistically significant difference at the 5% level between univariable and multivariable area under the curves of the dorsalis pedis and posterior tibial models ( p < 0.001).

Conclusion

Audible handheld Doppler ultrasound proved to be a reliable, simple, rapid, and inexpensive bedside exclusion test of peripheral arterial disease in diabetic and nondiabetic patients.

Brief Pain Inventory–Facial minimum clinically important difference

OBJECT

Neurosurgeons are frequently the primary physicians measuring pain relief in patients with trigeminal neuralgia (TN). Unfortunately, the measurement of pain can be complex. The Brief Pain Inventory–Facial (BPI-Facial) is a reliable and validated multidimensional tool that consists of 18 questions. It measures 3 domains of pain: 1) pain intensity (worst and average pain intensity), 2) interference with general activities of daily living (ADL), and 3) face-specific pain interference. The objective of this paper is to determine the patient-reported minimum clinically important difference (MCID) using the BPI-Facial.

METHODS

The authors conducted a retrospective study of 234 patients with TN seen in a single neurosurgeon's office. Patients completed baseline and 1-month follow-up BPI-Facial questionnaires. The MCID was calculated using an anchor-based approach in which the defined anchor was the 7-point patient global impression of change (PGIC). Two statistical methods were employed: mean change score and optimal cutoff point.

RESULTS

Using the mean change score method, the investigators calculated the MCID for the 3 domains of the BPIFacial: 44% and 30% improvement in pain intensity at its worst and average, respectively, 54% improvement in interference with general ADL, and 63% improvement in interference with facial ADL. Using the optimal cutoff point method, they also calculated the MCID for the 3 domains of the BPI-Facial: 57% and 28% improvement in pain intensity at its worst and average, respectively, 75% improvement in interference with general ADL, and 62% improvement in interference with facial ADL.

CONCLUSIONS

The BPI-Facial is a multidimensional pain scale that measures 3 domains of pain. Although 2 statistical methods were used to calculate the MCID, the optimal cutoff point method was the superior one because it used data from the majority of subjects included in this study. A 57% improvement in pain intensity at its worst and a 28% improvement in pain intensity at its average were the MCIDs for patients with facial pain. A greater improvement was needed to achieve the MCID for interference with general and facial ADL. A 75% improvement in interference with general ADL and a 62% improvement in interference with facial ADL were needed to achieve an MCID. While pain intensity is easier to measure, pain's interference with ADL may be more important for patient outcomes when designing or evaluating interventions in the field of TN. The BPI-Facial is a useful instrument to measure changes in multidimensional aspects of pain in patients with TN.

Excretion kinetics of 13C-urea breath test: influences of endogenous CO2 production and dose recovery on the diagnostic accuracy of Helicobacter pylori infection

We report for the first time the excretion kinetics of the percentage dose of (13)C recovered/h ((13)C-PDR %/h) and cumulative PDR, i.e. c-PDR (%) to accomplish the highest diagnostic accuracy of the (13)C-urea breath test ((13)C-UBT) for the detection of Helicobacter pylori infection without any risk of diagnostic errors using an optical cavity-enhanced integrated cavity output spectroscopy (ICOS) method. An optimal diagnostic cut-off point for the presence of H. pylori infection was determined to be c-PDR (%) = 1.47 % at 60 min, using the receiver operating characteristic curve (ROC) analysis to overcome the "grey zone" containing false-positive and false-negative results of the (13)C-UBT. The present (13)C-UBT exhibited 100 % diagnostic sensitivity (true-positive rate) and 100 % specificity (true-negative rate) with an accuracy of 100 % compared with invasive endoscopy and biopsy tests. Our c-PDR (%) methodology also manifested both diagnostic positive and negative predictive values of 100 %, demonstrating excellent diagnostic accuracy. We also observed that the effect of endogenous CO2 production related to basal metabolic rates in individuals was statistically insignificant (p = 0.78) on the diagnostic accuracy. However, the presence of H. pylori infection was indicated by the profound effect of urea hydrolysis rate (UHR). Our findings suggest that the current c-PDR (%) is a valid and sufficiently robust novel approach for an accurate, specific, fast and noninvasive diagnosis of H. pylori infection, which could routinely be used for large-scale screening purposes and diagnostic assessment, i.e. for early detection and follow-up of patients.

Risperidone metabolic ratio as a biomarker of individual CYP2D6 genotype in schizophrenic patients

PURPOSE

The purpose of the present study was to investigate the predictive value of the risperidone metabolic ratio for the individual CYP2D6 genotype.

METHODS

The determination of risperidone, 9-hydroxyrisperidone, and CYP2D6 genotype was performed in 89 schizophrenic patients. The receiver operator characteristic (ROC) method and the area under the ROC curve (AUC) were used to illustrate the predictive value of risperidone metabolic ratio for the individual CYP2D6 genotype. The area under the ROC curve (AUC) was used as a global measure of this predictive value. To evaluate the proposed cutoff levels of >1 and <0.1 to identify individuals with a poor or ultrarapid CYP2D6 genotype the sensitivity, specificity, positive predictive value and negative predictive were calculated.

RESULTS

The area under the ROC curve (AUC) for poor and ultrarapid metabolisers was 0.85 and 0.86, respectively. The sensitivity, specificity, positive predictive value and negative predictive value of a risperidone/9-OH-risperidone ratio >1 to CYP2D6 poor metaboliser genotype were 75 %, 95 %, 60 % and 97 %, respectively. The corresponding measures for a metabolic ratio < 0.1 to predict ultrarapid metabolisers were 80 %, 77 %, 18 % and 98 %.

CONCLUSIONS

A metabolic ratio > 1 or < 0.1 may be a useful therapeutic biomarker to recommend CYP2D6 genetic testing to guide the present or future treatment of patients in need of psychotropic drugs.