Utilization of the validated Psoriasis Epidemiology Screening Tool to identify signs and symptoms of psoriatic arthritis among those with psoriasis: a cross-sectional analysis from the US-based Corrona Psoriasis Registry.

BACKGROUND: Despite increasing awareness of the disease, rates of undiagnosed psoriatic arthritis (PsA) are high in patients with psoriasis (PsO). The validated Psoriasis Epidemiology Screening Tool (PEST) is a five-item questionnaire developed to help identify PsA at an early stage. OBJECTIVES: To assess the risk of possible undiagnosed PsA among patients with PsO and characterize patients based on PEST scores. METHODS: This study included all patients enrolled in the Corrona PsO Registry with data on all five PEST questions. Demographics, clinical characteristics and patient-reported outcomes were compared in Corrona PsO Registry patients with PEST scores ≥3 and <3 using t-tests for continuous variables and chi-squared tests for categorical variables; scores ≥3 may indicate PsA. RESULTS: Of 1516 patients with PsO, 904 did not have dermatologist-reported PsA; 112 of these 904 patients (12.4%) scored ≥3 and were significantly older, female, less likely to be working, and had higher BMI than patients with scores <3. They also had significantly longer PsO duration, were more likely to have nail PsO and had worse health status, pain, fatigue, Dermatology Life Quality Index and activity impairment. CONCLUSIONS: Improved PsA screening is needed in patients with PsO because the validated PEST identified over one-tenth of registry patients who were not noted to have PsA as having scores ≥3, who could have had undiagnosed PsA. Appropriate, earlier care is important because these patients were more likely to have nail PsO, worse health-related quality of life and worse activity impairment.

Persistence of low disease activity after tumour necrosis factor inhibitor (TNFi) discontinuation in patients with psoriatic arthritis.

OBJECTIVE: To determine the duration of clinical benefit among patients with psoriatic arthritis (PsA) discontinuing tumour necrosis factor inhibitor (TNFi) therapy while in low disease activity (LDA), and to identify patient characteristics associated with prolonged clinical benefit. METHODS: We performed an observational cohort study assessing patients with PsA from the Consortium of Rheumatology Researchers of North America (CORRONA) registry who had discontinued TNFi after achieving LDA, defined as clinical disease activity index (CDAI) score ≤10 and physician's global assessment (PGA) of skin psoriasis ≤20/100. Kaplan-Meier method was used to estimate the duration of clinical benefit. RESULTS: Of the 5945 patients with PsA in CORRONA, 302 patients had discontinued TNFi (n=325) while in LDA and had follow-up data available. At time of discontinuation, mean PsA duration was 9.8 years, mean CDAI was 3.9, and mean duration of TNFi use was 1.5 years; 52.6% of patients had discontinued their first TNFi. Median time to loss of benefit was 29.2 months. 179 (55.1%) patients had persistent benefit at their previous clinic visit. An increased risk of losing clinical benefit was seen among patients with higher disease activity at discontinuation (CDAI≥3.2 vs <3.2; HR 1.43 (p=0.32)) and among smokers (HR 1.78 (p=0.027)). CONCLUSIONS: Patients with PsA who achieve LDA may maintain clinical benefit after discontinuation of TNFi therapy.

Derivation and internal validation of an expanded cardiovascular risk prediction score for rheumatoid arthritis: a Consortium of Rheumatology Researchers of North America Registry Study.

OBJECTIVE: Cardiovascular disease (CVD) is the leading cause of mortality in rheumatoid arthritis (RA), but CV risk prediction scores derived from the general population do not accurately predict CV risk in RA patients. The goal of these analyses was to develop and internally validate an expanded CV risk prediction score for RA. METHODS: Study participants were patients with RA and no known CVD from the Consortium of Rheumatology Researchers of North America registry. Two-thirds of the cohort were used to derive the CV risk prediction score, and one-third for internal validation. Traditional CV risk factors were included in the base Cox regression model, and RA-related variables were assessed in an expanded model predicting confirmed CV events. Fit and utility of the expanded model were evaluated. RESULTS: The study cohort included 23,605 RA patients with 437 CV events over a median followup of 2.2 years. The RA variables found to be significant in the regression models and included in the expanded risk model were disease activity (Clinical Disease Activity Index >10 versus ≤10), disability (modified Health Assessment Questionnaire disability index >0.5 versus ≤0.5), daily prednisone use (any versus none), and disease duration (≥10 years versus <10 years). The expanded model had good fit (Hosmer-Lemeshow goodness of fit P = 0.94) and a lower Akaike's information criterion than the base model. In the internal validation cohort, the c-statistic for model discrimination was significantly improved from the base model to the expanded model (from 0.7261 to 0.7609; P = 0.0104). The net reclassification index of CV risk in models using a 4-category CV risk prediction tool was 40% (95% confidence interval 37-44%). CONCLUSION: This newly developed, expanded risk score for CV outcomes in RA performs well and improves the classification of CV risk in comparison to a risk prediction score in which only traditional risk factors were included.

A randomized trial on the efficacy of methylphenidate and modafinil for improving cognitive functioning and symptoms in patients with a primary brain tumor.

Limited research is available regarding the efficacy of psychostimulants in treating cognitive function in primary brain tumor patients. An open-label, randomized, pilot trial examined both the general and differential efficacy of 4 weeks of methylphenidate (MPH) and modafinil (MOD) in 24 brain tumor patients. Participants completed cognitive tests and self-report measures of fatigue, sleep disturbance, mood and quality of life at baseline and after 4 weeks.Following stimulant treatment, there was evidence of a beneficial effect on test performance in speed of processing and executive function requiring divided attention. Patients with the greatest deficit in executive function at baseline appeared to derive the greatest benefit following stimulant therapy. Inconsistent, differential effects were found on a measure of attention in favor of MPH and on a measure of processing speed in favor of MOD. There was also evidence of a general beneficial effect on patient-reported measures of fatigue, mood, and quality of life, with no statistically significant differences between treatment arms in these measures over time. The results from this small pilot study should be interpreted with caution, but appear to warrant additional research, in larger study samples, targeting fatigue, processing speed and executive function, and exploring different doses of stimulants. Future studies may also wish to explore the specific patient factors that may be associated with responsiveness to psychostimulant treatment.

Comparison of discriminatory power and accuracy of three lung cancer risk models.

BACKGROUND: Three lung cancer (LC) models have recently been constructed to predict an individual's absolute risk of LC within a defined period. Given their potential application in prevention strategies, a comparison of their accuracy in an independent population is important. METHODS: We used data for 3197 patients with LC and 1703 cancer-free controls recruited to an ongoing case-control study at the Harvard School of Public Health and Massachusetts General Hospital. We estimated the 5-year LC risk for each risk model and compared the discriminatory power, accuracy, and clinical utility of these models. RESULTS: Overall, the Liverpool Lung Project (LLP) and Spitz models had comparable discriminatory power (0.69), whereas the Bach model had significantly lower power (0.66; P=0.02). Positive predictive values were highest with the Spitz models, whereas negative predictive values were highest with the LLP model. The Spitz and Bach models had lower sensitivity but better specificity than did the LLP model. CONCLUSION: We observed modest differences in discriminatory power among the three LC risk models, but discriminatory powers were moderate at best, highlighting the difficulty in developing effective risk models.

Evaluating a New Risk Marker's Predictive Contribution in Survival Models.

Although the area under the receiver operating characteristic (ROC) curve (AUC) is the most popular measure of the performance of prediction models, it has limitations, especially when it is used to evaluate the added discrimination of a new risk marker in an existing risk model. Pencina et al. (2008) proposed two indices, the net reclassification improvement (NRI) and integrated discrimination improvement (IDI), to supplement the improvement in the AUC (IAUC). Their NRI and IDI are based on binary outcomes in case-control settings, which do not involve time-to-event outcome. However, many disease outcomes are time-dependent and the onset time can be censored. Measuring discrimination potential of a prognostic marker without considering time to event can lead to biased estimates. In this paper, we extended the NRI and IDI to time-to-event settings and derived the corresponding sample estimators and asymptotic tests. Simulation studies showed that the time-dependent NRI and IDI have better performance than Pencina's NRI and IDI for measuring the improved discriminatory power of a new risk marker in prognostic survival models.

Decreasing the cancer burden in developing countries: concerns and recommendations.

Although the close of the 20th century witnessed advances in cancer detection and treatment, cancer morbidity and mortality rates steadily increase across the globe within the 21st century. The majority of this cancer burden can be found in underdeveloped and developing countries. A growing concern can be seen regarding this issue, with the research community as well as governmental and non-governmental organizations considering efforts that need to be developed and implemented. In this article, we propose several strategies to reduce cancer burden in developing countries that involve not only governmental and non-governmental organizations in such developing countries but also the research community. Such measures may prove helpful in gaining a better understanding of cancer burden and assist in clinical decision making and the design of prevention strategies for developing countries.

Perception among upper middle class adolescent in Bombay regarding sex and sexuality.

The study was carried out among the adolescents in respect to their beliefs about sexual behavior and their intended decision with regard to engaging in sexual activity. Both male and female respondents indicated that they believe that individuals of their age should wait until they are older before engaging in sexual activity. However, there were significant differences between the responses of male and female adolescents.

Effect of Box-Cox transformation on power of Haseman-Elston and maximum-likelihood variance components tests to detect quantitative trait Loci.

Non-normality of the phenotypic distribution can affect power to detect quantitative trait loci in sib pair studies. Previously, we observed that Winsorizing the sib pair phenotypes increased the power of quantitative trait locus (QTL) detection for both Haseman-Elston (HE) least-squares tests [Hum Hered 2002;53:59-67] and maximum likelihood-based variance components (MLVC) analysis [Behav Genet (in press)]. Winsorizing the phenotypes led to a slight increase in type 1 error in H-E tests and a slight decrease in type I error for MLVC analysis. Herein, we considered transforming the sib pair phenotypes using the Box-Cox family of transformations. Data were simulated for normal and non-normal (skewed and kurtic) distributions. Phenotypic values were replaced by Box-Cox transformed values. Twenty thousand replications were performed for three H-E tests of linkage and the likelihood ratio test (LRT), the Wald test and other robust versions based on the MLVC method. We calculated the relative nominal inflation rate as the ratio of observed empirical type 1 error divided by the set alpha level (5, 1 and 0.1% alpha levels). MLVC tests applied to non-normal data had inflated type I errors (rate ratio greater than 1.0), which were controlled best by Box-Cox transformation and to a lesser degree by Winsorizing. For example, for non-transformed, skewed phenotypes (derived from a chi2 distribution with 2 degrees of freedom), the rates of empirical type 1 error with respect to set alpha level=0.01 were 0.80, 4.35 and 7.33 for the original H-E test, LRT and Wald test, respectively. For the same alpha level=0.01, these rates were 1.12, 3.095 and 4.088 after Winsorizing and 0.723, 1.195 and 1.905 after Box-Cox transformation. Winsorizing reduced inflated error rates for the leptokurtic distribution (derived from a Laplace distribution with mean 0 and variance 8). Further, power (adjusted for empirical type 1 error) at the 0.01 alpha level ranged from 4.7 to 17.3% across all tests using the non-transformed, skewed phenotypes, from 7.5 to 20.1% after Winsorizing and from 12.6 to 33.2% after Box-Cox transformation. Likewise, power (adjusted for empirical type 1 error) using leptokurtic phenotypes at the 0.01 alpha level ranged from 4.4 to 12.5% across all tests with no transformation, from 7 to 19.2% after Winsorizing and from 4.5 to 13.8% after Box-Cox transformation. Thus the Box-Cox transformation apparently provided the best type 1 error control and maximal power among the procedures we considered for analyzing a non-normal, skewed distribution (chi2) while Winzorizing worked best for the non-normal, kurtic distribution (Laplace). We repeated the same simulations using a larger sample size (200 sib pairs) and found similar results.

Assessing linkage of immunoglobulin E using a meta-analysis approach.

We report results using a new meta-analysis procedure to assess linkage of immunoglobulin E (IgE), an asthma related quantitative trait, using the nine data sets provided by the Genetic Analysis Workshop 12. This meta-analysis procedure combines univariate Haseman-Elston statistics across studies gaining strength by collating possibly distinct marker maps. We performed univariate Haseman-Elston over all data sets and identified linkage (p < 0.05) to some marker in at least one study on virtually every chromosome. Using the proposed meta-analysis procedure, we detected suggestive linkage (p-value < 7.4 x 10(-4), [Lander and Kruglyak, Nat Genet 11:214-47, 1995]) for two regions on chromosome 4 (around 50 and 150 cM) and one region on chromosome 11 (around 125 cM). We also identified areas which were evocative (p-value < 0.02) for linkage: chr 5-40 cM, chr 7-98 cM, chr 9-65 cM, chr 13-110 cM, chr 16-10 cM and approximately 105 cM, chr 17-70 cM, and chr 20-25 cM.

Meta-analysis by combining parameter estimates: simulated linkage studies.

Several meta-analytic techniques have been developed for combining information from multiple studies in contexts other than linkage detection. We apply the technique of combining parameter estimates to the problem of finding disease loci in the simulated data and compare results with those obtained by reanalyzing pooled raw data. To facilitate the combination of study results, we highly recommend that parameter estimates and their standard errors be reported in published studies. If different research groups were to make original data available, progress toward disease gene location and characterization may be more quickly made.

Meta-analysis by combining p-values: simulated linkage studies.

Meta-analysis has been little explored to make an overall assessment of linkage from different studies. In practice, it is likely that published linkage studies will only report p-values. We compared the performance of the widely used Fisher method for combining p-values with that of pooling raw data. More loci were consistently found by pooling raw data. In the absence of further information, combining p-values can provide an overall, but limited, assessment of different linkage studies. However, meta-analysis would be better viewed as a preliminary step toward the goal of analyzing the pooled raw data.