A primary care, multi-disciplinary disease management program for opioid-treated patients with chronic non-cancer pain and a high burden of psychiatric comorbidity

BACKGROUND: Chronic non-cancer pain is a common problem that is often accompanied by psychiatric comorbidity and disability. The effectiveness of a multi-disciplinary pain management program was tested in a 3 month before and after trial. METHODS: Providers in an academic general medicine clinic referred patients with chronic non-cancer pain for participation in a program that combined the skills of internists, clinical pharmacists, and a psychiatrist. Patients were either receiving opioids or being considered for opioid therapy. The intervention consisted of structured clinical assessments, monthly follow-up, pain contracts, medication titration, and psychiatric consultation. Pain, mood, and function were assessed at baseline and 3 months using the Brief Pain Inventory (BPI), the Center for Epidemiological Studies-Depression Scale scale (CESD) and the Pain Disability Index (PDI). Patients were monitored for substance misuse. RESULTS: Eighty-five patients were enrolled. Mean age was 51 years, 60% were male, 78% were Caucasian, and 93% were receiving opioids. Baseline average pain was 6.5 on an 11 point scale. The average CESD score was 24.0, and the mean PDI score was 47.0. Sixty-three patients (73%) completed 3 month follow-up. Fifteen withdrew from the program after identification of substance misuse. Among those completing 3 month follow-up, the average pain score improved to 5.5 (p = 0.003). The mean PDI score improved to 39.3 (p < 0.001). Mean CESD score was reduced to 18.0 (p < 0.001), and the proportion of depressed patients fell from 79% to 54% (p = 0.003). Substance misuse was identified in 27 patients (32%). CONCLUSIONS: A primary care disease management program improved pain, depression, and disability scores over three months in a cohort of opioid-treated patients with chronic non-cancer pain. Substance misuse and depression were common, and many patients who had substance misuse identified left the program when they were no longer prescribed opioids. Effective care of patients with chronic pain should include rigorous assessment and treatment of these comorbid disorders and intensive efforts to insure follow up

Major depression, fibromyalgia and labour force participation: A population-based cross-sectional study

BACKGROUND: Previous studies have documented an elevated frequency of depressive symptoms and disorders in fibromyalgia, but have not examined the association between this comorbidity and occupational status. The purpose of this study was to describe these epidemiological associations using a national probability sample. METHODS: Data from iteration 1.1 of the Canadian Community Health Survey (CCHS) were used. The CCHS 1.1 was a large-scale national general health survey. The prevalence of major depression in subjects reporting that they had been diagnosed with fibromyalgia by a health professional was estimated, and then stratified by demographic variables. Logistic regression models predicting labour force participation were also examined. RESULTS: The annual prevalence of major depression was three times higher in subjects with fibromyalgia: 22.2% (95% CI 19.4 – 24.9), than in those without this condition: 7.2% (95% CI 7.0 – 7.4). The association persisted despite stratification for demographic variables. Logistic regression models predicting labour force participation indicated that both conditions had an independent (negative) effect on labour force participation. CONCLUSION: Fibromyalgia and major depression commonly co-occur and may be related to each other at a pathophysiological level. However, each syndrome is independently and negatively associated with labour force participation. A strength of this study is that it was conducted in a large probability sample from the general population. The main limitations are its cross-sectional nature, and its reliance on self-reported diagnoses of fibromyalgia

Early reduction in painful physical symptoms is associated with improvements in long-term depression outcomes in patients treated with duloxetine

<p>Abstract</p> <p>Background</p> <p>To investigate the association of the change of painful physical symptoms (PPS) after 4 weeks, with the 6-month treatment outcomes of depressive symptoms in patients treated with duloxetine in clinical practice.</p> <p>Methods</p> <p>Multicenter, prospective, 6-month, non-interventional study in adult outpatients with a depressive episode and starting treatment with duloxetine. Depression severity was assessed by the clinician (Inventory for Depressive Symptomatology [IDS-C]) and patient (Kurz-Skala Stimmung/Aktivierung [KUSTA]). Somatic symptoms and PPS were assessed using the patient-rated Somatic Symptom Inventory (SSI) and visual analog scales (VAS) for pain items. Association of change in PPS with outcomes of depressive symptoms was analyzed based on mean KUSTA scores (mean of items mood, activity, tension/relaxation, sleep) and achievement of a 50% reduction in the total IDS-C score after 6 months using linear and logistic regression models, respectively.</p> <p>Results</p> <p>Of the 4,517 patients enrolled (mean age: 52.2 years, 71.8% female), 3,320 patients (73.5%) completed the study. 80% of the patients had moderate to severe overall pain (VAS > 30 mm) at baseline. A 50% VAS overall pain reduction after 4 weeks was associated with a 13.32 points higher mean KUSTA score after 6 months, and a 50% pain reduction after 2 weeks with a 6.33 points improvement. No unexpected safety signals were detected in this naturalistic study.</p> <p>Conclusion</p> <p>Pain reduction after 2 and 4 weeks can be used to estimate outcomes of long-term treatment with duloxetine. PPS associated with depression have a potential role in predicting remission of depressive symptoms in clinical practice.</p

Thinking dispositions for teaching : enabling and supporting resilience in context

Preparing pre-teachers for an increasingly challenging teaching profession is a complex work and requires teacher educators to engage in the careful design of both programmes and professional learning opportunities. This chapter explores how an explicit focus on thinking dispositions that enable effective teaching are developed in a Master of Teaching (Secondary) programme. This programme, delivered on-site at a secondary school, included carefully constructed teaching opportunities to support development of thinking dispositions. Ways of thinking and the impact they have on feelings, actions and beliefs will be examined along with how the implementation of our thinking dispositions framework supports the development of resilience in challenging teaching and learning contexts

Dynamic changes in gene expression in vivo predict prognosis of tamoxifen-treated patients with breast cancer

Introduction: Tamoxifen is the most widely prescribed anti-estrogen treatment for patients with estrogen receptor (ER)-positive breast cancer. However, there is still a need for biomarkers that reliably predict endocrine sensitivity in breast cancers and these may well be expressed in a dynamic manner. Methods: In this study we assessed gene expression changes at multiple time points (days 1, 2, 4, 7, 14) after tamoxifen treatment in the ER-positive ZR-75-1 xenograft model that displays significant changes in apoptosis, proliferation and angiogenesis within 2 days of therapy. Results: Hierarchical clustering identified six time-related gene expression patterns, which separated into three groups: two with early/transient responses, two with continuous/late responses and two with variable response patterns. The early/transient response represented reductions in many genes that are involved in cell cycle and proliferation (e.g. BUB1B, CCNA2, CDKN3, MKI67, UBE2C), whereas the continuous/late changed genes represented the more classical estrogen response genes (e.g. TFF1, TFF3, IGFBP5). Genes and the proteins they encode were confirmed to have similar temporal patterns of expression in vitro and in vivo and correlated with reduction in tumour volume in primary breast cancer. The profiles of genes that were most differentially expressed on days 2, 4 and 7 following treatment were able to predict prognosis, whereas those most changed on days 1 and 14 were not, in four tamoxifen treated datasets representing a total of 404 patients. Conclusions: Both early/transient/proliferation response genes and continuous/late/estrogen-response genes are able to predict prognosis of primary breast tumours in a dynamic manner. Temporal expression of therapy-response genes is clearly an important factor in characterising the response to endocrine therapy in breast tumours which has significant implications for the timing of biopsies in neoadjuvant biomarker studies.Publisher PDFPeer reviewe

The projection score - an evaluation criterion for variable subset selection in PCA visualization

<p>Abstract</p> <p>Background</p> <p>In many scientific domains, it is becoming increasingly common to collect high-dimensional data sets, often with an exploratory aim, to generate new and relevant hypotheses. The exploratory perspective often makes statistically guided visualization methods, such as Principal Component Analysis (PCA), the methods of choice. However, the clarity of the obtained visualizations, and thereby the potential to use them to formulate relevant hypotheses, may be confounded by the presence of the many non-informative variables. For microarray data, more easily interpretable visualizations are often obtained by filtering the variable set, for example by removing the variables with the smallest variances or by only including the variables most highly related to a specific response. The resulting visualization may depend heavily on the inclusion criterion, that is, effectively the number of retained variables. To our knowledge, there exists no objective method for determining the optimal inclusion criterion in the context of visualization.</p> <p>Results</p> <p>We present the projection score, which is a straightforward, intuitively appealing measure of the informativeness of a variable subset with respect to PCA visualization. This measure can be universally applied to find suitable inclusion criteria for any type of variable filtering. We apply the presented measure to find optimal variable subsets for different filtering methods in both microarray data sets and synthetic data sets. We note also that the projection score can be applied in general contexts, to compare the informativeness of any variable subsets with respect to visualization by PCA.</p> <p>Conclusions</p> <p>We conclude that the projection score provides an easily interpretable and universally applicable measure of the informativeness of a variable subset with respect to visualization by PCA, that can be used to systematically find the most interpretable PCA visualization in practical exploratory analysis.</p

Backward walking training improves balance in school-aged boys

<p>Abstract</p> <p>Background</p> <p>Falls remain a major cause of childhood morbidity and mortality. It is suggested that backward walking (BW) may offer some benefits especially in balance and motor control ability beyond those experienced through forward walking (FW), and may be a potential intervention for prevention of falls. The objective of this study was to investigate the effects of BW on balance in boys.</p> <p>Methods</p> <p>Sixteen healthy boys (age: 7.19 ± 0.40 y) were randomly assigned to either an experimental or a control group. The experimental group participated in a BW training program (12-week, 2 times weekly, and 25-min each time) but not the control group. Both groups had five dynamic balance assessments with a Biodex Stability System (anterior/posterior, medial/lateral, and overall balance index) before, during and after the training (week- 0, 4, 8, 12, 24). Six control and six experimental boys participated in a study comparing kinematics of lower limbs between FW and BW after the training (week-12).</p> <p>Results</p> <p>The balance of experimental group was better than that of control group after 8 weeks of training (<it>P </it>< 0.01), and was still better than that of control group (<it>P </it>< 0.05), when the BW training program had finished for 12 weeks. The kinematic analysis indicated that there was no difference between control and experimental groups in the kinematics of both FW and BW gaits after the BW training (<it>P </it>> 0.05). Compared to FW, the duration of stance phase of BW tended to be longer, while the swing phase, stride length, walking speed, and moving ranges of the thigh, calf and foot of BW decreased (<it>P </it>< 0.01).</p> <p>Conclusion</p> <p>Backward walking training in school-aged boys can improve balance.</p

Improving the Efficiency of Physical Examination Services

The objective of our project was to improve the efficiency of the physical examination screening service of a large hospital system. We began with a detailed simulation model to explore the relationships between four performance measures and three decision factors. We then attempted to identify the optimal physician inquiry starting time by solving a goal-programming problem, where the objective function includes multiple goals. One of our simulation results shows that the proposed optimal physician inquiry starting time decreased patient wait times by 50% without increasing overall physician utilization

Integrated Analysis of Multiple Microarray Datasets Identifies a Reproducible Survival Predictor in Ovarian Cancer

BACKGROUND: Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival. METHODOLOGY/PRINCIPAL FINDINGS: Four microarray datasets from different institutions comprising 265 advanced stage tumors were uniformly reprocessed into a single training dataset, also adjusting for inter-laboratory variation ("batch-effect"). Supervised principal component survival analysis was employed to identify prognostic models. Models were independently validated in a 61-patient cohort using a custom array genechip and a publicly available 229-array dataset. Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping. Previously established molecular phenotypes in the 2(nd) validation set were correlated with high and low-risk outcome groups. Functional representational and pathway analysis was used to explore gene networks associated with high and low risk phenotypes. A 19-gene model showed optimal performance in the training set (median OS 31 and 78 months, p < 0.01), 1(st) validation set (median OS 32 months versus not-yet-reached, p = 0.026) and 2(nd) validation set (median OS 43 versus 61 months, p = 0.013) maintaining independent prognostic power in multivariate analysis. There was strong molecular correspondence of the respective high- and low-risk tumors between training and 1(st) validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2(nd) validation set. CONCLUSIONS/SIGNIFICANCE: Integration of previously generated cancer microarray datasets may lead to robust and widely applicable survival predictors. These predictors are not simply a compilation of prognostic genes but appear to track true molecular phenotypes of good- and poor-outcome

Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli