Accuracy of immunological criteria for identifying virological failure in children on antiretroviral therapy - The IeDEA Southern Africa Collaboration

Objectives  To determine the diagnostic accuracy of World Health Organization (WHO) 2010 and 2006 as well as United States Department of Health and Human Services (DHHS) 2008 definitions of immunological failure for identifying virological failure (VF) in children on antiretroviral therapy (ART). Methods  Analysis of data from children (<16 years at ART initiation) at South African ART sites at which CD4 count/per cent and HIV-RNA monitoring are performed 6-monthly. Incomplete virological suppression (IVS) was defined as failure to achieve ≥1 HIV-RNA ≤400 copies/ml between 6 and 15 months on ART and viral rebound (VR) as confirmed HIV-RNA ≥5000 copies/ml in a child on ART for ≥18 months who had achieved suppression during the first year on treatment. Results  Among 3115 children [median (interquartile range) age 48 (20-84) months at ART initiation] on treatment for ≥1 year, sensitivity of immunological criteria for IVS was 10%, 6% and 26% for WHO 2006, WHO 2010 and DHHS 2008 criteria, respectively. The corresponding positive predictive values (PPV) were 31%, 20% and 20%. Diagnostic accuracy for VR was determined in 2513 children with ≥18 months of follow-up and virological suppression during the first year on ART with sensitivity of 5% (WHO 2006/2010) and 27% (DHHS 2008). PPV results were 42% (WHO 2010), 43% (WHO 2006) and 20% (DHHS 2008). Conclusion  Current immunological criteria are unable to correctly identify children failing ART virologically. Improved access to viral load testing is needed to reliably identify VF in children.

Influence of time point of calibration on accuracy of continuous glucose monitoring in individuals with type 1 diabetes

Abstract Background and Aims: Data on the influence of calibration on accuracy of continuous glucose monitoring (CGM) are scarce. The aim of the present study was to investigate whether the time point of calibration has an influence on sensor accuracy and whether this effect differs according to glycemic level. Subjects and Methods: Two CGM sensors were inserted simultaneously in the abdomen on either side of 20 individuals with type 1 diabetes. One sensor was calibrated predominantly using preprandial glucose (calibration(PRE)). The other sensor was calibrated predominantly using postprandial glucose (calibration(POST)). At minimum three additional glucose values per day were obtained for analysis of accuracy. Sensor readings were divided into four categories according to the glycemic range of the reference values (low, ≤4 mmol/L; euglycemic, 4.1-7 mmol/L; hyperglycemic I, 7.1-14 mmol/L; and hyperglycemic II, >14 mmol/L). Results: The overall mean±SEM absolute relative difference (MARD) between capillary reference values and sensor readings was 18.3±0.8% for calibration(PRE) and 21.9±1.2% for calibration(POST) (P<0.001). MARD according to glycemic range was 47.4±6.5% (low), 17.4±1.3% (euglycemic), 15.0±0.8% (hyperglycemic I), and 17.7±1.9% (hyperglycemic II) for calibration(PRE) and 67.5±9.5% (low), 24.2±1.8% (euglycemic), 15.5±0.9% (hyperglycemic I), and 15.3±1.9% (hyperglycemic II) for calibration(POST). In the low and euglycemic ranges MARD was significantly lower in calibration(PRE) compared with calibration(POST) (P=0.007 and P<0.001, respectively). Conclusions: Sensor calibration predominantly based on preprandial glucose resulted in a significantly higher overall sensor accuracy compared with a predominantly postprandial calibration. The difference was most pronounced in the hypo- and euglycemic reference range, whereas both calibration patterns were comparable in the hyperglycemic range.

On the accuracy of Software-based Energy Estimation Techniques

This paper examines the accuracy of software-based on-line energy estimation techniques. It evaluates today’s most widespread energy estimation model in order to investigate whether the current methodology of pure software-based energy estimation running on a sensor node itself can indeed reliably and accurately determine its energy consumption - independent of the particular node instance, the traffic load the node is exposed to, or the MAC protocol the node is running. The paper enhances today’s widely used energy estimation model by integrating radio transceiver switches into the model, and proposes a methodology to find the optimal estimation model parameters. It proves by statistical validation with experimental data that the proposed model enhancement and parameter calibration methodology significantly increases the estimation accuracy.

Assessment of smoking behaviour in the dental setting. A study comparing self-reported questionnaire data and exhaled carbon monoxide levels

The present study validated the accuracy of data from a self-reported questionnaire on smoking behaviour with the use of exhaled carbon monoxide (CO) level measurements in two groups of patients. Group 1 included patients referred to an oral medicine unit, whereas group 2 was recruited from the daily outpatient service. All patients filled in a standardized questionnaire regarding their current and former smoking habits. Additionally, exhaled CO levels were measured using a monitor. A total of 121 patients were included in group 1, and 116 patients were included in group 2. The mean value of exhaled CO was 7.6 ppm in the first group and 9.2 ppm in the second group. The mean CO values did not statistically significantly differ between the two groups. The two exhaled CO level measurements taken for each patient exhibited very good correlation (Spearman's coefficient of 0.9857). Smokers had a mean difference of exhaled CO values of 13.95 ppm (p < 0.001) compared to non-smokers adjusted for the first or second group. The consumption of one additional pack year resulted in an increase in CO values of 0.16 ppm (p = 0.003). The consumption of one additional cigarette per day elevated the CO measurements by 0.88 ppm (p < 0.001). Based on these results, the correlations between the self-reported smoking habits and exhaled CO values are robust and highly reproducible. CO monitors may offer a non-invasive method to objectively assess current smoking behaviour and to monitor tobacco use cessation attempts in the dental setting.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.

High-accuracy patient-to-image registration for the facilitation of image-guided robotic microsurgery on the head

Image-guided microsurgery requires accuracies an order of magnitude higher than today's navigation systems provide. A critical step toward the achievement of such low-error requirements is a highly accurate and verified patient-to-image registration. With the aim of reducing target registration error to a level that would facilitate the use of image-guided robotic microsurgery on the rigid anatomy of the head, we have developed a semiautomatic fiducial detection technique. Automatic force-controlled localization of fiducials on the patient is achieved through the implementation of a robotic-controlled tactile search within the head of a standard surgical screw. Precise detection of the corresponding fiducials in the image data is realized using an automated model-based matching algorithm on high-resolution, isometric cone beam CT images. Verification of the registration technique on phantoms demonstrated that through the elimination of user variability, clinically relevant target registration errors of approximately 0.1 mm could be achieved.

Antimicrobial use in Swiss dairy farms: quantification and evaluation of data quality

Data on antimicrobial use play a key role in the development of policies for the containment of antimicrobial resistance. On-farm data could provide a detailed overview of the antimicrobial use, but technical and methodological aspects of data collection and interpretation, as well as data quality need to be further assessed. The aims of this study were (1) to quantify antimicrobial use in the study population using different units of measurement and contrast the results obtained, (2) to evaluate data quality of farm records on antimicrobial use, and (3) to compare data quality of different recording systems. During 1 year, data on antimicrobial use were collected from 97 dairy farms. Antimicrobial consumption was quantified using: (1) the incidence density of antimicrobial treatments; (2) the weight of active substance; (3) the used daily dose and (4) the used course dose for antimicrobials for intestinal, intrauterine and systemic use; and (5) the used unit dose, for antimicrobials for intramammary use. Data quality was evaluated by describing completeness and accuracy of the recorded information, and by comparing farmers' and veterinarians' records. Relative consumption of antimicrobials depended on the unit of measurement: used doses reflected the treatment intensity better than weight of active substance. The use of antimicrobials classified as high priority was low, although under- and overdosing were frequently observed. Electronic recording systems allowed better traceability of the animals treated. Recording drug name or dosage often resulted in incomplete or inaccurate information. Veterinarians tended to record more drugs than farmers. The integration of veterinarian and farm data would improve data quality.

Validation of Two Methods of Data Collection of Self-Reported Medicine Use Among the Elderly

The accuracy of medicine use information was compared for a telephone interview and mail questionnaire, using an in-home medicine check as the standard of assessment The validity of medicine use information varied by data source, level of specificity of data, and respondent characteristics. The mail questionnaire was the more valid source of overall medicine use information. Implications for both service providers and researchers are provided.

Accuracy of Recognition and Diagnosis of Comorbid Depression in the Nursing Home

A variety of research has documented high levels of depression among older adults in the health care setting. Additional research has shown that care providers in health care settings are not very effective at diagnosing comorbid depression.This is a troublesome finding since comorbid depression has been linked to a number of negative outcomes in older adults. Early results have indicated that comorbid depression may be associated with a number of unfavorable consequences ranging from impairments in physical functioning to increased mortality.The health care setting with arguably the highest rate of physical impairment is the nursing home and it is the nursing home where the effects of comorbid depression may be most costly. Therefore, the current analysis uses data from the Institutional Population Component of the NationalMedical Expenditure Survey (US Department of Health and Human Services, 1990) to explore rates of both recognized and unrecognized comorbid depression in the nursing home setting. Using a constructed proxy variable representative of the DSM-III-R diagnosis of depression, results indicate that approximately 8.1% of nursing home residents have an unrecognized potential comorbid depression.

Accuracy of B-type natriuretic peptide tests to exclude congestive heart failure: systematic review of test accuracy studies

BACKGROUND: Congestive heart failure (CHF) is a major public health problem. The use of B-type natriuretic peptide (BNP) tests shows promising diagnostic accuracy. Herein, we summarize the evidence on the accuracy of BNP tests in the diagnosis of CHF and compare the performance of rapid enzyme-linked immunosorbent assay (ELISA) and standard radioimmunosorbent assay (RIA) tests. METHODS: We searched electronic databases and the reference lists of included studies, and we contacted experts. Data were extracted on the study population, the type of test used, and methods. Receiver operating characteristic (ROC) plots and summary ROC curves were produced and negative likelihood ratios pooled. Random-effect meta-analysis and metaregression were used to combine data and explore sources of between-study heterogeneity. RESULTS: Nineteen studies describing 22 patient populations (9 ELISA and 13 RIA) and 9093 patients were included. The diagnosis of CHF was verified by echocardiography, radionuclide scan, or echocardiography combined with clinical criteria. The pooled negative likelihood ratio overall from random-effect meta-analysis was 0.18 (95% confidence interval [CI], 0.13-0.23). It was lower for the ELISA test (0.12; 95% CI, 0.09-0.16) than for the RIA test (0.23; 95% CI, 0.16-0.32). For a pretest probability of 20%, which is typical for patients with suspected CHF in primary care, a negative result of the ELISA test would produce a posttest probability of 2.9%; a negative RIA test, a posttest probability of 5.4%. CONCLUSIONS: The use of BNP tests to rule out CHF in primary care settings could reduce demand for echocardiography. The advantages of rapid ELISA tests need to be balanced against their higher cost.

Sample sizes of studies on diagnostic accuracy: literature survey

OBJECTIVES: To determine sample sizes in studies on diagnostic accuracy and the proportion of studies that report calculations of sample size. DESIGN: Literature survey. DATA SOURCES: All issues of eight leading journals published in 2002. METHODS: Sample sizes, number of subgroup analyses, and how often studies reported calculations of sample size were extracted. RESULTS: 43 of 8999 articles were non-screening studies on diagnostic accuracy. The median sample size was 118 (interquartile range 71-350) and the median prevalence of the target condition was 43% (27-61%). The median number of patients with the target condition--needed to calculate a test's sensitivity--was 49 (28-91). The median number of patients without the target condition--needed to determine a test's specificity--was 76 (27-209). Two of the 43 studies (5%) reported a priori calculations of sample size. Twenty articles (47%) reported results for patient subgroups. The number of subgroups ranged from two to 19 (median four). No studies reported that sample size was calculated on the basis of preplanned analyses of subgroups. CONCLUSION: Few studies on diagnostic accuracy report considerations of sample size. The number of participants in most studies on diagnostic accuracy is probably too small to analyse variability of measures of accuracy across patient subgroups.

Accuracy of magnetic resonance imaging for the diagnosis of multiple sclerosis: systematic review

OBJECTIVE: To determine the accuracy of magnetic resonance imaging criteria for the early diagnosis of multiple sclerosis in patients with suspected disease. DESIGN: Systematic review. DATA SOURCES: 12 electronic databases, citation searches, and reference lists of included studies. Review methods Studies on accuracy of diagnosis that compared magnetic resonance imaging, or diagnostic criteria incorporating such imaging, to a reference standard for the diagnosis of multiple sclerosis. RESULTS: 29 studies (18 cohort studies, 11 other designs) were included. On average, studies of other designs (mainly diagnostic case-control studies) produced higher estimated diagnostic odds ratios than did cohort studies. Among 15 studies of higher methodological quality (cohort design, clinical follow-up as reference standard), those with longer follow-up produced higher estimates of specificity and lower estimates of sensitivity. Only two such studies followed patients for more than 10 years. Even in the presence of many lesions (> 10 or > 8), magnetic resonance imaging could not accurately rule multiple sclerosis in (likelihood ratio of a positive test result 3.0 and 2.0, respectively). Similarly, the absence of lesions was of limited utility in ruling out a diagnosis of multiple sclerosis (likelihood ratio of a negative test result 0.1 and 0.5). CONCLUSIONS: Many evaluations of the accuracy of magnetic resonance imaging for the early detection of multiple sclerosis have produced inflated estimates of test performance owing to methodological weaknesses. Use of magnetic resonance imaging to confirm multiple sclerosis on the basis of a single attack of neurological dysfunction may lead to over-diagnosis and over-treatment.

A Faster Circular Binary Segmentation Algorithm for the Analysis of Array CGH Data

Motivation: Array CGH technologies enable the simultaneous measurement of DNA copy number for thousands of sites on a genome. We developed the circular binary segmentation (CBS) algorithm to divide the genome into regions of equal copy number (Olshen {\it et~al}, 2004). The algorithm tests for change-points using a maximal $t$-statistic with a permutation reference distribution to obtain the corresponding $p$-value. The number of computations required for the maximal test statistic is $O(N^2),$ where $N$ is the number of markers. This makes the full permutation approach computationally prohibitive for the newer arrays that contain tens of thousands markers and highlights the need for a faster. algorithm. Results: We present a hybrid approach to obtain the $p$-value of the test statistic in linear time. We also introduce a rule for stopping early when there is strong evidence for the presence of a change. We show through simulations that the hybrid approach provides a substantial gain in speed with only a negligible loss in accuracy and that the stopping rule further increases speed. We also present the analysis of array CGH data from a breast cancer cell line to show the impact of the new approaches on the analysis of real data. Availability: An R (R Development Core Team, 2006) version of the CBS algorithm has been implemented in the ``DNAcopy'' package of the Bioconductor project (Gentleman {\it et~al}, 2004). The proposed hybrid method for the $p$-value is available in version 1.2.1 or higher and the stopping rule for declaring a change early is available in version 1.5.1 or higher.

Classification and selection of biomarkers in genomic data using LASSO

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been on assessing univariate associations between gene expression with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve. Under a specific probability model, this leads to consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.

Quantifying and Comparing the Accuracy of Binary Biomarkers When Predicting a Failure Time Outcome

The positive and negative predictive value are standard measures used to quantify the predictive accuracy of binary biomarkers when the outcome being predicted is also binary. When the biomarkers are instead being used to predict a failure time outcome, there is no standard way of quantifying predictive accuracy. We propose a natural extension of the traditional predictive values to accommodate censored survival data. We discuss not only quantifying predictive accuracy using these extended predictive values, but also rigorously comparing the accuracy of two biomarkers in terms of their predictive values. Using a marginal regression framework, we describe how to estimate differences in predictive accuracy and how to test whether the observed difference is statistically significant.