AUC Estimates Research Articles

Population Pharmacokinetics Analysis of Quetiapine Extended-release Formulation in Japanese Patients with Bipolar Depression

PurposeThe objectives of this study were to explore covariates of plasma quetiapine concentrations after oral administration of quetiapine extended-release formulation (XR), and to examine the exposure–response relationship in Japanese patients with bipolar depression, using population pharmacokinetics (PopPK) modeling. MethodsIn a multicenter, randomized, double-blind, placebo-controlled study of quetiapine XR in patients with bipolar depression, plasma for the measurement of quetiapine concentration was collected at weeks 2, 4, 8, 12, 20, 28, and 52 during oral administration of 150 or 300 mg once daily of quetiapine XR before bedtime. A PopPK model of quetiapine XR was developed using nonlinear mixed-effects modeling with first-order conditional estimation with interactions. The exposure–response relationship was examined using post-hoc exposures. The post-hoc AUC estimate was plotted against the change in the Montgomery-Åsberg Depression Rating Scale (ΔMADRS) total score from baseline to 8 weeks following once-daily doses at 300 mg. FindingsThe final PopPK analysis dataset contained 322 patients and 1162 observations (cutoff data at week 28; cutoff date, February 2016). The plasma quetiapine concentration–time profile in patients with bipolar depression after oral administration of quetiapine XR was represented well using a 1-compartment with first-order absorption model. Covariate analysis led to the selection of γ-glutamyl transpeptidase on apparent oral clearance and body weight on apparent volume of distribution as covariates. The final population mean values of apparent oral clearance and apparent volume of distribution were 87.7 L/h and 277 L, respectively, and the interindividual %CVs were 32.6% and 75.0%, respectively. ImplicationsThe effects of covariates on PK parameters were not large compared with the interindividual variability. In addition, there was no clear relationship between the AUC and ΔMADRS. ClinicalTrials.gov identifier: NCT01725308.

Abstract C065: Breast cancer risk in U.S-born Latina women: potential role of reactive electrophiles

Abstract Background: Breast cancer incidence is lower in foreign-born Latina women compared to U.S.-born Latinas, and the risk increases with younger age at migration. It is only partially known what factors contribute to these risk patterns. Several explanations have been proposed (e.g., changes in reproductive factors, diet, or environmental pollutants), but risk remains higher in U.S.-born Latinas after adjusting for established breast cancer risk factors. The goal of the present study was to find new risk factors that could explain how the migration experience, with its concomitant environmental and lifestyle-changes, affects the risk of breast cancer. Small reactive molecules have long been suspected of causing cancer by binding DNA and causing mutations. Protein adducts result from the presence of these reactive molecules but have the advantage of being easier to measure than DNA adducts. Methods: We measured levels of a panel of 23 human serum albumin (HSA)-Cys34 adducts by high-resolution mass spectrometric methods in selected samples of Latina women from the San Francisco Bay Area Breast Cancer Study for whom serum was available (146 controls and 146 cases, 45% foreign-born). Genetic ancestry was estimated from genome-wide genotypes. To identify adducts that were predictive of foreign-born status and breast cancer diagnosis, we used bootstrapped regularized logistic regression and random forest to rank all adducts and additional covariates such as percent of Indigenous American (IA) ancestry in terms of prediction importance, and then selected variables with higher prediction importance. As a measure of the prediction accuracy of selected variables for the binary outcomes, we calculated a cross-validated area under the receiver operating curve (AUC) estimate using the set of selected adducts and covariates, as well as a corresponding 95% confidence intervals. Results and Conclusions: In a model that included foreign-born status as the dependent variable, all adducts and demographic variables, two adducts were selected as predictors: mass to charge ratio (m/z)=849.07 (addition of S2O3H) and m/z=847.10 (S-methylsulfonylation). Adduct 849.07 had an associated random forest variable importance that was considerably greater than that of any other adduct. This adduct had lower levels in foreign-born Latinas and in Latinas with high IA ancestry. Additionally, adduct 849.07 was the top-ranking variable when breast cancer status was analyzed as the dependent variable. Other variables, including body mass index (BMI), African and IA ancestry, were also selected by the regularized regression as predictive, and together all five variables had a cross-validated AUC estimate of 0.61, 95% CI=(0.55,0.68) when predicting foreign-born status. Our results suggest that adduct 849.07 may be associated with the increased risk of breast cancer observed in U.S-born vs. foreign-born Latinas. More studies are needed to replicate our findings and identify the precursor molecule(s) for this adduct. Citation Format: Silvia J. Serrano-Gómez, Courtney Schiffman, Hasmik Grigoryan, Henrik Carlsson, Sandrine Dudoit, Esther M. John, Stephen M. Rappaport, Laura Fejerman. Breast cancer risk in U.S-born Latina women: potential role of reactive electrophiles [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr C065.

A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations

Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an artificial intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal images, which could add to existing chronic kidney disease screening strategies. We used data from three population-based, multiethnic, cross-sectional studies in Singapore and China. The Singapore Epidemiology of Eye Diseases study (SEED, patients aged ≥40 years) was used to develop (5188 patients) and validate (1297 patients) the DLA. External testing was done on two independent datasets: the Singapore Prospective Study Program (SP2, 3735 patients aged ≥25 years) and the Beijing Eye Study (BES, 1538 patients aged ≥40 years). Chronic kidney disease was defined as estimated glomerular filtration rate less than 60 mL/min per 1·73m2. Three models were trained: 1) image DLA; 2) risk factors (RF) including age, sex, ethnicity, diabetes, and hypertension; and 3) hybrid DLA combining image and RF. Model performances were evaluated using the area under the receiver operating characteristic curve (AUC). In the SEED validation dataset, the AUC was 0·911 for image DLA (95% CI 0·886 -0·936), 0·916 for RF (0·891-0·941), and 0·938 for hybrid DLA (0·917-0·959). Corresponding estimates in the SP2 testing dataset were 0·733 for image DLA (95% CI 0·696-0·770), 0·829 for RF (0·797-0·861), and 0·810 for hybrid DLA (0·776-0·844); and in the BES testing dataset estimates were 0·835 for image DLA (0·767-0·903), 0·887 for RF (0·828-0·946), and 0·858 for hybrid DLA (0·794-0·922). AUC estimates were similar in subgroups of people with diabetes (image DLA 0·889 [95% CI 0·850-0·928], RF 0·899 [0·862-0·936], hybrid 0·925 [0·893-0·957]) and hypertension (image DLA 0·889 [95% CI 0·860-0·918], RF 0·889 [0·860-0·918], hybrid 0·918 [0·893-0·943]). A retinal image DLA shows good performance for estimating chronic kidney disease, underlying the feasibility of using retinal photography as an adjunctive or opportunistic screening tool for chronic kidney disease in community populations. National Medical Research Council, Singapore.

Utilization of Integrated Child Development Services in India: Programmatic Insights from National Family Health Survey, 2016.

The Integrated Child Development Services (ICDS) program launched in India in 1975 is one of the world’s largest flagship programs that aims to improve early childhood care and development via a range of healthcare, nutrition and early education services. The key to success of ICDS is in finding solutions to the historical challenges of geographic and socioeconomic inequalities in access to various services under this umbrella scheme. Using birth history data from the National Family Health Survey (Demographic and Health Survey), 2015–2016, this study presents (a) socioeconomic patterning in service uptake across rural and urban India, and (b) continuum in service utilization at three points (i.e., by mothers during pregnancy, by mothers while breastfeeding and by children aged 0–72 months) in India. We used an intersectional approach and ran a series multilevel logistic regression (random effects) models to understand patterning in utilization among mothers across socioeconomic groups. We also computed the area under the receiver operating characteristic curve (ROC-AUC) based on a logistic regression model to examine concordance between service utilization across three different points. The service utilization (any service) by mothers during pregnancy was about 20 percentage points higher for rural areas (60.5 percent; 95% CI: 60.3; 30.7) than urban areas (38.8 percent; 95% CI: 38.4; 39.1). We also found a lower uptake of services related to health and nutrition education during pregnancy (41.9 percent in rural) and early childcare (preschool) (42.4 percent). One in every two mother–child pairs did not avail any benefits from ICDS in urban areas. Estimates from random effects model revealed higher odds of utilization among schedule caste mothers from middle-class households in rural households. AUC estimates suggested a high concordance between service utilization by mothers and their children (AUC: 0.79 in rural; 0.84 in urban) implying a higher likelihood of continuum if service utilization commences at pregnancy.

Noteworthy Idiosyncrasies of 1α,25‐Dihydroxyvitamin D3 Kinetics for Extrapolation from Mouse to Man

Calcitriol or 1α,25‐dihydroxyvitamin D3 [1,25(OH)2D3] is the active ligand of the vitamin D receptor (VDR) that plays a vital role in health and disease. Vitamin D is converted to the relatively inactive metabolite, 25‐hydroxyvitamin D3 [25(OH)D3] by CYP27A1 and CYP2R1 in the liver, then to 1,25(OH)2D3 by a specific, mitochondrial enzyme, CYP27B1 (1α‐hydroxylase) that is present primarily in the kidney. Degradation of both metabolites is mostly carried out by the more ubiquitous mitochondrial enzyme, CYP24A1. Despite that calcitriol inhibits its formation and degradation, allometric scaling revealed strong interspecies (mouse, rat, dog and human) correlations of the net calcitriol clearance (CL from dose/AUC∞), production rate (PR), and basal, plasma calcitriol concentration (CBL) with body weight (BW). A closer scrutiny of the collated literature data revealed that the basal plasma concentration of calcitriol was not taken into consideration in the estimation of AUC∞ (with subtraction of CBL) and CL after exogenous calcitriol dosing in both animals and humans. This led to an overestimation of AUC∞ and underestimation of CL. After correction, the allometric equations [plots of log(CL) or log(PR) versus log(body weight) or log(BW)] were: CL=0.654xBW0.807 and PR=36.6xBW0.718. The CL of calcitriol was further influenced by diseased states, and was found to be reduced in chronic kidney disease but increased in cancer when compared to healthy humans. These changes, however, were not discerned with allometric scaling since all clinical data appeared as a cluster of closely similar values on the allometric plot. PBPK‐PD (physiologically‐based pharmacokinetic‐pharmacodynamic) modeling (Ramakrishnan et al 2016), however, confirmed the dynamic interactions between calcitriol and Cyp27b1/Cyp24a1, showing the increase in CL as well as decrease in PR in a dose‐dependent fashion for the intravenous, murine data of Quach et al (2015). The PBPK model was successful in predicting data from the escalating oral and intravenous doses of calcitriol given to cancer patients upon scale‐up.

External Validation of PATHFx Version 3.0 in Patients Treated Surgically and Nonsurgically for Symptomatic Skeletal Metastases.

PATHFx is a clinical decision-support tool based on machine learning capable of estimating the likelihood of survival after surgery for patients with skeletal metastases. The applicability of any machine-learning tool depends not only on successful external validation in unique patient populations but also on remaining relevant as more effective systemic treatments are introduced. With advancements in the treatment of metastatic disease, it is our responsibility to patients to ensure clinical support tools remain contemporary and accurate. Therefore, we sought to (1) generate updated PATHFx models using recent data from patients treated at one large, urban tertiary referral center and (2) externally validate the models using two contemporary patient populations treated either surgically or nonsurgically with external-beam radiotherapy alone for symptomatic skeletal metastases for symptomatic lesions. After obtaining institutional review board approval, we collected data on 208 patients undergoing surgical treatment for pathologic fractures at Memorial Sloan Kettering Cancer Center between 2015 and 2018. These data were combined with the original PATHFx training set (n = 189) to create the final training set (n = 397). We then created six Bayesian belief networks designed to estimate the likelihood of 1-month, 3-month, 6-month, 12-month, 18-month, and 24-month survival after treatment. Bayesian belief analysis is a statistical method that allows data-driven learning to arise from conditional probabilities by exploring relationships between variables to estimate the likelihood of an outcome using observed data. For external validation, we extracted the records of patients treated between 2016 and 2018 from the International Bone Metastasis Registry and records of patients treated nonoperatively with external-beam radiation therapy for symptomatic skeletal metastases from 2012 to 2016 using the Military Health System Data Repository (radiotherapy-only group). From each record, we collected the date of treatment, laboratory values at the time of treatment initiation, demographic data, details of diagnosis, and the date of death. All records reported sufficient follow-up to establish survival (yes/no) at 24-months after treatment. For external validation, we applied the data from each record to the new PATHFx models. We assessed calibration (calibration plots), accuracy (Brier score), discriminatory ability (area under the receiver operating characteristic curve [AUC]). The updated PATHFx version 3.0 models successfully classified survival at each time interval in both external validation sets and demonstrated appropriate discriminatory ability and model calibration. The Bayesian models were reasonably calibrated to the Memorial Sloan Kettering Cancer Center training set. External validation with 197 records from the International Bone Metastasis Registry and 192 records from the Military Health System Data Repository for analysis found Brier scores that were all less than 0.20, with upper bounds of the 95% confidence intervals all less than 0.25, both for the radiotherapy-only and International Bone Metastasis Registry groups. Additionally, AUC estimates were all greater than 0.70, with lower bounds of the 95% CI all greater than 0.68, except for the 1-month radiotherapy-only group. To complete external validation, decision curve analysis demonstrated clinical utility. This means it was better to use the PATHFx models when compared to the default assumption that all or no patients would survive at all time periods except for the 1-month models. We believe the favorable Brier scores (< 0.20) as well as DCA indicate these models are suitable for clinical use. We successfully updated PATHFx using contemporary data from patients undergoing either surgical or nonsurgical treatment for symptomatic skeletal metastases. These models have been incorporated for clinical use on PATHFx version 3.0 (https://www.pathfx.org). Clinically, external validation suggests it is better to use PATHFx version 3.0 for all time periods except when deciding whether to give radiotherapy to patients with the life expectancy of less than 1 month. This is partly because most patients survived 1-month after treatment. With the advancement of medical technology in treatment and diagnosis for patients with metastatic bone disease, part of our fiduciary responsibility is to the main current clinical support tools. Level III, therapeutic study.

A Matching-Adjusted Indirect Comparison of Nivolumab Versus Brentuximab Vedotin for Relapsed/Refractory Classical Hodgkin Lymphoma after Failure of Autologous Hematopoietic Cell Transplantation

A Matching-Adjusted Indirect Comparison of Nivolumab Versus Brentuximab Vedotin for Relapsed/Refractory Classical Hodgkin Lymphoma after Failure of Autologous Hematopoietic Cell Transplantation

Limited sampling strategy to predict the area under the curve of tacrolimus in Mexican renal transplant pediatric patients receiving Prograf® or non-innovator formulations.

TDM of tacrolimus is usually performed with trough levels (C0h ). However, in pediatric patients, C0h may not be an adequate marker. The AUC is considered a more suitable indicator of drug exposure. As several blood samples are needed for the estimation of AUC, and LSS for predicting tacrolimus AUC and optimizing the dose adjustment have been proposed. Moreover, in emerging countries such as Mexico, non-innovator formulations, which bioequivalence has not been demonstrated, are frequently used. Hence, the aim of this study was to develop and validate a LSS to predict the tacrolimus AUC0-12h in Mexican pediatric kidney transplant recipients who received either Prograf® or non-innovator tacrolimus formulations. A total of 56 pharmacokinetic profiles were randomized into two groups: model development (n=28) and model validation (n=28). The limited sampling equations were obtained after a stepwise multiple regression using AUC as the dependent variable and tacrolimus blood concentrations, quantified by CMIA, at different time points as the independent variables. The final equation included observed concentrations at 1hour (C1h ) and 4hours (C4h ) after dose administration. The predictive performance of the model was adequate in terms of both, bias and precision. Results strongly suggest that the clinical use of this LSS could provide an ethical, cost-, and time-effective method in the TDM of tacrolimus in pediatric patients with kidney transplant. The model proved to be adequate with either Prograf® or non-innovator tacrolimus formulations of dubious bioequivalence.

Bayesian receiver operating characteristic metric for linear classifiers

We propose a novel classifier accuracy metric: the Bayesian Area Under the Receiver Operating Characteristic Curve (CBAUC). The method estimates the area under the ROC curve and is related to the recently proposed Bayesian Error Estimator. The metric can assess the quality of a classifier using only the training dataset without the need for computationally expensive cross-validation. We derive a closed-form solution of the proposed accuracy metric for any linear binary classifier under the Gaussianity assumption, and study the accuracy of the proposed estimator using simulated and real-world data. These experiments confirm that the closed-form CBAUC is both faster and more accurate than conventional AUC estimators.

On the validity of time-dependent AUC estimation in the presence of cure fraction.

During the last decades, several approaches have been proposed to estimate the time-dependent area under the receiver operating characteristic curve (AUC) of risk tools derived from survival data. The validity of these estimators relies on some regularity assumptions among which a survival function being proper. In practice, this assumption is not always satisfied because a fraction of the population may not be susceptible to experience the event of interest even for long follow-up. Studying the sensitivity of the proposed estimators to the violation of this assumption is of substantial interest. In this paper, we investigate the performance of a nonparametric simple estimator, developed for classical survival data, in the case when the population exhibits a cure fraction. Motivated from the current practice of deriving risk tools in oncology and cardiovascular disease prevention, we also assess the loss, in terms of predictive performance, when deriving risk tools from survival models that do not acknowledge the presence of cure. The simulation results show that the investigated method is valid even under the presence of cure. They also show that risk tools derived from survival models that ignore the presence of cure have smaller AUC compared to those derived from survival models that acknowledge the presence of cure. This was also attested with a real data analysis from a breast cancerstudy.

Estimation of AUC for Assessing Its Significance in Classification Models

The assessment of the performance of a diagnostic test when test results are measured on continuous scale can be evaluated using the measures of sensitivity and specificity over the range of possible cut-off points for the predictor variable. This is achieved by the use of a receiver operating characteristic (ROC) curve which is a graph of sensitivity against 1-specificity across all possible decision cut-offs values from a diagnostic test result. This curve evaluates the diagnostic ability of tests to discriminate the true state of subjects especially in classification models. These tasks of assessing the predictive accuracy of classification models is always better achieved using a summary measure of accuracy across all possible ranges of cut-off values called the area under the receiver operating characteristic curve (AUC). In this paper, we propose a simple nonparametric method of calculating AUC from predicted probability of positive response involving multiple prediction rules. This method is based on the knowledge of non-parametric Mann-Whitney U statistic. Based on the predicted outcomes and observed outcomes, the performance of diagnostic tests is assessed for the classification models through the AUC calculated from these outcomes. The proposed method when applied on real data, the significance of AUC for the classification models is assessed. The method offers reliable statistical inferences and circumvents the difficulties of deriving the statistical moments of complex summary statistics seen in the parametric method. The proposed method as a non-parametric estimation is recommended for calculating the AUC as it compares favorably with the existing parametric and non-parametric methods. Keywords: Cut-off value, ROC, Predicted probability, parametric, non-parametric DOI : 10.7176/JNSR/9-9-03 Publication date :May 31 st 2019

The utility of ESR, CRP and platelets in the diagnosis of GCA

BackgroundTo compare the utility of ESR, CRP and platelets for the diagnosis of GCA.MethodA clinical diagnosis of GCA was determined by case-note review of 270 individuals (68% female, mean age 72 years) referred to a central pathology service for a temporal artery biopsy between 2011 and 2014. The highest levels of ESR, CRP and platelets (within 2 weeks of diagnosis) were documented. Evaluation of ESR, CRP and platelets for the diagnosis of GCA were compared using Receiver Operating Characteristic Area Under the Curve (ROC-AUC), and sensitivity/specificity at optimum cut-off values.ResultsGCA was clinically diagnosed in 139 (67%) patients, with 81 TAB positive. The AUC estimates for ESR, CRP and platelets were comparable (0.65 vs 0.72 vs 0.72, p = 0.08). The estimated optimal cut-off levels were confirmed at 50 mm/hour for ESR, and determined as 20 mg/L for CRP and 300 × 109/L for platelets. Sensitivity estimates for these three tests were comparable (p = 0.45) and ranged between 66% for ESR and 71% for platelets. Specificity estimates were also comparable (p = 0.11) and ranged between 57% for ESR and 68% for CRP. There was only moderate agreement between the three positive tests (agreement 67%, kappa: 0.34), and when considered collectively, CRP and platelet positive tests were independent predictors of GCA (p < 0.001), but the ESR was not (p = 0.76).ConclusionESR, CRP and platelets are moderate, equivalent diagnostic tests for GCA, but may yield disparate results in individual patients. A combination of CRP and platelet tests may provide the best diagnostic utility for GCA.

Optimal nonparametric estimator of the area under ROC curve based on clustered data

In diagnostic trials, clustered data are obtained when several subunits of the same patient are observed. Intracluster correlations need to be taken into account when analyzing such clustered data. A nonparametric method has been proposed by Obuchowski (1997) to estimate the Receiver Operating Characteristic curve area (AUC) for such clustered data. However, Obuchowski’s estimator is not efficient as it gives equal weight to all pairwise rankings within and between cluster. In this paper, we propose a more efficient nonparametric AUC estimator with two sets of optimal weights. Simulation results show that the loss of efficiency of Obuchowski’s estimator for a single AUC or the AUC difference can be substantial when there is a moderate intracluster test correlation and the cluster size is large. The efficiency gain of our weighted AUC estimator for a single AUC or the AUC difference is further illustrated using the data from a study of screening tests for neonatal hearing.

The spatial leave-pair-out cross-validation method for reliable AUC estimation of spatial classifiers

Machine learning based classification methods are widely used in geoscience applications, including mineral prospectivity mapping. Typical characteristics of the data, such as small number of positive instances, imbalanced class distributions and lack of verified negative instances make ROC analysis and cross-validation natural choices for classifier evaluation. However, recent literature has identified two sources of bias, that can affect reliability of area under ROC curve estimation via cross-validation on spatial data. The pooling procedure performed by methods such as leave-one-out can introduce a substantial negative bias to results. At the same time, spatial dependencies leading to spatial autocorrelation can result in overoptimistic results, if not corrected for. In this work, we introduce the spatial leave-pair-out cross-validation method, that corrects for both of these biases simultaneously. The methodology is used to benchmark a number of classification methods on mineral prospectivity mapping data from the Central Lapland greenstone belt. The evaluation highlights the dangers of obtaining misleading results on spatial data and demonstrates how these problems can be avoided. Further, the results show the advantages of simple linear models for this classification task.

1415. Implementation and Validation of a Vancomycin AUC/MIC Calculator and Dosing Protocol at a Large Community Hospital

BackgroundCurrent IDSA guidelines recommend targeting a vancomycin trough between 15 and 20 mg/L to achieve an AUC/MIC ≥ 400; however, recent literature shows increased rates of nephrotoxicity within this range and the target AUC/MIC ratio may be achieved with lower troughs. We sought to determine whether a vancomycin AUC-based dosing protocol and spreadsheet-based calculator, using one-compartment population-based dosing and two steady-state serum levels, accurately predicted AUC/MIC ratio in patients with moderate to severe Staphylococcus aureus infections.MethodsA retrospective analysis of 60 adult patients treated with vancomycin for culture-confirmed S. aureus was conducted. The primary outcome was percent of patients who met the AUC/MIC goal of ≥400. Secondary outcomes included: mean initial trough concentration in patients who met the AUC/MIC goal compared with those who did not; correlation of two different methods of AUC-estimation with the patient’s actual AUC; percentage of patients meeting AUC/MIC goal stratified by initial trough concentrations, age, weight, and indication; and percentage of patients meeting the AUC goal stratified by the same variables. Results were analyzed using descriptive statistics and calculations were performed using SPSS.ResultsThe median age (range) was 55 (22–86) and 52% were male. Eighty-three percent of patients achieved an initial AUC/MIC ≥ 400, including 93% with serum troughs between 10 and 14.9 mg/L. Patients who met the AUC/MIC goal had an average trough of 13.5 mg/L, whereas those who did not had an average trough of 7.8 mg/L (P < 0.001). AUC estimation using population pharmacokinetics was significantly correlated with actual AUC (P = 0.011); however, this was not a strong correlation (r = 0.340). Subgroup analysis based on age, weight, and indication identified areas for improvement in the empiric dosing protocol at our institution.ConclusionUse of the vancomycin AUC-based dosing protocol and calculator resulted in achievement of efficacy goal in the majority of patients, including many with vancomycin troughs between 10 and 15 mg/L. More accurate estimation of AUC and other pharmacokinetic variables using local patient data is important for improving the reliability of vancomycin dosing protocols. Disclosures All authors: No reported disclosures.

Identification of gene expression predictors of occupational benzene exposure.

BackgroundPreviously, using microarrays and mRNA-Sequencing (mRNA-Seq) we found that occupational exposure to a range of benzene levels perturbed gene expression in peripheral blood mononuclear cells.ObjectivesIn the current study, we sought to identify gene expression biomarkers predictive of benzene exposure below 1 part per million (ppm), the occupational standard in the U.S.MethodsFirst, we used the nCounter platform to validate altered expression of 30 genes in 33 unexposed controls and 57 subjects exposed to benzene (<1 to ≥5 ppm). Second, we used SuperLearner (SL) to identify a minimal number of genes for which altered expression could predict <1 ppm benzene exposure, in 44 subjects with a mean air benzene level of 0.55±0.248 ppm (minimum 0.203ppm).ResultsnCounter and microarray expression levels were highly correlated (coefficients >0.7, p<0.05) for 26 microarray-selected genes. nCounter and mRNA-Seq levels were poorly correlated for 4 mRNA-Seq-selected genes. Using negative binomial regression with adjustment for covariates and multiple testing, we confirmed differential expression of 23 microarray-selected genes in the entire benzene-exposed group, and 27 genes in the <1 ppm-exposed subgroup, compared with the control group. Using SL, we identified 3 pairs of genes that could predict <1 ppm benzene exposure with cross-validated AUC estimates >0.9 (p<0.0001) and were not predictive of other exposures (nickel, arsenic, smoking, stress). The predictive gene pairs are PRG2/CLEC5A, NFKBI/CLEC5A, and ACSL1/CLEC5A. They play roles in innate immunity and inflammatory responses.ConclusionsUsing nCounter and SL, we validated the altered expression of multiple mRNAs by benzene and identified gene pairs predictive of exposure to benzene at levels below the US occupational standard of 1ppm.

Value of radiographic measurements in depiction of glenohumeral and acromial anatomical changes in patients with subacromial impingement syndrome and rotator cuff tears

The purpose.Optimization of radiographic study of the shoulder in patients with SIS and estimation of the clinical value of radiographic measurements in SIS diagnosis and rotator cuff tears (RCT).Materials and methods.128 patients with a painful shoulder (67 women, 61 men), mean age 57.3 ± 12.8 y.o. and 35 patients without shoulder pain mean age 56 ± 10.4 y.o. underwent a complex study including radiography, MRI, MDCTor MR-arthrography, arthroscopy. Special angles and distances were estimated on a Radiographic Work Station by 2 radiologists (acromion tilt (AT), acromion slope, inferior acromion protrusion, acromio-humeral distance, acromio-humeral index, critical shoulder angle (CSA), lateral acromion angle, transverse acromion angle). Mean values of these parameters were compared between patients with SIS and control group, and between patients with RCT and without . Test-retest reproducibility and inter rater agreement were calculated with Kohen’s kappa. Diagnostic value of the most informative parameters was compared with estimation of AUC under the ROC curve.Results.CSA and AT demonstrated independence with age and excellent test-retest reproducibility. In SIS patients mean values of CSA were statistically higher (35.78 ± 4.64°), AT values – lower (25.90 ± 4.27°), then in controls. In all patients with CSA more then 35, the frequency of RCT was 74%. In patients with RCT were detected higher values of CSA (36.70 ± 2.61°) and lower values of AT (23.67 ± 3.07°), then in patients without tears. Lateral acromion angle (acromion type) demonstrated correlation with patient’s age and moderate reproducibility in test-retest studies.Conclusion.AT (measured on the outlet X-ray view) and CSA (measured in direct views) could be recommended for clinical use for evaluation of patients with SIS and prognosing RCT.

Junior temperament character inventory together with quantitative EEG discriminate children with attention deficit hyperactivity disorder combined subtype from children with attention deficit hyperactivity disorder combined subtype plus oppositional defiant disorder

Oppositional defiant disorder (ODD) is frequently associated with Attention Deficit Hyperactivity Disorder (ADHD) but no clear neurophysiological evidence exists that distinguishes the two groups. Our aim was to identify biomarkers that distinguish children with Attention Deficit Hyperactivity Disorder combined subtype (ADHD_C) from children with ADHD_C + ODD, by combining the results of quantitative EEG (qEEG) and the Junior Temperament Character Inventory (JTCI).28 ADHD_C and 22 ADHD_C + ODD children who met the DSMV criteria participated in the study. JTCI and EEG were analyzed. Stability based Biomarkers identification methodology was applied to the JTCI and the qEEG separately and combined. The qEEG was tested at the scalp and the sources levels. The classification power of the selected biomarkers was tested with a robust ROC technique. The best discriminant power was obtained when TCI and qEEG were analyzed together. Novelty seeking, self-directedness and cooperativeness were selected as biomarkers together with F4 and Cz in Delta; Fz and F4 in Theta and F7 and F8 in Beta, with a robust AUC of 0.95 for the ROC. At sources level: the regions were the right lateral and medial orbito-frontal cortex, cingular region, angular gyrus, right inferior occipital gyrus, occipital pole and the left insula in Theta, Alpha and Beta. The robust estimate of the total AUC was 0.91. These structures are part of extensive networks of novelty seeking, self-directedness and cooperativeness systems that seem dysregulated in these children. These methods represent an original approach to associate differences of personality and behavior to specific neuronal systems and subsystems.

Pharmacokinetics of rifampicin in adult TB patients and healthy volunteers: a systematic review and meta-analysis.

ObjectivesThe objectives of this study were to explore inter-study heterogeneity in the pharmacokinetics (PK) of orally administered rifampicin, to derive summary estimates of rifampicin PK parameters at standard dosages and to compare these with summary estimates for higher dosages. MethodsA systematic search was performed for studies of rifampicin PK published in the English language up to May 2017. Data describing the Cmax and AUC were extracted. Meta-analysis provided summary estimates for PK parameter estimates at standard rifampicin dosages. Heterogeneity was assessed by estimation of the I2 statistic and visual inspection of forest plots. Summary AUC estimates at standard and higher dosages were compared graphically and contextualized using preclinical pharmacodynamic (PD) data.ResultsSubstantial heterogeneity in PK parameters was evident and upheld in meta-regression. Treatment duration had a significant impact on the summary estimates for rifampicin PK parameters, with Cmax 8.98 mg/L (SEM 2.19) after a single dose and 5.79 mg/L (SEM 2.14) at steady-state dosing, and AUC 72.56 mg·h/L (SEM 2.60) and 38.73 mg·h/L (SEM 4.33) after single and steady-state dosing, respectively. Rifampicin dosages of at least 25 mg/kg are required to achieve plasma PK/PD targets defined in preclinical studies.ConclusionsVast inter-study heterogeneity exists in rifampicin PK parameter estimates. This is not explained by the available modifying variables. The recommended dosage of rifampicin should be increased to improve efficacy. This study provides an important point of reference for understanding rifampicin PK at standard dosages as efforts to explore higher dosing strategies continue in this field.

Estimation of Area Under the ROC Curve under nonignorable verification bias.

The Area Under the Receiving Operating Characteristic Curve (AUC) is frequently used for assessing the overall accuracy of a diagnostic marker. However, estimation of AUC relies on knowledge of the true outcomes of subjects: diseased or non-diseased. Because disease verification based on a gold standard is often expensive and/or invasive, only a limited number of patients are sent to verification at doctors' discretion. Estimation of AUC is generally biased if only small verified samples are used and it is thus necessary to make corrections for such lack of information. However, correction based on the ignorable missingness assumption (or missing at random) is also biased if the missing mechanism indeed depends on the unknown disease outcome, which is called nonignorable missing. In this paper, we propose a propensity-score-adjustment method for estimating AUC based on the instrumental variable assumption when the missingness of disease status is nonignorable. The new method makes parametric assumptions on the verification probability, and the probability of being diseased for verified samples rather than for the whole sample. The proposed parametric assumption on the observed sample is easier to be verified than the parametric assumption on the full sample. We establish the asymptotic properties of the proposed estimators. A simulation study is performed to compare the proposed method with existing methods. The proposed method is also applied to an Alzheimer's disease data collected by National Alzheimer's Coordinating Center.