A Diagnostic Algorithm Based on a Simple Clinical Prediction Rule for the Diagnosis of Cranial Giant Cell Arteritis.

Background: Risk stratification based on pre-test probability may improve the diagnostic accuracy of temporal artery high-resolution compression sonography (hrTCS) in the diagnostic workup of cranial giant cell arteritis (cGCA).

Methods: A logistic regression model with candidate items was derived from a cohort of patients with suspected cGCA ( = 87). The diagnostic accuracy of the model was tested in the derivation cohort and in an independent validation cohort ( = 114) by receiver operator characteristics (ROC) analysis.

Embolic Protection in Complex Femoropopliteal Interventions: Safety, Efficacy and Predictors of Filter Macroembolization.

Objectives: To evaluate the safety and efficacy of a filter embolic protection device (FEPD) in endovascular interventions of the femoropopliteal arteries.

Methods: Patients who underwent endovascular interventions of the femoropopliteal arteries between 2008 and 2016 and in whom the SpiderFX FEPD was applied were included in this retrospective study. Clinical and angiographic characteristics, filter macroembolization (FME), device-related complications, distal embolization, as well as the early clinical and hemodynamic outcome, were assessed.

Ocular ischaemic complications in giant cell arteritis: CHADS2-score predicts risk of permanent visual impairment.

Objectives: To identify independent risk factors for permanent visual loss (PVL) in patients with giant cell arteritis (GCA), with a special focus on sonographic findings of the temporal, carotid and subclavian/axillary arteries, and on established scoring systems of ischaemia risk assessment.

Methods: Consecutive patients with a diagnosis of GCA between 2002 and 2013 were retrospectively identified from a prospectively maintained database. Data on clinical characteristics including ophthalmological findings, laboratory values, and sonographic findings of the temporal, carotid an axillary arteries were extracted.

The impact of different sources of heterogeneity on loss of accuracy from genomic prediction models.

Cross-study validation (CSV) of prediction models is an alternative to traditional cross-validation (CV) in domains where multiple comparable datasets are available. Although many studies have noted potential sources of heterogeneity in genomic studies, to our knowledge none have systematically investigated their intertwined impacts on prediction accuracy across studies. We employ a hybrid parametric/non-parametric bootstrap method to realistically simulate publicly available compendia of microarray, RNA-seq, and whole metagenome shotgun microbiome studies of health outcomes.

Improving cross-study prediction through addon batch effect adjustment or addon normalization.

Motivation: To date most medical tests derived by applying classification methods to high-dimensional molecular data are hardly used in clinical practice. This is partly because the prediction error resulting when applying them to external data is usually much higher than internal error as evaluated through within-study validation procedures. We suggest the use of addon normalization and addon batch effect removal techniques in this context to reduce systematic differences between external data and the original dataset with the aim to improve prediction performance.

A measure of the impact of CV incompleteness on prediction error estimation with application to PCA and normalization.

Background: In applications of supervised statistical learning in the biomedical field it is necessary to assess the prediction error of the respective prediction rules. Often, data preparation steps are performed on the dataset-in its entirety-before training/test set based prediction error estimation by cross-validation (CV)-an approach referred to as "incomplete CV". Whether incomplete CV can result in an optimistically biased error estimate depends on the data preparation step under consideration.

Shear bond strength of two adhesives to bovine dentin contaminated with various astringents.

Purpose: To investigate the in vitro shear bond strength of two adhesives to bovine dentin contaminated with various astringents.

Methods: 120 adult bovine incisors were collected and cut to obtain 240 specimens. The specimens were randomly divided into a self-etch adhesive group (N = 120) and a total-etch adhesive group (N = 120).

Impact of cranial and axillary/subclavian artery involvement by color duplex sonography on response to treatment in giant cell arteritis.

Objective: Color duplex sonography (CDS) today is broadly used in the diagnostic workup of patients with suspected cranial or extracranial giant cell arteritis (GCA). This study aimed to determine the prognostic impact of the disease pattern assessed by CDS on the treatment response in GCA.

Methods: This was a retrospective, longitudinal follow-up study of 43 patients who were diagnosed with GCA at our institution between 2002 and 2010.

Cross-study validation for the assessment of prediction algorithms.

Motivation: Numerous competing algorithms for prediction in high-dimensional settings have been developed in the statistical and machine-learning literature. Learning algorithms and the prediction models they generate are typically evaluated on the basis of cross-validation error estimates in a few exemplary datasets. However, in most applications, the ultimate goal of prediction modeling is to provide accurate predictions for independent samples obtained in different settings.

Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer.

Background: Ovarian cancer is the fifth most common cause of cancer deaths in women in the United States. Numerous gene signatures of patient prognosis have been proposed, but diverse data and methods make these difficult to compare or use in a clinically meaningful way. We sought to identify successful published prognostic gene signatures through systematic validation using public data.

Correcting the optimal resampling-based error rate by estimating the error rate of wrapper algorithms.

High-dimensional binary classification tasks, for example, the classification of microarray samples into normal and cancer tissues, usually involve a tuning parameter. By reporting the performance of the best tuning parameter value only, over-optimistic prediction errors are obtained. For correcting this tuning bias, we develop a new method which is based on a decomposition of the unconditional error rate involving the tuning procedure, that is, we estimate the error rate of wrapper algorithms as introduced in the context of internal cross-validation (ICV) by Varma and Simon (2006, BMC Bioinformatics 7, 91).

Impact of the postthrombotic syndrome on quality of life after primary upper extremity deep venous thrombosis.

Background: To determine the impact of the postthrombotic syndrome (PTS) on quality of life after primary upper extremity deep venous thrombosis (UEDVT).

Patients And Methods: Twenty-five patients with a history of primary UEDVT, treated with anticoagulation alone, and twenty healthy controls were retrospectively identified and prospectively assessed for health-related quality of life (SF-36 and VEINES-QOL-questionnaire) and upper extremity functional impairment (DASH-score). Presence of PTS was classified according to the modified Villalta-score.