Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection

Purpose: To develop a new three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) data analysis method using a machine learning technique based on variable-size super pixel segmentation that efficiently utilizes full 3D dataset to improve the discrimination between early glaucomatous and healthy eyes. Methods: 192 eyes of 96 subjects (44 healthy, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with SD-OCT. Each SD-OCT cube dataset was first converted into 2D feature map based on retinal nerve fiber layer (RNFL) segmentation and then divided into various number of super pixels. Unlike the conventional super pixel having a fixed number of points, this newly developed variable-size super pixel is defined as a cluster of homogeneous adjacent pixels with variable size, shape and number. Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes. For discriminating performance assessment, area under the curve (AUC) of the receiver operating characteristics of the machine classifier outputs were compared with the conventional circumpapillary RNFL (cpRNFL) thickness measurements. Results: The super pixel analysis showed statistically significantly higher AUC than the cpRNFL (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminated from healthy, while no significant difference was found when confirmed glaucoma eyes were discriminated from healthy eyes. Conclusions: A novel 3D OCT analysis technique performed at least as well as the cpRNFL in glaucoma discrimination and even better at glaucoma suspect discrimination. This new method has the potential to improve early detection of glaucomatous damage. © 2013 Xu et al

On the probability of cost-effectiveness using data from randomized clinical trials

BACKGROUND: Acceptability curves have been proposed for quantifying the probability that a treatment under investigation in a clinical trial is cost-effective. Various definitions and estimation methods have been proposed. Loosely speaking, all the definitions, Bayesian or otherwise, relate to the probability that the treatment under consideration is cost-effective as a function of the value placed on a unit of effectiveness. These definitions are, in fact, expressions of the certainty with which the current evidence would lead us to believe that the treatment under consideration is cost-effective, and are dependent on the amount of evidence (i.e. sample size). METHODS: An alternative for quantifying the probability that the treatment under consideration is cost-effective, which is independent of sample size, is proposed. RESULTS: Non-parametric methods are given for point and interval estimation. In addition, these methods provide a non-parametric estimator and confidence interval for the incremental cost-effectiveness ratio. An example is provided. CONCLUSIONS: The proposed parameter for quantifying the probability that a new therapy is cost-effective is superior to the acceptability curve because it is not sample size dependent and because it can be interpreted as the proportion of patients who would benefit if given the new therapy. Non-parametric methods are used to estimate the parameter and its variance, providing the appropriate confidence intervals and test of hypothesis

Centre selection for clinical trials and the generalisability of results: a mixed methods study.

BACKGROUND: The rationale for centre selection in randomised controlled trials (RCTs) is often unclear but may have important implications for the generalisability of trial results. The aims of this study were to evaluate the factors which currently influence centre selection in RCTs and consider how generalisability considerations inform current and optimal practice. METHODS AND FINDINGS: Mixed methods approach consisting of a systematic review and meta-summary of centre selection criteria reported in RCT protocols funded by the UK National Institute of Health Research (NIHR) initiated between January 2005-January 2012; and an online survey on the topic of current and optimal centre selection, distributed to professionals in the 48 UK Clinical Trials Units and 10 NIHR Research Design Services. The survey design was informed by the systematic review and by two focus groups conducted with trialists at the Birmingham Centre for Clinical Trials. 129 trial protocols were included in the systematic review, with a total target sample size in excess of 317,000 participants. The meta-summary identified 53 unique centre selection criteria. 78 protocols (60%) provided at least one criterion for centre selection, but only 31 (24%) protocols explicitly acknowledged generalisability. This is consistent with the survey findings (n = 70), where less than a third of participants reported generalisability as a key driver of centre selection in current practice. This contrasts with trialists' views on optimal practice, where generalisability in terms of clinical practice, population characteristics and economic results were prime considerations for 60% (n = 42), 57% (n = 40) and 46% (n = 32) of respondents, respectively. CONCLUSIONS: Centres are rarely enrolled in RCTs with an explicit view to external validity, although trialists acknowledge that incorporating generalisability in centre selection should ideally be more prominent. There is a need to operationalize 'generalisability' and incorporate it at the design stage of RCTs so that results are readily transferable to 'real world' practice

Predicting live birth, preterm and low birth weight infant after in-vitro fertilisation: a prospective study of 144018 treatment cycles

Background The extent to which baseline couple characteristics affect the probability of live birth and adverse perinatal outcomes after assisted conception is unknown. Methods and Findings We utilised the Human Fertilisation and Embryology Authority database to examine the predictors of live birth in all in vitro fertilisation (IVF) cycles undertaken in the UK between 2003 and 2007 (n = 144,018). We examined the potential clinical utility of a validated model that pre-dated the introduction of intracytoplasmic sperm injection (ICSI) as compared to a novel model. For those treatment cycles that resulted in a live singleton birth (n = 24,226), we determined the associates of potential risk factors with preterm birth, low birth weight, and macrosomia. The overall rate of at least one live birth was 23.4 per 100 cycles (95% confidence interval [CI] 23.2–23.7). In multivariable models the odds of at least one live birth decreased with increasing maternal age, increasing duration of infertility, a greater number of previously unsuccessful IVF treatments, use of own oocytes, necessity for a second or third treatment cycle, or if it was not unexplained infertility. The association of own versus donor oocyte with reduced odds of live birth strengthened with increasing age of the mother. A previous IVF live birth increased the odds of future success (OR 1.58, 95% CI 1.46–1.71) more than that of a previous spontaneous live birth (OR 1.19, 95% CI 0.99–1.24); p-value for difference in estimate <0.001. Use of ICSI increased the odds of live birth, and male causes of infertility were associated with reduced odds of live birth only in couples who had not received ICSI. Prediction of live birth was feasible with moderate discrimination and excellent calibration; calibration was markedly improved in the novel compared to the established model. Preterm birth and low birth weight were increased if oocyte donation was required and ICSI was not used. Risk of macrosomia increased with advancing maternal age and a history of previous live births. Infertility due to cervical problems was associated with increased odds of all three outcomes—preterm birth, low birth weight, and macrosomia. Conclusions Pending external validation, our results show that couple- and treatment-specific factors can be used to provide infertile couples with an accurate assessment of whether they have low or high risk of a successful outcome following IVF

Pelvic organ prolapse symptoms in relation to POPQ, ordinal stages and ultrasound prolapse assessment

Adequate staging of pelvic organ prolapse is important in clinical practice and research. The ability of the POPQ, ordinal stages and ultrasound prolapse assessment were evaluated for their ability to discriminate between women with and without prolapse symptoms. The leading edge of the predominant compartment in the three assessment systems was used for the calculation of receiver operating characteristics curves. Two hundred and sixty five (265) consecutive women were evaluated. The area under the receiver operating characteristics curve for the three staging systems ranged from 0.715 to 0.783. POPQ staging and ordinal staging performed equally well in the prediction of prolapse symptoms (p = 0.780), and both performed better as compared with ultrasound prolapse assessment (p = 0.048 and p = 0.015, respectively). Prolapse staging can equally be performed by the POPQ and ordinal stages systems as far as the discrimination between women with and without prolapse symptoms is concerned. The ultrasound prolapse assessment does not perform better as compared with these two systems

Glucosylsphingosine Is a Highly Sensitive and Specific Biomarker for Primary Diagnostic and Follow-Up Monitoring in Gaucher Disease in a Non-Jewish, Caucasian Cohort of Gaucher Disease Patients

Gaucher disease (GD) is the most common lysosomal storage disorder (LSD). Based on a deficient β-glucocerebrosidase it leads to an accumulation of glucosylceramide. Standard diagnostic procedures include measurement of enzyme activity, genetic testing as well as analysis of chitotriosidase and CCL18/PARC as biomarkers. Even though chitotriosidase is the most well-established biomarker in GD, it is not specific for GD. Furthermore, it may be false negative in a significant percentage of GD patients due to mutation. Additionally, chitotriosidase reflects the changes in the course of the disease belatedly. This further enhances the need for a reliable biomarker, especially for the monitoring of the disease and the impact of potential treatments.Here, we evaluated the sensitivity and specificity of the previously reported biomarker Glucosylsphingosine with regard to different control groups (healthy control vs. GD carriers vs. other LSDs).Only GD patients displayed elevated levels of Glucosylsphingosine higher than 12 ng/ml whereas the comparison controls groups revealed concentrations below the pathological cut-off, verifying the specificity of Glucosylsphingosine as a biomarker for GD. In addition, we evaluated the biomarker before and during enzyme replacement therapy (ERT) in 19 patients, demonstrating a decrease in Glucosylsphingosine over time with the most pronounced reduction within the first 6 months of ERT. Furthermore, our data reveals a correlation between the medical consequence of specific mutations and Glucosylsphingosine.In summary, Glucosylsphingosine is a very promising, reliable and specific biomarker for GD

What do we know and when do we know it?

Two essential aspects of virtual screening are considered: experimental design and performance metrics. In the design of any retrospective virtual screen, choices have to be made as to the purpose of the exercise. Is the goal to compare methods? Is the interest in a particular type of target or all targets? Are we simulating a ‘real-world’ setting, or teasing out distinguishing features of a method? What are the confidence limits for the results? What should be reported in a publication? In particular, what criteria should be used to decide between different performance metrics? Comparing the field of molecular modeling to other endeavors, such as medical statistics, criminology, or computer hardware evaluation indicates some clear directions. Taken together these suggest the modeling field has a long way to go to provide effective assessment of its approaches, either to itself or to a broader audience, but that there are no technical reasons why progress cannot be made

Body Size Measurements as Predictors of Type 2 Diabetes in Aboriginal People

OBJECTIVE: To investigate waist circumference, waist-to-hip ratio, body mass index (BMI), weight and hip circumference as risk factors for type 2 diabetes in Aboriginal Australians. DESIGN: Community-based cross-sectional study. SUBJECTS: In total, 915 Australian Aboriginal adults (age: 18-74 y) from a remote Aboriginal community in the Northern Territory of Australia. MEASUREMENTS: Body size measurements included waist circumference, waist-to-hip ratio, BMI, weight and hip circumference. Diabetes status was determined according to medical history and fasting and 2-h postload plasma glucose values. Logistic regression was used to calculate odds ratio for diabetes associated with 1 standard deviation (s.d.) increase in a body size measurement. The areas under the ROC curves of five body size measurements were calculated and compared. RESULTS: Risk of diabetes increased with increasing levels of body size. ORs (95% CI) for diabetes with adjustment for age and sex were 2.16 (1.75, 2.66), 1.80 (1.49, 2.17), 1.41 (1.17, 1.71), 1.81 (1.51, 2.19) and 1.84 (1.50, 2.24) associated with 1 s.d. increase in waist circumference, BMI, weight, waist-to-hip ratio, and hip circumference, respectively. The area under the ROC curve for waist circumference was significantly higher than those for other measurements. CONCLUSION: Waist circumference is the best body size measurement in predicting diabetes in Aboriginal people

Refining and testing the diagnostic accuracy of an assessment tool (PAT-POPS) to predict admission and discharge of children and young people who attend an emergency department : protocol for an observational study

Background: Increasing attendances by children (aged 0–16 years) to United Kingdom Emergency Departments (EDs) challenges patient safety within the National Health Service (NHS) with health professionals required to make complex judgements on whether children attending urgent and emergency care services can be sent home safely or require admission. Health regulation bodies have recommended that an early identification systems should be developed to recognise children developing critical illnesses. The Pennine Acute Hospitals NHS Trust Paediatric Observation Priority Score (PAT-POPS) was developed as an ED-specific tool for this purpose. This study aims to revise and improve the existing tool and determine its utility in determining safe admission and discharge decision making. Methods/design: An observational study to improve diagnostic accuracy using data from children and young people attending the ED and Urgent Care Centre (UCC) at three hospitals over a 12 month period. The data being collected is part of routine practice; therefore opt-out methods of consent will be used. The reference standard is admission or discharge. A revised PAT-POPs scoring tool will be developed using clinically guided logistic regression models to explore which components best predict hospital admission and safe discharge. Suitable cut-points for safe admission and discharge will be established using sensitivity and specificity as judged by an expert consensus meeting. The diagnostic accuracy of the revised tool will be assessed, and it will be compared to the former version of PAT-POPS using ROC analysis. Discussion: This new predictive tool will aid discharge and admission decision-making in relation to children and young people in hospital urgent and emergency care facilities. Trial registration: NIHR RfPB Grant: PB-PG-0815-20034. ClinicalTrials.gov: 213469. Retrospectively registered on 11 April 2018. Keywords: Paediatric, Emergency department, Diagnostic accuracy, Early identification systems, screening tool, Observational, Early warning score, Early warning system, hospital admission