Using a psychosocial subgroup assignment to predict sickness absence in a working population with neck and back pain

<p>Abstract</p> <p>Background</p> <p>The overall objective was to evaluate the predictive validity of a subgroup classification based on the Swedish version of the MPI, the MPI-S, among gainfully employed workers with neck pain (NP) and/or low back pain (LBP) during a follow-up period of 18 and 36 months.</p> <p>Methods</p> <p>This is a prospective cohort study that is part of a larger longitudinal multi-centre study entitled Work and Health in the Process and Engineering Industries (AHA). The attempt was to classify individuals at risk for developing chronic disabling NP and LBP. This is the first study using the MPI-questionnaire in a working population with NP and LBP.</p> <p>Results</p> <p>Dysfunctional individuals (DYS) demonstrated more statistically significant sickness absence compared to adaptive copers (AC) after 36 months. DYS also had a threefold increase in the risk ratio of long-term sickness absence at 18 months. Interpersonally distressed (ID) subgroup showed overall more sickness absence compared to the AC subgroup at the 36-month follow-up and had a twofold increase in the risk ratio of long-term sickness absence at 18 months. There was a significant difference in bodily pain, mental and physical health for ID and DYS subgroups compared to the AC group at both follow-ups.</p> <p>Conclusions</p> <p>The present study shows that this multidimensional approach to the classification of individuals based on psychological and psychosocial characteristics can distinguish different groups in gainfully employed working population with NP/LBP. The results in this study confirm the predictive validity of the MPI-S subgroup classification system.</p

Collision sellar lesions: experience with eight cases and review of the literature

The concomitant presence of a pituitary adenoma with a second sellar lesion in patients operated upon for pituitary adenoma is an uncommon entity. Although rare, quite a great variety of lesions have been indentified coexisting with pituitary adenomas. In fact, most combinations have been described before, but an overview with information on the frequency of combined pathologies in a large series has not been published. We present a series of eight collision sellar lesions indentified among 548 transsphenoidally resected pituitary adenomas in two Neurosurgical Departments. The histological studies confirmed a case of sarcoidosis within a non-functioning pituitary adenoma, a case of intrasellar schwannoma coexisting with growth hormone (GH) secreting adenoma, two Rathke’s cleft cysts combined with pituitary adenomas, three gangliocytomas associated with GH-secreting adenomas, and a case of a double pituitary adenoma. The pertinent literature is discussed with emphasis on pathogenetic theories of dual sellar lesions. Although there is no direct evidence to confirm the pathogenetic relationship of collision sellar lesions, the number of cases presented in literature makes the theory of an incidental occurrence rather doubtful. Suggested hypotheses about a common embryonic origin or a potential interaction between pituitary adenomas and the immune system are presented

Overview of data-synthesis in systematic reviews of studies on outcome prediction models

Background: Many prognostic models have been developed. Different types of models, i.e. prognostic factor and outcome prediction studies, serve different purposes, which should be reflected in how the results are summarized in reviews. Therefore we set out to investigate how authors of reviews synthesize and report the results of primary outcome prediction studies. Methods: Outcome prediction reviews published in MEDLINE between October 2005 and March 2011 were eligible and 127 Systematic reviews with the aim to summarize outcome prediction studies written in English were identified for inclusion. Characteristics of the reviews and the primary studies that were included were independently assessed by 2 review authors, using standardized forms. Results: After consensus meetings a total of 50 systematic reviews that met the inclusion criteria were included. The type of primary studies included (prognostic factor or outcome prediction) was unclear in two-thirds of the reviews. A minority of the reviews reported univariable or multivariable point estimates and measures of dispersion from the primary studies. Moreover, the variables considered for outcome prediction model development were often not reported, or were unclear. In most reviews there was no information about model performance. Quantitative analysis was performed in 10 reviews, and 49 reviews assessed the primary studies qualitatively. In both analyses types a range of different methods was used to present the results of the outcome prediction studies. Conclusions: Different methods are applied to synthesize primary study results but quantitative analysis is rarely performed. The description of its objectives and of the primary studies is suboptimal and performance parameters of the outcome prediction models are rarely mentioned. The poor reporting and the wide variety of data synthesis strategies are prone to influence the conclusions of outcome prediction reviews. Therefore, there is much room for improvement in reviews of outcome prediction studies. (aut.ref.

PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS

Background: The rarity of mutations in PALB2, CHEK2 and ATM make it difficult to estimate precisely associated cancer risks. Population-based family studies have provided evidence that at least some of these mutations are associated with breast cancer risk as high as those associated with rare BRCA2 mutations. We aimed to estimate the relative risks associated with specific rare variants in PALB2, CHEK2 and ATM via a multicentre case-control study.Methods: We genotyped 10 rare mutations using the custom iCOGS array: PALB2 c.1592delT, c.2816T&gt;G and c.3113G&gt;A, CHEK2c.349A&gt;G, c.538C&gt;T, c.715G&gt;A, c.1036C&gt;T, c.1312G&gt;T, and c.1343T&gt;G and ATM c.7271T&gt;G. We assessed associations with breast cancer risk (42 671 cases and 42 164 controls), as well as prostate (22 301 cases and 22 320 controls) and ovarian (14 542 cases and 23 491 controls) cancer risk, for each variant.Results: For European women, strong evidence of association with breast cancer risk was observed for PALB2 c.1592delT OR 3.44 (95% CI 1.39 to 8.52, p=7.1×10−5), PALB2 c.3113G&gt;A OR 4.21 (95% CI 1.84 to 9.60, p=6.9×10−8) and ATM c.7271T&gt;G OR 11.0 (95% CI 1.42 to 85.7, p=0.0012). We also found evidence of association with breast cancer risk for three variants in CHEK2, c.349A&gt;G OR 2.26 (95% CI 1.29 to 3.95), c.1036C&gt;T OR 5.06 (95% CI 1.09 to 23.5) and c.538C&gt;T OR 1.33 (95% CI 1.05 to 1.67) (p≤0.017). Evidence for prostate cancer risk was observed for CHEK2 c.1343T&gt;G OR 3.03 (95% CI 1.53 to 6.03, p=0.0006) for African men and CHEK2 c.1312G&gt;T OR 2.21 (95% CI 1.06 to 4.63, p=0.030) for European men. No evidence of association with ovarian cancer was found for any of these variants.Conclusions: This report adds to accumulating evidence that at least some variants in these genes are associated with an increased risk of breast cancer that is clinically important.</p

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs

On the Origin of Event-Related Potentials Indexing Covert Attentional Selection During Visual Search

Despite nearly a century of electrophysiological studies recording extracranially from humans and intracranially from monkeys, the neural generators of nearly all human event-related potentials (ERPs) have not been definitively localized. We recorded an attention-related ERP component, known as the N2pc, simultaneously with intracranial spikes and local field potentials (LFPs) in macaques to test the hypothesis that an attentional-control structure, the frontal eye field (FEF), contributed to the generation of the macaque homologue of the N2pc (m-N2pc). While macaques performed a difficult visual search task, the search target was selected earliest by spikes from single FEF neurons, later by FEF LFPs, and latest by the m-N2pc. This neurochronometric comparison provides an empirical bridge connecting macaque and human experiments and a step toward localizing the neural generator of this important attention-related ERP component