Prevalence of major levator abnormalities in symptomatic patients with an underactive pelvic floor contraction

Introduction and hypothesis: Major levator ani abnormalities (LAA) may lead to abnormal pelvic floor muscle contraction (pfmC) and secondarily to stress urinary incontinence (SUI), prolapse, or fecal incontinence (FI). Methods: A retrospective observational study included 352 symptomatic patients to determine prevalence of LAA in underactive pfmC and the relationship with sympt

Three myths about risk thresholds for prediction models

Acknowledgments This work was developed as part of the international initiative of strengthening analytical thinking for observational studies (STRATOS). The objective of STRATOS is to provide accessible and accurate guidance in the design and analysis of observational studies (http://stratos-initiative.org/). Members of the STRATOS Topic Group ‘Evaluating diagnostic tests and prediction models’ are Gary Collins, Carl Moons, Ewout Steyerberg, Patrick Bossuyt, Petra Macaskill, David McLernon, Ben van Calster, and Andrew Vickers. Funding The study is supported by the Research Foundation-Flanders (FWO) project G0B4716N and Internal Funds KU Leuven (project C24/15/037). Laure Wynants is a post-doctoral fellow of the Research Foundation – Flanders (FWO). The funding bodies had no role in the design of the study, collection, analysis, interpretation of data, nor in writing the manuscript. Contributions LW and BVC conceived the original idea of the manuscript, to which ES, MVS and DML then contributed. DT acquired the data. LW analyzed the data, interpreted the results and wrote the first draft. All authors revised the work, approved the submitted version, and are accountable for the integrity and accuracy of the work.Peer reviewedPublisher PD

Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator

Introduction: Preoperative characterisation of ovarian masses into benign or malignant is of paramount importance to optimise patient management. Objectives: In this study, we developed and validated a computerised model to characterise ovarian masses as benign or malignant. Materials and methods: Transvaginal 2D B mode static ultrasound images of 187 ovarian masses with known histological diagnosis were included. Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image. A Support Vector Machine (SVM) was trained using stratified cross validation with randomised sampling. The process was repeated 15 times and in each round 100 images were randomly selected. Results: The SVM classified the original non-treated static images as benign or malignant masses with an average accuracy of 0.62 (95% CI: 0.59-0.65). This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test). Conclusion: We have shown that an SVM can classify static 2D B mode ultrasound images of ovarian masses into benign and malignant categories. The accuracy improves if texture related LBP features extracted from the images are considered

DALI: Vitamin D and lifestyle intervention for gestational diabetes mellitus (GDM) prevention: An European multicentre, randomised trial - study protocol

Background: Gestational diabetes mellitus (GDM) is an increasing problem world-wide. Lifestyle interventions and/or vitamin D supplementation might help prevent GDM in some women.Methods/design: Pregnant women at risk of GDM (BMI≥29 (kg/m2)) from 9 European countries will be invited to participate and consent obtained before 19+6 weeks of gestation. After giving informed consent, women without GDM will be included (based on IADPSG criteria: fasting glucose\u3c5.1mmol; 1 hour glucose \u3c10.0 mmol; 2 hour glucose \u3c8.5 mmol) and randomized to one of the 8 intervention arms using a 2×(2×2) factorial design: (1) healthy eating (HE), 2) physical activity (PA), 3) HE+PA, 4) control, 5) HE+PA+vitamin D, 6) HE+PA+placebo, 7) vitamin D alone, 8) placebo alone), pre-stratified for each site. In total, 880 women will be included with 110 women allocated to each arm. Between entry and 35 weeks of gestation, women allocated to a lifestyle intervention will receive 5 face-to-face, and 4 telephone coaching sessions, based on the principles of motivational interviewing. The lifestyle intervention includes a discussion about the risks of GDM, a weight gain target \u3c5kg and either 7 healthy eating \u27messages\u27 and/or 5 physical activity \u27messages\u27 depending on randomization. Fidelity is monitored by the use of a personal digital assistance (PDA) system. Participants randomized to the vitamin D intervention receive either 1600 IU vitamin D or placebo for daily intake until delivery. Data is collected at baseline measurement, at 24-28 weeks, 35-37 weeks of gestation and after delivery. Primary outcome measures are gestational weight gain, fasting glucose and insulin sensitivity, with a range of obstetric secondary outcome measures including birth weight.Discussion: DALI is a unique Europe-wide randomised controlled trial, which will gain insight into preventive measures against the development of GDM in overweight and obese women. © 2013 Jelsma et al.; licensee BioMed Central Ltd

Improved modeling of clinical data with kernel methods

Objective: Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters. Methods: When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data. Results: The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies. Conclusion: For clinical data consisting of variables of different types, the proposed kernel function which takes into account the type and range of each variable - has shown to be a better alternative for linear and non-linear classification problems. (C) 2011 Elsevier B.V. All rights reserved

Clinical and ultrasound characteristics of surgically removed adnexal lesions with largest diameter ≤ 2.5 cm: a pictorial essay

Objectives: To describe the ultrasound characteristics, indications for surgery and histological diagnoses of surgically removed adnexal masses with a largest diameter of ≤ 2.5 cm (very small tumors), to estimate the sensitivity and specificity of diagnosis of malignancy by subjective assessment of ultrasound images of very small tumors and to present a collection of ultrasound images of surgically removed very small tumors, with emphasis on those causing diagnostic difficulty. Methods: Information on surgically removed adnexal tumors with a largest diameter of ≤ 2.5 cm was retrieved from the ultrasound databases of seven participating centers. The ultrasound images were described using the International Ovarian Tumor Analysis terminology. The original diagnosis, based on subjective assessment of the ultrasound images by the ultrasound examiner, was used to calculate the sensitivity and specificity of diagnosis of malignancy. Results: Of the 129 identified adnexal masses with largest diameter ≤ 2.5 cm, 104 (81%) were benign, 15 (12%) borderline malignant and 10 (8%) invasive tumors. The main indication for performing surgery was suspicion of malignancy in 22% (23/104) of the benign tumors and in all 25 malignant tumors. None of the malignant tumors was a unilocular cyst (vs 50% of the benign tumors), all malignancies contained solid components (vs 43% of the benign tumors), 80% of the borderline tumors had papillary projections (vs 21% of the benign tumors and 20% of the invasive malignancies) and all invasive tumors and 80% of the borderline tumors were vascularized on color/power Doppler examination (vs 44% of the benign tumors). The ovarian crescent sign was present in 85% of the benign tumors, 80% of the borderline tumors and 50% of the invasive malignancies. The sensitivity of diagnosis of malignancy by subjective assessment of ultrasound images was 100% (25/25) and the specificity was 86% (89/104). Excluding unilocular cysts, the specificity was 71% (37/52). Analysis of images illustrated the difficulty in distinguishing benign from borderline very small cysts with papillations and benign from malignant very small well vascularized (color score 3 or 4) solid adnexal tumors. Conclusions: Very small malignant tumors manifest generally accepted ultrasound signs of malignancy. Small unilocular cysts are usually benign, while small non-unilocular masses, particularly ones with solid components, incur a risk of malignancy and pose a clinical dilemma

Does ignoring clustering in multicenter data influence the performance of prediction models? A simulation study

Clinical risk prediction models are increasingly being developed and validated on multicenter datasets. In this article, we present a comprehensive framework for the evaluation of the predictive performance of prediction models at the center level and the population level, considering population-averaged predictions, center-specific predictions, and predictions assuming an average random center effect. We demonstrated in a simulation study that calibration slopes do not only deviate from one because of over- or underfitting of patterns in the development dataset, but also as a result of the choice of the model (standard versus mixed effects logistic regression), the type of predictions (marginal versus conditional versus assuming an average random effect), and the level of model validation (center versus population). In particular, when data is heavily clustered (ICC 20%), center-specific predictions offer the best predictive performance at the population level and the center level. We recommend that models should reflect the data structure, while the level of model validation should reflect the research question

Changing predictor measurement procedures affected the performance of prediction models in clinical examples

Objectives: The aim of this study was to quantify the impact of predictor measurement heterogeneity on prediction model performance. Predictor measurement heterogeneity refers to variation in the measurement of predictor(s) between the derivation of a prediction model and its validation or application. It arises, for instance, when predictors are measured using different measurement instruments or protocols. Study Design and Setting: We examined the effects of various scenarios of predictor measurement heterogeneity in r

Screening for data clustering in multicenter studies: the residual intraclass correlation

status: publishe

Beliefs, barriers and preferences of European overweight women to adopt a healthier lifestyle in pregnancy to minimize risk of developing gestational diabetes mellitus: an explorative study

Introduction: Overweight and obese women are at high risk of developing gestational diabetes mellitus (GDM). Lifestyle programs might help curb the GDM risk. We explored beliefs, perceived barriers and preferences regarding lifestyle changes among overweight European pregnant women to help inform the development of future high quality lifestyle interventions. Methods: An explorative mixed methods, two-staged study was conducted to gather information from pregnant European women (BMI≥25kg/m2). In three European countries (Belgium, Netherlands, United Kingdom) interviews were conducted, followed by questionnaires in six other European countries (Austria, Denmark, Ireland, Italy, Poland, Spain). Content analysis, descriptive and chi square statistics were applied (p&#60;0.05). Results: Women preferred to obtain detailed information about their personal risk. The health of their baby was major motivating factor. Perceived barriers for physical activity included pregnancy-specific issues such as tiredness and experiencing physical complaints. Insufficient time was a barrier more frequently reported by women with children. Abstaining from snacking was identified as a challenge for the majority of women, especially for those without children. Women preferred to obtain support from their partner, as well as health professionals and valued flexible lifestyle programs. Conclusions: Healthcare professionals need to inform overweight pregnant women about their personal risk, discuss lifestyle modification and assist in weight management. Lifestyle programs should be tailored to the individual, taking into account barriers experienced by overweight first-time mothers and multipara women