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To project national hepatitis C virus (HCV) burden, unbiased estimation of HCV progression to liver cirrhosis is required for the whole community of HCV-infected individuals. However, widely varying estimates of progression rates to cirrhosis have been produced. This disparity is partly associated with the statistical methods applied, but is mainly due to the differing types of study cohort. We use an inverse probability weighted estimation method to recover the true parameters for the (Weibull regression) model that determines the incubation period from infection to cirrhosis for the community of HCV-infected individuals, when there is cirrhosis-related recruitment bias to the studied cohort. We apply the method to simulated data for a liver clinic which attracts patients from a community of 1000 HCV-infected individuals under different event-biased referral patterns. We investigate how well the method performs in recovering the true community parameters, and then apply it to Edinburgh Royal Infirmary's liver clinic series. The results obtained are compared to those from a Weibull survival analysis which ignores the selection bias. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To investigate whether there are differential effects of disease activity and damage on physical functioning as measured by the Health Assessment Questionnaire (HAQ) over the course of psoriatic arthritis (PsA). METHODS: Between June 1993 and March 2005, 382 patients attending the University of Toronto PsA clinic had completed >/=2 HAQs on an annual basis. At the time of each HAQ assessment, clinical and laboratory measures of disease activity and damage were recorded. Generalized linear mixed-effects models were used to investigate the longitudinal relationship between disease activity, damage, and the HAQ score. To avoid floor effects that would arise in a single mixed-effects model, we adopted a 2-part model. RESULTS: The number of actively inflamed joints (measure of disease activity) and the number of clinically deformed joints (measure of damage) were positively and significantly related to the HAQ score. Furthermore, interaction terms for illness duration with the number of actively inflamed joints were statistically significant, with or without inclusion of the erythrocyte sedimentation rate and morning stiffness in the model (P = 0.029 and P < 0.001, respectively). The positive effects of actively inflamed joints on the level of the HAQ score decreased over increasing duration of PsA. There was less evidence to suggest that the positive effect of joint damage on the HAQ score increased over time. CONCLUSION: Our results support the view that the influence of disease activity on HAQ scores declines with increased disease duration. We could not demonstrate strong evidence that the effect of clinical damage increases over the course of illness. [ABSTRACT FROM AUTHOR]

The identification of sets of co-regulated genes that share a common function is a key question of modern genomics. Bayesian profile regression is a semi-supervised mixture modelling approach that makes use of a response to guide inference toward relevant clusterings. Previous applications of profile regression have considered univariate continuous, categorical, and count outcomes. In this work, we extend Bayesian profile regression to cases where the outcome is longitudinal (or multivariate continuous) and provide PReMiuMlongi, an updated version of PReMiuM, the R package for profile regression. We consider multivariate normal and Gaussian process regression response models and provide proof of principle applications to four simulation studies. The model is applied on budding yeast data to identify groups of genes co-regulated during the Saccharomyces cerevisiae cell cycle. We identify 4 distinct groups of genes associated with specific patterns of gene expression trajectories, along with the bound transcriptional factors, likely involved in their co-regulation process.

We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to intensive care unit, the probability of death in hospital for patients before and after intensive care unit admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. We compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, we find that some groups appear to be at very low risk of some events, in particular intensive care unit admission, and these are best represented by using 'cure-rate' models to define transition-specific hazards. We provide general-purpose software to implement all the models we describe in the flexsurv R package, which allows arbitrarily flexible distributions to be used to represent the cause-specific hazards or times to events.

There is a growing interest in precision medicine where individual heterogeneity is incorporated into decision-making and treatments are tailored to individuals to provide better healthcare. One important aspect of precision medicine is the estimation of the optimal individualized treatment rule (ITR) that optimizes the expected outcome. Most methods developed for this purpose are restricted to the setting with two treatments, while clinical studies with more than two treatments are common in practice. In this work, we summarize methods to estimate the optimal ITR in the multi-arm setting and compare their performance in large-scale clinical trials via simulation studies. We then illustrate their utilities with a case study using the data from the INTERVAL trial, which randomly assigned over 20,000 male blood donors from England to one of the three inter-donation intervals (12-week, 10-week, and eight-week) over two years. We estimate the optimal individualized donation strategies under three different objectives. Our findings are fairly consistent across five different approaches that are applied: when we target the maximization of the total units of blood collected, almost all donors are assigned to the eight-week inter-donation interval, whereas if we aim at minimizing the low hemoglobin deferral rates, almost all donors are assigned to donate every 12 weeks. However, when the goal is to maximize the utility score that "discounts" the total units of blood collected by the incidences of low hemoglobin deferrals, we observe some heterogeneity in the optimal inter-donation interval across donors and the optimal donor assignment strategy is highly dependent on the trade-off parameter in the utility function.

Background: Recruitment is often a bottleneck in secondary prevention trials in Alzheimer disease (AD). Furthermore, screen-failure rates in these trials are typically high due to relatively low prevalence of AD pathology in individuals without dementia, especially among cognitively unimpaired. Prescreening on AD risk factors may facilitate recruitment, but the efficiency will depend on how these factors link to participation rates and AD pathology. We investigated whether common AD-related factors predict trial-ready cohort participation and amyloid status across different prescreen settings., Methods: We monitored the prescreening in four cohorts linked to the European Prevention of Alzheimer Dementia (EPAD) Registry (n = 16,877; mean ± SD age = 64 ± 8 years). These included a clinical cohort, a research in-person cohort, a research online cohort, and a population-based cohort. Individuals were asked to participate in the EPAD longitudinal cohort study (EPAD-LCS), which serves as a trial-ready cohort for secondary prevention trials. Amyloid positivity was measured in cerebrospinal fluid as part of the EPAD-LCS assessment. We calculated participation rates and numbers needed to prescreen (NNPS) per participant that was amyloid-positive. We tested if age, sex, education level, APOE status, family history for dementia, memory complaints or memory scores, previously collected in these cohorts, could predict participation and amyloid status., Results: A total of 2595 participants were contacted for participation in the EPAD-LCS. Participation rates varied by setting between 3 and 59%. The NNPS were 6.9 (clinical cohort), 7.5 (research in-person cohort), 8.4 (research online cohort), and 88.5 (population-based cohort). Participation in the EPAD-LCS (n = 413 (16%)) was associated with lower age (odds ratio (OR) age = 0.97 [0.95-0.99]), high education (OR = 1.64 [1.23-2.17]), male sex (OR = 1.56 [1.19-2.04]), and positive family history of dementia (OR = 1.66 [1.19-2.31]). Among participants in the EPAD-LCS, amyloid positivity (33%) was associated with higher age (OR = 1.06 [1.02-1.10]) and APOE ɛ4 allele carriership (OR = 2.99 [1.81-4.94]). These results were similar across prescreen settings., Conclusions: Numbers needed to prescreen varied greatly between settings. Understanding how common AD risk factors link to study participation and amyloid positivity is informative for recruitment strategy of studies on secondary prevention of AD.

Several researchers have described two-part models with patient-specific stochastic processes for analysing longitudinal semicontinuous data. In theory, such models can offer greater flexibility than the standard two-part model with patient-specific random effects. However, in practice, the high dimensional integrations involved in the marginal likelihood (i.e. integrated over the stochastic processes) significantly complicates model fitting. Thus, non-standard computationally intensive procedures based on simulating the marginal likelihood have so far only been proposed. In this paper, we describe an efficient method of implementation by demonstrating how the high dimensional integrations involved in the marginal likelihood can be computed efficiently. Specifically, by using a property of the multivariate normal distribution and the standard marginal cumulative distribution function identity, we transform the marginal likelihood so that the high dimensional integrations are contained in the cumulative distribution function of a multivariate normal distribution, which can then be efficiently evaluated. Hence, maximum likelihood estimation can be used to obtain parameter estimates and asymptotic standard errors (from the observed information matrix) of model parameters. We describe our proposed efficient implementation procedure for the standard two-part model parameterisation and when it is of interest to directly model the overall marginal mean. The methodology is applied on a psoriatic arthritis data set concerning functional disability.

In psoriatic arthritis, many patients do not develop permanent joint damage even after a prolonged follow-up. This has led several authors to consider the possibility of a subpopulation of stayers (those who do not have the propensity to experience the event of interest), as opposed to assuming the entire population consist of movers (those who have the propensity to experience the event of interest). In addition, it is recognised that the damaged joints process may act very differently across different joint areas, particularly the hands, feet and large joints. From a clinical perspective, interest lies in identifying possible relationships between the damaged joints processes in these joint areas for the movers and estimating the proportion of stayers in these joint areas, if they exist. For this purpose, this paper proposes a novel trivariate mover-stayer model consisting of mover-stayer truncated negative binomial margins, and patient-level dynamic covariates and random effects in the models for the movers and stayers, respectively. The model is then extended to have a two-level mover-stayer structure for its margins so that the nature of the stayer property can be investigated. A particularly attractive feature of the proposed models is that only an optimisation routine is required in their model fitting procedures. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd., (©2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.)

For semi-continuous data which are a mixture of true zeros and continuously distributed positive values, the use of two-part mixed models provides a convenient modelling framework. However, deriving population-averaged (marginal) effects from such models is not always straightforward. Su et al. presented a model that provided convenient estimation of marginal effects for the logistic component of the two-part model but the specification of marginal effects for the continuous part of the model presented in that paper was based on an incorrect formulation. We present a corrected formulation and additionally explore the use of the two-part model for inferences on the overall marginal mean, which may be of more practical relevance in our application and more generally., (© The Author(s) 2013.)

Background: Most reported outcome measures in rheumatoid arthritis (RA) trials are composite, whose components comprise single measures that are combined into one outcome. The aims of this review were to assess the range of missing data rates in primary composite outcomes and to document the current practice for handling and reporting missing data in published RA trials compared to the Consolidated Standards of Reporting Trials (CONSORT) recommendations., Methods: A systematic search for randomised controlled trials was conducted for RA trials published between 2008 and 2013 in four rheumatology and four high impact general medical journals., Results: A total of 51 trials with a composite primary outcome were identified, of which 38 (75 %) used the binary American College of Rheumatology responder index and 13 (25 %) used the Disease Activity Score for 28 joints (DAS28). Forty-four trials (86 %) reported on an intention-to-treat analysis population, while 7 trials (14 %) analysed according to a modified intention-to-treat population. Missing data rates for the primary composite outcome ranged from 2-53 % and were above 30 % in 9 trials, 20-30 % in 11 trials, 10-20 % in 18 trials and below 10 % in 13 trials. Thirty-eight trials (75 %) used non-responder imputation and 10 (20 %) used last observation carried forward to impute missing composite outcome data at the primary time point. The rate of dropout was on average 61 % times higher in the placebo group compared to the treatment group in the 34 placebo controlled trials (relative rate 1.61, 95 % CI: 1.29, 2.02). Thirty-seven trials (73 %) did not report the use of sensitivity analyses to assess the handling of missing data in the primary analysis as recommended by CONSORT guidelines., Conclusions: This review highlights an improvement in rheumatology trial practice since the revision of CONSORT guidelines, in terms of power calculation and participant's flow diagram. However, there is a need to improve the handling and reporting of missing composite outcome data and their components in RA trials. In particular, sensitivity analyses need to be more widely used in RA trials because imputation is widespread and generally uses single imputation methods, and in this area the missing data rates are commonly differentially higher in the placebo group.

In longitudinal randomised trials and observational studies within a medical context, a composite outcome-which is a function of several individual patient-specific outcomes-may be felt to best represent the outcome of interest. As in other contexts, missing data on patient outcome, due to patient drop-out or for other reasons, may pose a problem. Multiple imputation is a widely used method for handling missing data, but its use for composite outcomes has been seldom discussed. Whilst standard multiple imputation methodology can be used directly for the composite outcome, the distribution of a composite outcome may be of a complicated form and perhaps not amenable to statistical modelling. We compare direct multiple imputation of a composite outcome with separate imputation of the components of a composite outcome. We consider two imputation approaches. One approach involves modelling each component of a composite outcome using standard likelihood-based models. The other approach is to use linear increments methods. A linear increments approach can provide an appealing alternative as assumptions concerning both the missingness structure within the data and the imputation models are different from the standard likelihood-based approach. We compare both approaches using simulation studies and data from a randomised trial on early rheumatoid arthritis patients. Results suggest that both approaches are comparable and that for each, separate imputation offers some improvement on the direct imputation of a composite outcome.

Objective: We previously developed and performed an initial validation of a screening questionnaire, the Toronto Psoriatic Arthritis Screen (ToPAS), for psoriatic arthritis (PsA). In our original analysis, we found that the index constructed appeared to discriminate well between those with a confirmed diagnosis of PsA and those without PsA in various clinical settings. However, it was suggested that ToPAS would benefit from additional refinement to the questions and the scoring system, because items pertaining to axial involvement were not included in our original index. Subsequently, a second version of ToPAS was developed, ToPAS 2, which incorporated the suggested refinements. We aimed to validate ToPAS 2 as a screening instrument for PsA., Methods: ToPAS 2 was administered to 3 "diagnostic" groups of individuals - patients with PsA, patients with psoriasis, and healthy controls, and the data collected were analyzed., Results: It was found that the new version of ToPAS, ToPAS 2, again performed well, with the axial domain now featuring in the new scoring system. The constructed index, ToPAS2&#95;cap, had an overall area under the receiver-operation curve of 0.910, with overall values of sensitivity and specificity, at a cutpoint of 8 (or 7), of 87.2% (92.0%) and 82.7% (77.2%), respectively., Conclusion: ToPAS 2 shows much promise as a screening instrument for identifying PsA both in people with psoriasis and in individuals from the general population. Its performance against other proposed screening instruments for PsA should be evaluated in other clinics and for other study designs.

Two-part models are an attractive approach for analysing longitudinal semicontinuous data consisting of a mixture of true zeros and continuously distributed positive values. When the population-averaged (marginal) covariate effects are of interest, two-part models that provide straightforward interpretation of the marginal effects are desirable. Presently, the only available approaches for fitting two-part marginal models to longitudinal semicontinuous data are computationally difficult to implement. Therefore, there exists a need to develop two-part marginal models that can be easily implemented in practice. We propose a fully likelihood-based two-part marginal model that satisfies this need by using the bridge distribution for the random effect in the binary part of an underlying two-part mixed model; and its maximum likelihood estimation can be routinely implemented via standard statistical software such as the SAS NLMIXED procedure. We illustrate the usage of this new model by investigating the marginal effects of pre-specified genetic markers on physical functioning, as measured by the Health Assessment Questionnaire, in a cohort of psoriatic arthritis patients from the University of Toronto Psoriatic Arthritis Clinic. An added benefit of our proposed marginal model when compared to a two-part mixed model is the robustness in regression parameter estimation when departure from the true random effects structure occurs. This is demonstrated through simulation., (© The Author(s) 2011 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.)

Multi-state models provide a convenient statistical framework for a wide variety of medical applications characterized by multiple events and longitudinal data. We illustrate this through four examples. The potential value of the incorporation of unobserved or partially observed states is highlighted. In addition, joint modelling of multiple processes is illustrated with application to potentially informative loss to follow-up, mis-measured or missclassified data and causal inference.

In many studies, interest lies in determining whether members of the study population will undergo a particular event of interest. Such scenarios are often termed 'mover-stayer' scenarios, and interest lies in modelling two sub-populations of 'movers' (those who have a propensity to undergo the event of interest) and 'stayers' (those who do not). In general, mover-stayer scenarios within data sets are accounted for through the use of mixture distributions, and in this paper, we investigate the use of various random effects distributions for this purpose. Using data from the University of Toronto psoriatic arthritis clinic, we present a multi-state model to describe the progression of clinical damage in hand joints of patients with psoriatic arthritis. We consider the use of mover-stayer gamma, inverse Gaussian and compound Poisson distributions to account for both the correlation amongst joint locations and the possible mover-stayer situation with regard to clinical hand joint damage. We compare the fits obtained from these models and discuss the extent to which a mover-stayer scenario exists in these data. Furthermore, we fit a mover-stayer model that allows a dependence of the probability of a patient being a stayer on a patient-level explanatory variable., (Copyright © 2012 John Wiley & Sons, Ltd.)

Objective: MTX is widely used to treat synovitis in PsA without supporting trial evidence. The aim of our study was to test the value of MTX in the first large randomized placebo-controlled trial (RCT) in PsA., Methods: A 6-month double-blind RCT compared MTX (15 mg/week) with placebo in active PsA. The primary outcome was PsA response criteria (PsARC). Other outcomes included ACR20, DAS-28 and their individual components. Missing data were imputed using multiple imputation methods. Treatments were compared using logistic regression analysis (adjusted for age, sex, disease duration and, where appropriate, individual baseline scores)., Results: Four hundred and sixty-two patients were screened and 221 recruited. One hundred and nine patients received MTX and 112 received placebo. Forty-four patients were lost to follow-up (21 MTX, 23 placebo). Twenty-six patients discontinued treatment (14 MTX, 12 placebo). Comparing MTX with placebo in all randomized patients at 6 months showed no significant effect on PsARC [odds ratio (OR) 1.77, 95% CI 0.97, 3.23], ACR20 (OR 2.00, 95% CI 0.65, 6.22) or DAS-28 (OR 1.70, 95% CI 0.90, 3.17). There were also no significant treatment effects on tender and swollen joint counts, ESR, CRP, HAQ and pain. The only benefits of MTX were reductions in patient and assessor global scores and skin scores at 6 months (P = 0.03, P < 0.001 and P = 0.02, respectively). There were no unexpected adverse events., Conclusions: This trial of active PsA found no evidence for MTX improving synovitis and consequently raises questions about its classification as a disease-modifying drug in PsA. Trial registration. Current Controlled Trials, www.controlled-trials.com, ISRCTN:54376151.

Objective: To test the bidirectional hypothesis that depressive symptoms influence changes in pain over time, and pain influences changes in depressive symptoms., Methods: A total of 394 patients attending the University of Toronto Psoriatic Arthritis clinic were followed over a mean period of 7.5 years with annual assessments, including number of swollen joints (SJC), Health Assessment Questionnaire (HAQ), and the Medical Outcomes Survey Short Form 36 (SF-36). Linear mixed-effects models were used to examine the cross- and lagged associations between the changes in HAQ pain and in the SF-36 mental component summary (MCS) score, adjusting for SJC and other covariates., Results: The strongest predictors of changes in pain, SJC, and depressive symptoms between visits were scores of the corresponding variables at the previous visit, with standardized regression coefficients exceeding 0.75 in absolute value. There was, however, evidence of a small, but consequential, bidirectional relationship (i.e., standardized regression coefficients <0.3) between depressive symptoms and pain. Both previous MCS scores and change in MCS scores were associated with change in pain between visits; conversely, previous pain scores and change in pain scores were associated with change in depressive symptoms between visits., Conclusion: Even though cross-variable associations between pain and depressive symptoms exist, changes in pain and depressive symptoms appear to be strongly driven by their measurements at the previous visit. To optimize patient outcomes, a clinical approach that assesses and treats clinically significant depressive symptoms, as well as pain, is required., (Copyright © 2012 by the American College of Rheumatology.)

Motivated by investigations of factors related to various patient-reported outcome measures in psoriatic arthritis patients, after controlling for the effect of disease activity on these outcomes, we outline an approach for dealing with a rapidly fluctuating explanatory variable in a multistate model. On the basis of a representation of this variable as an ordinal classification, we suggest the use of an expanded multistate model. We examine the bias in estimating effects associated with other variables via simulation for different modelling choices. We present an analysis of a motivating data set on physical functional disability in psoriatic arthritis patients., (Copyright © 2011 John Wiley & Sons, Ltd.)

In psoriatic arthritis, permanent joint damage characterizes disease progression and represents a major debilitating aspect of the disease. Understanding the process of joint damage will assist in the treatment and disease management of patients. Multistate models provide a means to examine patterns of disease, such as symmetric joint damage. Additionally, the link between damage and the dynamic course of disease activity (represented by joint swelling and stress pain) at both the individual joint level and otherwise can be represented within a correlated multistate model framework. Correlation is reflected through the use of random effects for progressive models and robust variance estimation for non-progressive models. Such analyses, undertaken with data from a large psoriatic arthritis cohort, are discussed and the extent to which they permit causal reasoning is considered. For this, emphasis is given to the use of the Bradford Hill criteria for causation in observational studies and the concept of local (in)dependence to capture the dynamic nature of the relationships.

Objective: To assess the impact of the 2009 A/H1N1 influenza pandemic in England during the two waves of activity up to end of February 2010 by estimating the probabilities of cases leading to severe events and the proportion of the population infected., Design: A Bayesian evidence synthesis of all available relevant surveillance data in England to estimate severity of the pandemic., Data Sources: All available surveillance systems relevant to the pandemic 2009 A/H1N1 influenza outbreak in England from June 2009 to February 2010. Pre-existing influenza surveillance systems, including estimated numbers of symptomatic cases based on consultations to the health service for influenza-like illness and cross sectional population serological surveys, as well as systems set up in response to the pandemic, including follow-up of laboratory confirmed cases up to end of June 2009 (FF100 and Fluzone databases), retrospective and prospective follow-up of confirmed hospitalised cases, and reported deaths associated with pandemic 2009 A/H1N1 influenza. Main outcome measures Age specific and wave specific probabilities of infection and symptomatic infection resulting in hospitalisation, intensive care admission, and death, as well as infection attack rates (both symptomatic and total). The probabilities of intensive care admission and death given hospitalisation over time are also estimated to evaluate potential changes in severity across waves., Results: In the summer wave of A/H1N1 influenza, 0.54% (95% credible interval 0.33% to 0.82%) of the estimated 606,100 (419,300 to 886,300) symptomatic cases were hospitalised, 0.05% (0.03% to 0.08%) entered intensive care, and 0.015% (0.010% to 0.022%) died. These correspond to 3200 (2300 to 4700) hospital admissions, 310 (200 to 480) intensive care admissions, and 90 (80 to 110) deaths in the summer wave. In the second wave, 0.55% (0.28% to 0.89%) of the 1,352,000 (829,900 to 2,806,000) estimated symptomatic cases were hospitalised, 0.10% (0.05% to 0.16%) were admitted to intensive care, and 0.025% (0.013% to 0.040%) died. These correspond to 7500 (5900 to 9700) hospitalisations, 1340 (1030 to 1790) admissions to intensive care, and 240 (310 to 380) deaths. Just over a third (35% (26% to 45%)) of infections were estimated to be symptomatic. The estimated probabilities of infections resulting in severe events were therefore 0.19% (0.12% to 0.29%), 0.02% (0.01% to 0.03%), and 0.005% (0.004% to 0.008%) in the summer wave for hospitalisation, intensive care admission, and death respectively. The corresponding second wave probabilities are 0.19% (0.10% to 0.32%), 0.03% (0.02% to 0.06%), and 0.009% (0.004% to 0.014%). An estimated 30% (20% to 43%) of hospitalisations were detected in surveillance systems in the summer, compared with 20% (15% to 25%) in the second wave. Across the two waves, a mid-estimate of 11.2% (7.4% to 18.9%) of the population of England were infected, rising to 29.5% (16.9% to 64.1%) in 5-14 year olds. Sensitivity analyses to the evidence included suggest this infection attack rate could be as low as 5.9% (4.2% to 8.7%) or as high as 28.4% (26.0% to 30.8%). In terms of the probability that an infection leads to death in the second wave, these correspond, respectively, to a high estimate of 0.017% (0.011% to 0.024%) and a low estimate of 0.0027% (0.0024% to 0.0031%)., Conclusions: This study suggests a mild pandemic, characterised by case and infection severity ratios increasing between waves. Results suggest low ascertainment rates, highlighting the importance of systems enabling early robust estimation of severity, to inform optimal public health responses, particularly in light of the apparent resurgence of the 2009 A/H1N1 strain in the 2010-11 influenza season.

Characterization of the incubation time from infection to onset is important for understanding the natural history of infectious diseases. Attempts to estimate the incubation time distribution for novel A(H1N1v) have been, up to now, based on limited data or peculiar samples. We characterized this distribution for a generic group of symptomatic cases using laboratory-confirmed swine influenza case-information. Estimates of the incubation distribution for the pandemic influenza were derived through parametric time-to-event analyses of data on onset of symptoms and exposure dates, accounting for interval censoring. We estimated a mean of about 1·6-1·7 days with a standard deviation of 2 days for the incubation time distribution in those who became symptomatic after infection with the A(H1N1v) virus strain. Separate analyses for the <15 years and ≥ 15 years age groups showed a significant (P<0·02) difference with a longer mean incubation time in the older age group.

Fertilization-induced cytoplasmic flows are a conserved feature of eggs in many species. However, until now the importance of cytoplasmic flows for the development of mammalian embryos has been unknown. Here, by combining a rapid imaging of the freshly fertilized mouse egg with advanced image analysis based on particle image velocimetry, we show that fertilization induces rhythmical cytoplasmic movements that coincide with pulsations of the protrusion forming above the sperm head. We find that these movements are caused by contractions of the actomyosin cytoskeleton triggered by Ca(2+) oscillations induced by fertilization. Most importantly, the relationship between the movements and the events of egg activation makes it possible to use the movements alone to predict developmental potential of the zygote. In conclusion, this method offers, thus far, the earliest and fastest, non-invasive way to predict the viability of eggs fertilized in vitro and therefore can potentially improve greatly the prospects for IVF treatment.

Objective: To develop a recommended measure of response for use in psoriatic arthritis (PsA) clinical trials and observational cohort studies reflecting joint involvement., Methods: Previously, we used data from phase III randomized placebo-controlled trials of anti-tumor necrosis factor (TNF) agents to determine models, based primarily on statistical considerations but with some clinical input when necessary, that best distinguish drug-treated from placebo-treated patients. For the same data, we examine response criteria currently used for PsA and logistic regression models based on the individual components of these response criteria. Comparison with our previously developed models, based primarily on statistical consideration, is made., Results: A simplified score, the PsA Joint Activity Index (PsAJAI), based on components of the ACR30, performed better than the ACR20 and PsARC, and comparable to our previously developed models. The PsAJAI is a weighted sum of 30% improvement in core measures with weights of 2 given to the joint count measure, the C-reactive protein laboratory measure, and the physician global assessment of disease activity measure. Weights of 1 should be given to the remaining 30% improvement measures including pain, patient global assessment of disease activity, and the Health Assessment Questionnaire., Conclusion: We recommend the PsAJAI be used as an outcome measure for assessing joint disease response in PsA clinical trials.

Objective: To develop statistical models, based on the analysis of data from phase III randomized placebo-controlled trials of tumor necrosis factor-alpha (TNF-alpha) inhibitors over a 24-week period, that may inform the definition of response measures for clinical trials in psoriatic arthritis (PsA)., Methods: Data from phase III randomized controlled trials with anti-TNF agents were used. A training set using baseline and 24-week data from 2 trials was used to derive the models, which were then tested on a dataset using baseline and interim data from the third trial, and baseline and interim data from the first 2 trials. Logistic regression, tree analysis, and factor analysis were considered in the development of the models. Receiver-operating characteristic curves were constructed and area under the curve (AUC) calculated to assess performance of the models., Results: Two models were derived. One was based on differences between baseline and last-visit values, which identified the current 68 tender joint count (TJC68), baseline and change in C-reactive protein (CRP), and the measure with the highest difference among the patient and physician global assessment of disease activity (GDA), patient assessment of pain and the Health Assessment Questionnaire (HAQ). The second model was based on percentage change from baseline and included TJC68, CRP, physician GDA, patient global assessment of arthritis pain, and HAQ. Both models provided high AUC of at least 0.8 for both the training and testing sets., Conclusion: Models for discriminating joint disease response patterns in PsA were derived from data from randomized controlled trials. These models can now be used to inform further consideration of response measures for trials.

Objective: To describe the longitudinal course of fatigue in psoriatic arthritis (PsA)., Methods: Our study included 390 patients who attended the University of Toronto Psoriatic Arthritis Clinic between 1998 and 2006 and who completed 2 or more administrations of the modified Fatigue Severity Scale (mFSS) at yearly intervals. Clinical data were used that corresponded to visits in which mFSS was administered. We used linear mixed effects models to examine the relationships of disease-related and nondisease-related variables with mFSS scores across multiple clinic visits, and linear regression models to investigate the association between change in mFSS scores (DeltamFSS) and changes in covariates between visits., Results: Clinical measures of disease activity were related to fatigue over time; however, these relationships disappeared in the context of patient-reported physical disability and pain. Patient-reported measures of physical disability, pain, and psychological distress were most closely related to higher mFSS scores (greater fatigue) across clinic assessments. Fatigue was found to vary over time, at least when assessed at yearly intervals. In general, measures of clinical and functional status at the current visit were more predictive of DeltamFSS in between previous and current visits than change scores in these measures between visits. Comorbid fibromyalgia or hypertension were also associated with greater fatigue across multiple visits and with change in fatigue between visits., Conclusion: A combination of factors is associated with fatigue in PsA. The full effect of comorbidities on fatigue warrants further study to better understand the effective management of fatigue in PsA.

Objective: To determine the relationship between fatigue and disease-related and psychosocial variables in psoriatic arthritis (PsA)., Method: 499 patients attending the University of Toronto PsA Clinic were administered the modified fatigue severity scale (mFSS). At the time of mFSS administration, clinical and laboratory measures of disease activity and damage were recorded. Linear regression models were used to examine the cross-sectional relationship between disease-related and psychosocial variables and mFSS scores., Results: At least moderate fatigue occurred in 49.5% of patients and severe fatigue in 28.7%. Univariately the vast majority of variables were significantly associated with mFSS scores. The final multivariate model was composed of female sex, the medical outcome survey short form 36 (SF-36) pain and mental health scales, the number of fibromyalgia tender points, the health assessment questionnaire (HAQ) and "ever used" methotrexate, and explained 54.5% of the variation in mFSS scores. The SF-36 mental health scale played the largest role in the multivariate model, uniquely accounting for 6.6% of the variation in the fatigue severity scale. The disease-related factors significant at the univariate level did not achieve statistical significance in the context of HAQ and pain measures., Conclusion: Fatigue is a common symptom in PsA, and is associated, in a multivariate model, with pain, female sex, physical functional disability, medication status and psychological distress. Fatigue appears to provide some information that does not overlap with the core set of outcome domains in PsA.

Background: Increasing evidence for cardiovascular mortality among patients with psoriasis and psoriatic arthritis (PsA) has accumulated, together with evidence for increased prevalence of risk factors for cardiovascular disease (CVD)., Objectives: To describe cardiovascular morbidity in PsA, determine its prevalence and identify risk factors for its development., Methods: At the University of Toronto, patients were followed up prospectively according to a standard protocol, including disease-related features and comorbidities. Patients with CVD, including myocardial infarction (MI), angina, hypertension and cerebrovascular accident (CVA), were identified. The prevalence of CVD morbidities in these patients was compared with data from the Canadian Community Health Survey through standardised prevalence ratios (SPRs). Cox relative risk regression analysis was used to analyse risk factors., Results: At the time of analysis, 648 patients were registered in the database. After clinic entry, 122 developed hypertension, 38 had an MI and 5, 21 and 11 had CVA, angina and congestive heart failure (CHF), respectively. 155 patients had at least one of these conditions. The SPRs for MI (2.57; 95% CI 1.73 to 3.80), angina (1.97; 95% CI 1.24 to 3.12) and hypertension (1.90; 95% CI 1.59 to 2.27) were statistically significant, whereas the SPRs for CHF (1.19; 95% CI 0.50 to 2.86) and CVA (0.91; 95% CI 0.34 to 2.43) were not. Factors associated with CVD included diabetes, hyperlipidaemia and high Psoriasis Area and Severity Index scores., Conclusion: Patients with PsA are at increased risk of cardiovascular morbidities compared with the general population. In addition to known risk factors for CVD, severe psoriasis is an important predictor in patients with PsA.

To project national hepatitis C virus (HCV) burden, unbiased estimation of HCV progression to liver cirrhosis is required for the whole community of HCV-infected individuals. However, widely varying estimates of progression rates to cirrhosis have been produced. This disparity is partly associated with the statistical methods applied, but is mainly due to the differing types of study cohort. We use an inverse probability weighted estimation method to recover the true parameters for the (Weibull regression) model that determines the incubation period from infection to cirrhosis for the community of HCV-infected individuals, when there is cirrhosis-related recruitment bias to the studied cohort. We apply the method to simulated data for a liver clinic which attracts patients from a community of 1000 HCV-infected individuals under different event-biased referral patterns. We investigate how well the method performs in recovering the true community parameters, and then apply it to Edinburgh Royal Infirmary's liver clinic series. The results obtained are compared to those from a Weibull survival analysis which ignores the selection bias.

Semicontinuous data in the form of a mixture of zeros and continuously distributed positive values frequently arise in biomedical research. Two-part mixed models with correlated random effects are an attractive approach to characterize the complex structure of longitudinal semicontinuous data. In practice, however, an independence assumption about random effects in these models may often be made for convenience and computational feasibility. In this article, we show that bias can be induced for regression coefficients when random effects are truly correlated but misspecified as independent in a 2-part mixed model. Paralleling work on bias under nonignorable missingness within a shared parameter model, we derive and investigate the asymptotic bias in selected settings for misspecified 2-part mixed models. The performance of these models in practice is further evaluated using Monte Carlo simulations. Additionally, the potential bias is investigated when artificial zeros, due to left censoring from some detection or measuring limit, are incorporated. To illustrate, we fit different 2-part mixed models to the data from the University of Toronto Psoriatic Arthritis Clinic, the aim being to examine whether there are differential effects of disease activity and damage on physical functioning as measured by the health assessment questionnaire scores over the course of psoriatic arthritis. Some practical issues on variance component estimation revealed through this data analysis are considered.

Objective: To develop and validate a psoriatic arthritis (PsA) screening questionnaire: the Toronto Psoriatic Arthritis Screen (ToPAS)., Methods: The ToPAS was developed through review of items seen in patients with PsA and evaluation by patients with PsA and patients with other rheumatological conditions, and was administered to consecutive consenting patients attending five clinics: PsA, psoriasis, general dermatology, general rheumatology (excluding PsA patients) and family medicine. All patients were assessed by a rheumatologist according to a standard protocol. A three-step analysis strategy was adopted: a stepwise logistic regression to identify the questions most important in discriminating between those with and without PsA; a logistic model was fitted to three clinically relevant domains for PsA: skin, joints and nails; and a simpler weighting of each of the domains used in step 2. Receiver operating characteristic (ROC) curves were obtained based on these various models., Results: In all, there were 134 patients from the PsA clinic, 123 with psoriasis, 118 from dermatology, 135 from rheumatology and 178 from family medicine. A simplified discriminatory score based on the skin, joint and nail domains gave results comparable to other methods with an observed overall sensitivity and specificity, based on a single cut point, of 86.8% and 93.1%. When the patients with PsA were compared with each of the other four patient groups individually, the sensitivity and specificity of the ToPAS were: psoriasis 89.1%, 86.3%; dermatology 91.9%, 95.2%; rheumatology 92.6%, 85.7%; and family medicine 90.4%, 100%., Conclusion: Our simplified index is very good at classifying those who are not diagnosed with PsA and those who are diagnosed with PsA.

Objective: Survival of patients with systemic lupus erythematosus (SLE) has improved significantly, but new morbidities have emerged, leading to altered patterns of outcome in this disease. We examined changes in mortality and other outcomes over time in a large SLE cohort., Methods: A group of 1241 patients from the University of Toronto Lupus Clinic followed prospectively were divided into 4 entry cohorts - 1: 1970-1978, 2: 1979-1987, 3: 1988-1996, 4: 1997-2005. These cohorts were followed through four 9-year calendar periods defined over the same intervals. Both cohort and calendar effects were assessed for the following outcomes: mortality (standardized mortality ratio; SMR), disease activity over time (adjusted mean SLEDAI; AMS), cumulative damage (Systemic Lupus International Collaborating Clinics Damage Index; SDI), coronary artery disease (CAD), and osteonecrosis (ON). Cox regression models were used to further investigate mortality and the influence on it of the disease-related factors., Results: Over the 36-year period of the study, 211 deaths occurred. The overall SMR in the first and last decades were 12.60 (95% CI: 9.13, 17.39) and 3.46 (95% CI: 2.71, 4.40) respectively. When SMR were stratified by the entry cohort and calendar period, there is evidence of a calendar-period effect but no cohort effect. The AMS decreased over the decades, while SDI, CAD, and ON increased. There were significant detrimental effects for male sex, CAD, AMS, SDI, and use of immunosuppressive drugs and significant protective effects for use of antimalarials and the effect of calendar period on mortality., Conclusion: Our study demonstrates improved survival in patients with SLE over a 36-year period. Disease-related variables included in the model are important factors for mortality in this SLE cohort, but could not completely explain the trend of improved survival over calendar period observed.

Objective: To determine the prevalence and types of malignancy in a large cohort of patients with psoriatic arthritis (PsA), and to compare this rate with that in the general population., Methods: A cohort analysis of patients who were followed up prospectively from 1978 to 2004 at the University of Toronto Psoriatic Arthritis Clinic was performed. Patients were followed up at 6-12-month intervals according to a standard protocol, which included recording of malignancy, and tracked on a computer database. The cohort was linked with a provincial database to find malignancies that may have been missed by the protocol or developed after patients were lost to followup. Data were presented and analyzed using descriptive statistics and the Cox regression model with robust estimate of variance. Rates of first malignancy in the cohort were compared with rates in the population to derive standardized incidence ratios (SIRs)., Results: Of the 665 patients included, 68 (10.2%) developed a malignancy at an average age of 62.4 years. The most frequently seen malignancies were breast (20.6%), lung (13.2%), and prostate (8.8%) cancer. The SIR for all cancers was 0.98 (95% confidence interval 0.77-1.24). Overall cancer type-specific SIRs were 0.69 (95% CI 0.26-1.83) for hematologic and 0.88 (95% CI 0.46-1.69) for lung cancer. In females, the SIR for breast cancer was 1.55 (95% CI 0.92-2.62), and in males, the SIR for prostate cancer was 0.65 (95% CI 0.29-1.44)., Conclusion: Overall, 10.2% of patients in the Toronto PsA cohort developed cancer. The most frequent cancers were breast, lung, and prostate cancer. The incidence of malignancy in the large PsA cohort did not differ from that in the general population.

Objective: To investigate how cirrhosis-biased referral to liver clinics can explain the wide variation in progression rates for differently recruited cohorts and, in particular, for liver clinic cohorts compared to community-based studies of the natural history of hepatitis C virus (HCV)., Study Design and Setting: A simulation was designed to illustrate the sort of referral bias pattern that is capable of converting a 20-year progression rate to cirrhosis of around 5% in the community of HCV-infected individuals into a 20% progression rate for patients who have been selectively referred to a liver clinic., Results: We show that event-biased recruitment, such as occurs if referral to liver clinics is increasingly likely the closer a patient is to cirrhosis, can produce severely upwardly biased estimates of progression rates, can dampen the influence of "poor prognostic" factors (such as history of excessive alcohol consumption), but overrepresents the proportion of patients in the community of HCV-infected individuals who have poor prognosis., Conclusion: When attempting to establish the natural history of new diseases with long incubation periods, researchers should be on the look out for potential biases that result from the way patients are referred into clinical cohorts.

Objective: To determine whether there has been a change in mortality rates over the last 3 decades in patients with psoriatic arthritis (PsA) whose cases were followed prospectively., Methods: Patients receiving followup care according to a standard protocol at the University of Toronto PsA Clinic between 1978 and 2004 were included. Information on patient deaths was collected prospectively. Mortality data for the general population of Ontario, Canada, stratified by 5-year age bands, sex, and calendar year from 1978 to 2004, were used to calculate the reference rates. Standardized mortality ratios (SMRs) were calculated through use of Poisson regression models for the number of observed deaths. Time trend analyses were performed through the use of 10-year "rolling-average" SMRs and followup period-specific SMRs stratified by the period of entry into clinic., Results: Of 680 patients with PsA, 106 (15.6%) (55 women and 51 men) have died. Major causes of death were disease of the circulatory system, neoplasms, diseases of the respiratory system, diseases of the gastrointestinal system, injuries/poisoning, and unknown. The overall SMR for the period 1978-2004 was 1.36 (95% confidence interval 1.12, 1.64). The estimated number of life-years lost by the PsA patient cohort overall was 2.99 years (95% confidence interval 1.14, 4.77). For patients who entered the cohort during the years 1978-1986, the SMRs were 1.89, 1.83, and 1.21 for followup periods 1978-1986, 1987-1995, and 1996-2004, respectively. For patients who entered the cohort during the years 1987-1995, the SMRs were 0.55 and 0.82, while the SMR for those who entered during 1996-2004 was 0.56., Conclusion: The drop in SMRs in this PsA clinic population suggests that the mortality risk has improved over time. This improved survival may reflect disease severity at presentation in the earlier cohort as well as earlier diagnosis and more aggressive treatment in the more recent followup period.

Background: Evidence suggests the wide variation in platelet response within the population is genetically controlled. Unraveling the complex relationship between sequence variation and platelet phenotype requires accurate and reproducible measurement of platelet response., Objective: To develop a methodology suitable for measuring signaling pathway-specific platelet phenotype, to use this to measure platelet response in a large cohort, and to demonstrate the effect size of sequence variation in a relevant model gene., Methods: Three established platelet assays were evaluated: mobilization of [Ca(2+)](i), aggregometry and flow cytometry, each in response to adenosine 5'-diphosphate (ADP) or the glycoprotein (GP) VI-specific crosslinked collagen-related peptide (CRP). Flow cytometric measurement of fibrinogen binding and P-selectin expression in response to a single, intermediate dose of each agonist gave the best combination of reproducibility and inter-individual variability and was used to measure the platelet response in 506 healthy volunteers. Pathway specificity was ensured by blocking the main subsidiary signaling pathways., Results: Individuals were identified who were hypo- or hyper-responders for both pathways, or who had differential responses to the two agonists, or between outcomes. 89 individuals, retested three months later using the same methodology, showed high concordance between the two visits in all four assays (r(2) = 0.872, 0.868, 0.766 and 0.549); all subjects retaining their phenotype at recall. The effect of sequence variation at the GP6 locus accounted for approximately 35% of the variation in the CRP-XL response., Conclusion: Genotyping-phenotype association studies in a well-characterized, large cohort provides a powerful strategy to measure the effect of sequence variation in genes regulating the platelet response.

Background: A common feature of microarray experiments is the occurrence of missing gene expression data. These missing values occur for a variety of reasons, in particular, because of the filtering of poor quality spots and the removal of undefined values when a logarithmic transformation is applied to negative background-corrected intensities. The efficiency and power of an analysis performed can be substantially reduced by having an incomplete matrix of gene intensities. Additionally, most statistical methods require a complete intensity matrix. Furthermore, biases may be introduced into analyses through missing information on some genes. Thus methods for appropriately replacing (imputing) missing data and/or weighting poor quality spots are required., Results: We present a likelihood-based method for imputing missing data or weighting poor quality spots that requires a number of biological or technical replicates. This likelihood-based approach assumes that the data for a given spot arising from each channel of a two-dye (two-channel) cDNA microarray comparison experiment independently come from a three-component mixture distribution--the parameters of which are estimated through use of a constrained E-M algorithm. Posterior probabilities of belonging to each component of the mixture distributions are calculated and used to decide whether imputation is required. These posterior probabilities may also be used to construct quality weights that can down-weight poor quality spots in any analysis performed afterwards. The approach is illustrated using data obtained from an experiment to observe gene expression changes with 24 hr paclitaxel (Taxol) treatment on a human cervical cancer derived cell line (HeLa)., Conclusion: As the quality of microarray experiments affect downstream processes, it is important to have a reliable and automatic method of identifying poor quality spots and arrays. We propose a method of identifying poor quality spots, and suggest a method of repairing the arrays by either imputation or assigning quality weights to the spots. This repaired data set would be less biased and can be analysed using any of the appropriate statistical methods found in the microarray literature.

Motivation: It is widely acknowledged that microarray data are subject to high noise levels and results are often platform dependent. Therefore, microarray experiments should be replicated several times and in several laboratories before the results can be relied upon. To make the best use of such extensive datasets, methods for microarray data fusion are required. Ideally, the fused data should distil important aspects of the data while suppressing unwanted sources of variation and be amenable to further informal and formal methods of analysis. Also, the variability in the quality of experimentation should be taken into account., Results: We present such an approach to data fusion, based on multivariate regression. We apply our methodology to data from a previous study on cell-cycle control in Schizosaccharomyces pombe., Availability: The algorithm implemented in R is freely available from the authors on request.

Cluster of differentiation (CD) antigens are expressed on cells of myeloid and lymphoid lineages. As most disease processes involve immune system activation or suppression, these antigens offer unique opportunities for monitoring host responses. Immunophenotyping using limited numbers of CD antigens enables differentiation states of immune system cells to be determined. Extended phenotyping involving parallel measurement of multiple CD antigens may help identify expression pattern signatures associated with specific disease states. To explore this possibility we have made a CD monoclonal antibody array and scanner, enabling the parallel immunophenotyping of leukocyte cell suspensions in a single and rapid analysis. To demonstrate this approach, we used the specific example of patients infected with human immunodeficiency virus type-1 (HIV-1). An invariant HIV-induced CD antigen signature has been defined that is both robust and independent of clinical outcome, composed of a unique profile of CD antigen expression levels that are both increased and decreased relative to internal controls. The results indicate that HIV-induced changes in CD antigen expression are disease specific and independent of outcome. Their invariant nature indicates an irreversible component to retroviral infection and suggests the utility of CD antigen expression patterns in other disease settings.

Background: Disability questions require older people to report difficulties with everyday activities, using broad categorical responses. Relatively little is known about population group differences in the thresholds for reporting difficulty or inability with medium-distance mobility against tested mobility-related performance. We aimed to estimate the thresholds on tested performance at which self-reports change from one category to another, across a range of sociodemographic subgroups. We also aimed to compare reported and tested performance across two national population studies., Methods: The samples were from the third U.S. National Health and Nutrition Examination Study (NHANES III) and the Longitudinal Aging Study Amsterdam (LASA). Measures of gait speed, chair stands, and peak expiratory flow rate in both studies yielded the validated index of mobility-related physical limitations (MOBLI). Latent probit models were used to estimate cutpoints (thresholds) on the index for reporting difficulty or inability to walk a medium distance., Results: Thresholds for reporting difficulty or inability were studied by age, sex, race, educational level, and income in NHANES III. In models adjusting for the other factors, performance thresholds for reporting disability categories varied by age and income. The younger elderly persons in NHANES III on average reported difficulties or inabilities only when they reached a more severe level of tested limitation compared with older old persons. A similar pattern exists for those on higher incomes. For race, differences in threshold were present only for reporting inability, but not difficulty. Significant differences in thresholds were not present between groups defined by sex or for years of education. Comparisons between the NHANES and LASA studies show that lower reported mobility difficulty or inability prevalence in the Dutch sample is attributable both to reporting at higher levels of limitation and to better functioning., Conclusions: There is evidence of differences in thresholds for reporting mobility disability, especially across age and income groups in older Americans. Further work is needed to understand the perceptual, attitudinal, or environmental factors that cause these reporting differences.

Objective: To evaluate the impact of an ultrasound finding of a pseudo-sac (PS), a uterine sac without a double decidual ring or a yolk sac, on the management of cases with possible ectopic pregnancy., Design: A retrospective review of a series of cases., Setting: A general hospital., Patient(s): Seventy-seven patients who had a diagnostic laparoscopy over a period of 3 years for suspected ectopic pregnancy., Main Outcome Measure(s): A logistic regression analysis was conducted to evaluate the effect of the ultrasound finding of a PS on predicting a negative finding at laparoscopy., Result(s): The report of a PS is significantly associated with a false-positive diagnosis of ectopic pregnancy., Conclusion(s): A diagnosis of PS should not be interpreted as indicative of an ectopic pregnancy because radiological differentiation between an early intrauterine pregnancy failure and an ectopic pregnancy is not possible.

Objective: To compare contrast-enhanced MR angiography (CEMRA) with intra-arterial digital subtraction angiography (DSA) for evaluating carotid stenosis., Methods: A total of 167 consecutive symptomatic patients, scheduled for DSA following screening duplex ultrasound (DUS), were prospectively recruited to have CEMRA. Three independent readers reported on each examination in a blinded and random manner. Agreement was assessed using the Bland-Altman method. Diagnostic and potential clinical impact of CEMRA was evaluated, singly and in combination with DUS., Results: CEMRA tended to overestimate stenosis by a mean bias ranging from 2.4 to 3.8%. A significant part of the disagreement between CEMRA and DSA was directly caused by interobserver variability. For detection of severe stenosis, CEMRA alone had a sensitivity of 93.0% and specificity of 80.6%, with a diagnostic misclassification rate of 15.0% (n = 30). More importantly, clinical decision-making would, however, have been potentially altered only in 6.0% of cases (n = 12). The combination of concordant DUS and CEMRA reduced diagnostic misclassification rate to 10.1% (n = 19) at the expense of 47 (24.9%) discordant cases needing to proceed to DSA. An intermediate approach of selective DUS review resulted in a marginally worse diagnostic misclassification rate of 11.6% (n = 22) but with only 6.8% of discordant cases (n = 13)., Conclusions: DSA remains the gold standard for carotid imaging. The clinical misclassification rate with CEMRA, however, is acceptably low to support its safe use instead of DSA. The appropriateness of combination strategies depends on institutional choice and cost-effectiveness issues.

Background: Hypertension and hypercholesterolemia are recognized complications of liver transplantation, but whether they contribute to the development of cardiovascular disease is uncertain. We aimed first to determine the prevalence of risk factors for coronary heart disease (CHD) after liver transplantation and second to study the effect of liver transplantation on the predicted 10-year risk of developing CHD and the incidence of cardiovascular events in comparison with a matched local population., Methods: Data on blood pressure, serum lipids, weight, diabetes mellitus, smoking, and incidence of myocardial infarction (MI) and stroke were obtained retrospectively from the case notes of 181 consecutive adult liver transplant recipients (median follow-up 54 months). The Framingham coronary risk equations were used to calculate the 10-year probability of developing CHD., Results: The prevalences of hypertension and hypercholesterolemia after transplantation were 77% and 62%, respectively. The predicted 10-year risk of CHD increased from 6.9% before transplantation to 11.5% at 1 year after transplantation, whereas that of a matched local population was 7%. Compared with a matched nontransplant population, the incidence ratios for MI and stroke were 0.55 (95% confidence interval, 0.01-3.06 ) and 1.45 (95% confidence interval, 0.18-5.22), respectively. No patients died from MI or stroke., Conclusions: Liver transplant recipients have a high prevalence of risk factors for cardiovascular disease, exceeding that of the general population, and have a higher predicted risk of developing CHD. Despite this, there were no deaths from CHD or stroke during the study period.