Higher psychological distress in patients seeking care for a knee disorder is associated with diagnostic discordance between health care providers: a secondary analysis of a diagnostic concordance study.

BACKGROUND: Knee disorders are highly prevalent and may be a disabling condition. An accurate diagnosis is necessary to guide toward a rapid and efficient management of knee disorders. However, the ability to make a valid diagnosis is often complex for clinicians and evidence is mainly focused on clinician cognitive biases or errors produced during clinical reasoning. The aim of this secondary exploratory analysis is to identify patient-specific characteristics associated with diagnostic discordance between health care providers in making a diagnosis for a new knee disorder. METHODS: We performed a secondary analysis of a diagnostic study comparing the diagnostic ability of a physiotherapist to medical musculoskeletal specialists. Patients' socio-demographic, psychosocial and clinical characteristics were compared between the concordant and discordant diagnostic groups. Psychosocial symptoms were evaluated using the validated Kessler 6 (K6) questionnaire. We performed multivariable logistic regressions using the Bayesian Information Criterion to identify the most probable model including patients' characteristics associated with diagnostic discordance. Overall probability of identified variables to explain diagnostic discordance and associated odd ratios (OR) with 95% credibility intervals (95% CrI) were calculated. RESULTS: Overall, 279 participants were evaluated by a physiotherapist and medical musculoskeletal specialists. The mean age of the participants was 49.1 ± 15.8 years and 57.7% were female. The most common disorder was osteoarthritis (n = 117, 18.8% of cases were discordant). The most probable model explaining diagnostic discordance (11.13%) included having depressive symptoms, which was associated with an increased probability of diagnostic discordance (OR: 3.9; 95% CrI: 1.9 - 8.0) and having a higher number of comorbidities, which was associated with a decreased probability of diagnostic discordance (OR: 0.6; 95% CrI: 0.5 - 0.9). The depression item of the K6 questionnaire had a 99.4% chance to be included in a model explaining diagnostic discordance. Other variables taken separately had less than 50% chance to be included in a model explaining diagnostic discordance and cannot be considered significant. CONCLUSION: Our results suggest that depressive symptoms may increase the risk of knee diagnostic discordance. Clinicians may be more likely to make diagnostic errors and should be more cautious when evaluating patients with knee disorders suffering from psychological distress.

Bayesian consensus-based sample size criteria for binomial proportions.

Many sample size criteria exist. These include power calculations and methods based on confidence interval widths from a frequentist viewpoint, and Bayesian methods based on credible interval widths or decision theory. Bayesian methods account for the inherent uncertainty of inputs to sample size calculations through the use of prior information rather than the point estimates typically used by frequentist methods. However, the choice of prior density can be problematic because there will almost always be different appreciations of the past evidence. Such differences can be accommodated a priori by robust methods for Bayesian design, for example, using mixtures or ϵ-contaminated priors. This would then ensure that the prior class includes divergent opinions. However, one may prefer to report several posterior densities arising from a "community of priors," which cover the range of plausible prior densities, rather than forming a single class of priors. To date, however, there are no corresponding sample size methods that specifically account for a community of prior densities in the sense of ensuring a large-enough sample size for the data to sufficiently overwhelm the priors to ensure consensus across widely divergent prior views. In this paper, we develop methods that account for the variability in prior opinions by providing the sample size required to induce posterior agreement to a prespecified degree. Prototypic examples to one- and two-sample binomial outcomes are included. We compare sample sizes from criteria that consider a family of priors to those that would result from previous interval-based Bayesian criteria.

Pneumococcal pneumonia prevalence among adults with severe acute respiratory illness in Thailand - comparison of Bayesian latent class modeling and conventional analysis.

BACKGROUND: Determining the etiology of pneumonia is essential to guide public health interventions. Diagnostic test results, including from polymerase chain reaction (PCR) assays of upper respiratory tract specimens, have been used to estimate prevalence of pneumococcal pneumonia. However limitations in test sensitivity and specificity and the specimen types available make establishing a definitive diagnosis challenging. Prevalence estimates for pneumococcal pneumonia could be biased in the absence of a true gold standard reference test for detecting Streptococcus pneumoniae. METHODS: We conducted a case control study to identify etiologies of community acquired pneumonia (CAP) from April 2014 through August 2015 in Thailand. We estimated the prevalence of pneumococcal pneumonia among adults hospitalized for CAP using Bayesian latent class models (BLCMs) incorporating results of real-time polymerase chain reaction (qPCR) testing of upper respiratory tract specimens and a urine antigen test (UAT) from cases and controls. We compared the prevalence estimate to conventional analyses using only UAT as a reference test. RESULTS: The estimated prevalence of pneumococcal pneumonia was 8% (95% CI: 5-11%) by conventional analyses. By BLCM, we estimated the prevalence to be 10% (95% CrI: 7-16%) using binary qPCR and UAT results, and 11% (95% CrI: 7-17%) using binary UAT results and qPCR cycle threshold (Ct) values. CONCLUSIONS: BLCM suggests a > 25% higher prevalence of pneumococcal pneumonia than estimated by a conventional approach assuming UAT as a gold standard reference test. Higher quantities of pneumococcal DNA in the upper respiratory tract were associated with pneumococcal pneumonia in adults but the addition of a second specific pneumococcal test was required to accurately estimate disease status and prevalence. By incorporating the inherent uncertainty of diagnostic tests, BLCM can obtain more reliable estimates of disease status and improve understanding of underlying etiology.

Fine particulate air pollution and systemic autoimmune rheumatic disease in two Canadian provinces.

OBJECTIVE: To estimate the degree to which fine particulate (PM2.5) air pollution is associated with systemic autoimmune rheumatic diseases (SARDs). METHODS: We used population-based administrative data from Alberta (1993-2007) and Quebec (1989-2011). SARD algorithms included ≥2 physician billing codes, or ≥1 rheumatology billing code, or ≥1 hospitalization diagnostic code (for systemic lupus, Sjogren's Syndrome, scleroderma, polymyositis, dermatomyositis, or undifferentiated connective tissue disease). Bayesian hierarchical latent class regression models estimated the probability that any given resident was a SARD case, based on the algorithms. Mean 2001-2006 residential ambient PM2.5 levels were assigned using satellite-derived data for dissemination area regions in Alberta and CLSC regions in Quebec. The sum of individual level probabilities provided the estimated total cases per region in each province, according to age, sex, urban-versus-rural residence, income, and PM2.5 levels. In Alberta, we ran separate models for First-Nations (FN) and non-First Nations subgroups. Bayesian logistic regression modeling generated odds ratio (OR) estimates for being a SARD case, accounting concurrently for demographics, as well as an interaction term between age and sex. RESULTS: Our data suggested that the probability of being a SARD case was higher among females versus males and for residents aged >45 versus younger, with the highest ORs for older females. Independently, the odds of being a SARDs case increased with PM2.5 levels in both provinces. CONCLUSION: Our data suggest that PM2.5 exposure may be associated with an increased risk of SARDs.

Estimating risk of emergency room visits for asthma from personal versus fixed site measurements of NO2.

BACKGROUND: We examined the impact of data source and exposure measurement error for ambient NO2 on risk estimates derived from a case-crossover study of emergency room visits for asthma in Windsor, Canada between 2002 and 2009. METHODS: Paired personal and fixed-site NO2 data were available from an independent population (47 children and 48 adults) in Windsor between 2005 and 2006. We used linear regression to estimate the relationship and measurement error variance induced between fixed site and personal measurements of NO2, and through a series of simulations, evaluated the potential for a Bayesian model to adjust for this change in scale and measurement error. Finally, we re-analyzed data from the previous case-crossover study adjusting for the estimated change in slope and measurement error. RESULTS: Correlations between paired NO2 measurements were weak (R(2)≤0.08) and slopes were far from unity (0.0029≤ß≤0.30). Adjusting the previous case-crossover analysis suggested a much stronger association between personal NO2 (per 1ppb) (Odds Ratio (OR)=1.276, 95% Credible Interval (CrI): 1.034, 1.569) and emergency room visits for asthma among children relative to the fixed-site estimate (OR=1.024, 95% CrI 1.004-1.045). CONCLUSIONS: Our findings suggest that risk estimates based on fixed-site NO2 concentrations may differ substantially from estimates based on personal exposures if the change in scale and/or measurement error is large. In practice, one must always keep the scale being used in mind when interpreting risk estimates and not assume that coefficients for ambient concentrations reflect risks at the personal level.

Fine particulate air pollution, nitrogen dioxide, and systemic autoimmune rheumatic disease in Calgary, Alberta.

OBJECTIVE: To estimate the association between fine particulate (PM2.5) and nitrogen dioxide (NO2) pollution and systemic autoimmune rheumatic diseases (SARDs). METHODS: Associations between ambient air pollution (PM2.5 and NO2) and SARDs were assessed using land-use regression models for Calgary, Alberta and administrative health data (1993-2007). SARD case definitions were based on ≥2 physician claims, or ≥1 rheumatology billing code; or ≥1 hospitalization code (for systemic lupus, Sjogren's Syndrome, scleroderma, polymyositis, dermatomyositis, or undifferentiated connective tissue disease). Bayesian hierarchical latent class regression models estimated the probability that each resident was a SARD case, based on these case definitions. The sum of individual level probabilities provided the estimated number of cases in each area. The latent class model included terms for age, sex, and an interaction term between age and sex. Bayesian logistic regression models were used to generate adjusted odds ratios (OR) for NO2 and PM2.5. pollutant models, adjusting for neighbourhood income, age, sex, and an interaction between age and sex. We also examined models stratified for First-Nations (FN) and non-FN subgroups. RESULTS: Residents that were female and/or aged >45 had a greater probability of being a SARD case, with the highest OR estimates for older females. Independently, the odds of being a SARDs case increased with PM2.5 levels, but the results were inconclusive for NO2. The results stratified by FN and non-FN groups were not distinctly different. CONCLUSION: In this urban Canadian sample, adjusting for demographics, exposure to PM2.5 was associated with an increased risk of SARDs. The results for NO2 were inconclusive.

The prevalence of systemic autoimmune rheumatic diseases in Canadian pediatric populations: administrative database estimates.

To estimate systemic autoimmune rheumatic disease (SARD) prevalence using administrative data for pediatric populations in four Canadian provinces. Physician billing claims and inpatient hospitalizations from Alberta, Manitoba, Quebec, and Saskatchewan were used to define cases aged ≤18 years with a SARD diagnosis code in: one or more hospitalization, two or more physician visits within 2 years and at least 2 months apart, or one or more physician visit to a rheumatologist. Estimates ranged from 15.9/100,000 in Quebec [95% confidence interval (95% CI) 14.1, 18.0] to 23.0/100,000 in Manitoba (95% CI 17.9, 29.2). SARDs were more common in females than in males across all provinces. There was a slightly higher prevalence among those living in urban compared to rural areas of Alberta (rate difference 14.4, 95% CI 8.6, 20.1) and Saskatchewan (rate difference 13.8, 95% CI 1.0, 26.6). Our results provide population-based prevalence estimates of pediatric SARDs in four Canadian provinces.

Chronic inflammatory arthritis prevalence estimates for children and adolescents in three Canadian provinces.

There is a paucity of published population-based estimates of the prevalence of chronic inflammatory arthritis in the pediatric population. We used administrative health data to estimate the prevalence of chronic inflammatory arthritis in individuals ≤18 years in three Canadian provinces: Quebec, Manitoba, and Saskatchewan. Cases aged ≤18 years were identified by meeting any one of the following criteria: (a) ≥1 hospital discharge abstract with an ICD-9 code of 714 or ICD-10-CA codes of M05, M06 or M08, or (b) ≥2 ICD-9 714 billing codes ≥8 weeks apart, but within 2 years, or (c) ≥1 ICD-9 714 billing code by a rheumatologist. Crude prevalence estimates per 10,000 population were estimated with 95 % confidence intervals (CIs). Prevalence estimates were 11.7 per 10,000 individuals ≤18 years of age in Manitoba, 9.8 per 10,000 in Saskatchewan, and 8.0 per 10,000 in Quebec. In pairwise comparisons of rate differences, Manitoba and Saskatchewan had higher estimates than Quebec. Prevalence estimates were higher for females than males, with a difference of 5.9 cases per 10,000 residents (95 % CI 5.1, 6.7). Saskatchewan was the only province with a higher estimate in urban compared to rural residents (5.2, 95 % CI 2.5, 8.0). Variations in provincial estimates may be due to differences in underlying population characteristics. Although these estimates have face validity and are in keeping with the range of previously published pediatric prevalence estimates, studies to establish the empiric validity of case-finding algorithms are needed to advance research in pediatric chronic disease epidemiology.

Associations between neighbourhood walkability and daily steps in adults: a systematic review and meta-analysis.

BACKGROUND: Higher street connectivity, land use mix and residential density (collectively referred to as neighbourhood walkability) have been linked to higher levels of walking. The objective of our study was to summarize the current body of knowledge on the association between neighbourhood walkability and biosensor-assessed daily steps in adults. METHODS: We conducted a systematic search of PubMed, SCOPUS, and Embase (Ovid) for articles published prior to May 2014 on the association between walkability (based on Geographic Information Systems-derived street connectivity, land use mix, and/or residential density) and daily steps (pedometer or accelerometer-assessed) in adults. The mean differences in daily steps between adults living in high versus low walkable neighbourhoods were pooled across studies using a Bayesian hierarchical model. RESULTS: The search strategy yielded 8,744 unique abstracts. Thirty of these underwent full article review of which six met the inclusion criteria. Four of these studies were conducted in Europe and two were conducted in Asia. A meta-analysis of four of these six studies indicates that participants living in high compared to low walkable neighbourhoods accumulate 766 more steps per day (95 % credible interval 250, 1271). This accounts for approximately 8 % of recommended daily steps. CONCLUSIONS: The results of European and Asian studies support the hypothesis that higher neighbourhood walkability is associated with higher levels of biosensor-assessed walking in adults. More studies on this association are needed in North America.

Bayesian sample size determination for case-control studies when exposure may be misclassified.

Odds ratios are frequently used for estimating the effect of an exposure on the probability of disease in case-control studies. In planning such studies, methods for sample size determination are required to ensure sufficient accuracy in estimating odds ratios once the data are collected. Often, the exposure used in epidemiologic studies is not perfectly ascertained. This can arise from recall bias, the use of a proxy exposure measurement, uncertain work exposure history, and laboratory or other errors. The resulting misclassification can have large impacts on the accuracy and precision of estimators, and specialized estimation techniques have been developed to adjust for these biases. However, much less work has been done to account for the anticipated decrease in the precision of estimators at the design stage. Here, we develop methods for sample size determination for odds ratios in the presence of exposure misclassification by using several interval-based Bayesian criteria. By using a series of prototypical examples, we compare sample size requirements after adjustment for misclassification with those required when this problem is ignored. We illustrate the methods by planning a case-control study of the effect of late introduction of peanut to the diet of children to the subsequent development of peanut allergy.

Comparison of transradial and femoral approaches for percutaneous coronary interventions: a systematic review and hierarchical Bayesian meta-analysis.

BACKGROUND: Despite lower risks of access site-related complications with transradial approach (TRA), its clinical benefit for percutaneous coronary intervention (PCI) is uncertain. We conducted a systematic review and meta-analysis of clinical studies comparing TRA and transfemoral approach (TFA) for PCI. METHODS: Randomized trials and observational studies (1993-2011) comparing TRA with TFA for PCI with reports of ischemic and bleeding outcomes were included. Crude and adjusted (for age and sex) odds ratios (OR) were estimated by a hierarchical Bayesian random-effects model with prespecified stratification for observational and randomized designs. The primary outcomes were rates of death, combined incidence of death or myocardial infarction, bleeding, and transfusions, early (≤ 30 days) and late after PCI. RESULTS: We collected data from 76 studies (15 randomized, 61 observational) involving a total of 761,919 patients. Compared with TFA, TRA was associated with a 78% reduction in bleeding (OR 0.22, 95% credible interval [CrI] 0.16-0.29) and 80% in transfusions (OR 0.20, 95% CrI 0.11-0.32). These findings were consistent in both randomized and observational studies. Early after PCI, there was a 44% reduction of mortality with TRA (OR 0.56, 95% CrI 0.45-0.67), although the effect was mainly due to observational studies (OR 0.52, 95% CrI 0.40-0.63, adjusted OR 0.49 [95% CrI 0.37-0.60]), with an OR of 0.80 (95% CrI 0.49-1.23) in randomized trials. CONCLUSION: Our results combining observational and randomized studies show that PCI performed by TRA is associated with substantially less risks of bleeding and transfusions compared with TFA. Benefit on the incidence of death or combined death or myocardial infarction is found in observational studies but remains inconclusive in randomized trials.

Personal exposure to specific volatile organic compounds and acute changes in lung function and heart rate variability among urban cyclists.

BACKGROUND: Few studies have examined the acute cardiorespiratory effects of specific volatile organic compound (VOC) exposures from traffic pollution. METHODS: A cross-over study was conducted among 42 healthy adults during summer 2010 in Ottawa, Canada. Participants cycled for 1-h along high and low-traffic routes and VOC exposures were determined along each route. Lung function, exhaled nitric oxide, and heart rate variability were monitored before cycling and 1-4h after the start of cycling. Bayesian hierarchical models were used to examine the relationship between 26 VOCs and acute changes in clinical outcomes adjusted for potential confounding factors. RESULTS: Each inter-quartile range (IQR) increase in propane/butane exposure was associated with a 2.0 millisecond (ms) (95% CI: 0.65, 3.2) increase in SDNN (standard deviation of normal-to-normal intervals), a 24 ms(2) (95% CI: 6.6, 41) increase in HF (high frequency power), and a 65 ms(2) (95% CI: 11, 118) increase in LF (low frequency power) in the hours following cycling. IQR increases in ethane and isoprene were associated with a 5.8 ms (95% CI: -9.8, -1.7): decrease in SDNN and a 24 ms(2) (95% CI: -44, -7.9) decrease in HF, respectively. IQR increases in benzene exposure were associated with a 1.7 ppb (95% CI: 1.1, 2.3) increase in exhaled nitric oxide and each IQR increase in 3-methylhexane exposure was associated with a 102 mL (95% CI: -157, -47) decrease in forced expiratory volume in 1-s. CONCLUSIONS: Exposure to traffic-related VOCs may contribute to acute changes in lung function, inflammation, or heart rate variability.

Modeling continuous diagnostic test data using approximate Dirichlet process distributions.

There is now a large literature on the analysis of diagnostic test data. In the absence of a gold standard test, latent class analysis is most often used to estimate the prevalence of the condition of interest and the properties of the diagnostic tests. When test results are measured on a continuous scale, both parametric and nonparametric models have been proposed. Parametric methods such as the commonly used bi-normal model may not fit the data well; nonparametric methods developed to date have been relatively complex to apply in practice, and their properties have not been carefully evaluated in the diagnostic testing context. In this paper, we propose a simple yet flexible Bayesian nonparametric model which approximates a Dirichlet process for continuous data. We compare results from the nonparametric model with those from the bi-normal model via simulations, investigating both how much is lost in using a nonparametric model when the bi-normal model is correct and how much can be gained in using a nonparametric model when normality does not hold. We also carefully investigate the trade-offs that occur between flexibility and identifiability of the model as different Dirichlet process prior distributions are used. Motivated by an application to tuberculosis clustering, we extend our nonparametric model to accommodate two additional dichotomous tests and proceed to analyze these data using both the continuous test alone as well as all three tests together.

Domperidone increases harmful cardiac events in Parkinson's disease: A Bayesian re-analysis of an observational study.

OBJECTIVES: To assess the risks of ventricular tachyarrhythmia/sudden cardiac death (VT/SCD) with domperidone use in Parkinson's disease (PD). STUDY DESIGNS AND SETTINGS: Using Bayesian methods, results from an observationalstudy were combined with prior beliefs to calculate posterior probabilities of increasedrelative risk (RR)) of VT/SCD with use of domperidone compared to non-use and ofharm, defined as risk exceeding 15%. The analyses were carried with normallydistributed priors (log (RR)): uninformative (N(0,10)) or informative (N(0.53,179)),derived from a meta-analysis (OR (95%CI):1.70 (1.47-1.97)). Sensitivity analyses used:different priors' strengths, different priors, and Bayesian meta-analysis RESULTS: The uninformative prior yielded a RR: 1.23 (95% credible interval (CrI):0.94-1.62), like the published frequentist RR: 1.22 (95% CI:0.99-1.50), with 69% probabilityof harm. With an informative prior weighted at 100%, 50% and 10%, the RR were 1.63(1.41-1.88), 1.57 (1.31-1.91) and 1.39 (1.10-1.93), respectively. The correspondingprobabilities of harm were 100%, 99%, and 94%, respectively. CONCLUSION: While both the frequentist and Bayesian approaches with anuninformative prior were unable to reach a definitive conclusion concerning thearrhythmic risk of domperidone in PD patients, the Bayesian analysis with informativepriors showed a high probability of increased risk that was robust to multiple priorsensitivity analyses.

Bayesian estimation of the probability of asbestos exposure from lung fiber counts.

Asbestos exposure is a well-known risk factor for various lung diseases, and when they occur, workmen's compensation boards need to make decisions concerning the probability the cause is work related. In the absence of a definitive work history, measures of short and long asbestos fibers as well as counts of asbestos bodies in the lung can be used as diagnostic tests for asbestos exposure. Typically, data from one or more lung samples are available to estimate the probability of asbestos exposure, often by comparing the values with those from a reference nonexposed population. As there is no gold standard measure, we explore a variety of latent class models that take into account the mixed discrete/continuous nature of the data, that each subject may provide data from more than one lung sample, and that the within-subject results across different samples may be correlated. Our methods can be useful to compensation boards in providing individual level probabilities of exposure based on available data, to researchers who are studying the test properties for the various measures used in this area, and more generally, to other test situations with similar data structure.

Bayesian sample size for diagnostic test studies in the absence of a gold standard: Comparing identifiable with non-identifiable models.

Diagnostic tests rarely provide perfect results. The misclassification induced by imperfect sensitivities and specificities of diagnostic tests must be accounted for when planning prevalence studies or investigations into properties of new tests. The previous work has shown that applying a single imperfect test to estimate prevalence can often result in very large sample size requirements, and that sometimes even an infinite sample size is insufficient for precise estimation because the problem is non-identifiable. Adding a second test can sometimes reduce the sample size substantially, but infinite sample sizes can still occur as the problem remains non-identifiable. We investigate the further improvement possible when three diagnostic tests are to be applied. We first develop methods required for studies when three conditionally independent tests are available, using different Bayesian criteria. We then apply these criteria to prototypic scenarios, showing that large sample size reductions can occur compared to when only one or two tests are used. As the problem is now identifiable, infinite sample sizes cannot occur except in pathological situations. Finally, we relax the conditional independence assumption, demonstrating in this once again non-identifiable situation that sample sizes may substantially grow and possibly be infinite. We apply our methods to the planning of two infectious disease studies, the first designed to estimate the prevalence of Strongyloides infection, and the second relating to estimating the sensitivity of a new test for tuberculosis transmission. The much smaller sample sizes that are typically required when three as compared to one or two tests are used should encourage researchers to plan their studies using more than two diagnostic tests whenever possible. User-friendly software is available for both design and analysis stages greatly facilitating the use of these methods.

Behavioural interventions for smoking cessation: a meta-analysis of randomized controlled trials.

AIMS: Widely varying estimates of treatment effects have been reported in randomized controlled trials (RCTs) investigating the efficacy of behavioural interventions for smoking cessation. Previous meta-analyses investigating behavioural interventions have important limitations and do not include recently published RCTs. We undertook a meta-analysis of RCTs to synthesize the treatment effects of four behavioural interventions, including minimal clinical intervention (brief advice from a healthcare worker), and intensive interventions, including individual, group, and telephone counselling. METHODS AND RESULTS: We searched the CDC Tobacco Information and Prevention, Cochrane Library, EMBASE, Medline, and PsycINFO databases. We included only RCTs that reported biochemically validated smoking cessation outcomes at 6 and/or 12 months after the target quit date. Outcomes were aggregated using hierarchical Bayesian random-effects models. We identified 50 RCTs, which randomized n = 26 927 patients (minimal clinical intervention: 9 RCTs, n = 6456; individual counselling: 23 RCTs, n = 8646; group counselling: 12 RCTs, n = 3600; telephone counselling: 10 RCTs, n = 8225). The estimated mean treatment effects were minimal clinical intervention [odds ratio (OR) 1.50, 95% credible interval (CrI) 0.84-2.78], individual counselling (OR 1.49, 95% CrI 1.08-2.07), group counselling (OR 1.76, 95% CrI 1.11-2.93), and telephone counselling (OR 1.58, 95% CrI 1.15-2.29). CONCLUSION: Intensive behavioural interventions result in substantial increases in smoking abstinence compared with control. Although minimal clinical intervention may increase smoking abstinence, there is insufficient evidence to draw strong conclusions regarding its efficacy.

Immediate vs Delayed Stenting in ST-Elevation Myocardial Infarction: Rationale and Design of the International PRIMACY Bayesian Randomized Controlled Trial.

BACKGROUND: Primary percutaneous coronary intervention is used to restore blood flow in the infarct-related coronary artery, followed by immediate stenting to prevent reocclusion. Stents implanted in thrombus-laden arteries cause distal embolization, which paradoxically impairs myocardial reperfusion and ventricular function. Whether a strategy of delayed stenting improves outcomes in patients with acute ST-elevation myocardial infarction (STEMI) is uncertain. METHODS: The Primary Reperfusion Secondary Stenting (PRIMACY) is a Bayesian prospective, randomized, open-label, blinded end point trial in which delayed vs immediate stenting in patients with STEMI were compared for prevention of cardiovascular death, nonfatal myocardial infarction, heart failure, or unplanned target vessel revascularization at 9 months. All participants were immediately reperfused, but those assigned to the delayed arm underwent stenting after an interval of 24 to 48 hours. This interval was bridged with antithrombin therapy to reduce thrombus burden. In the principal Bayesian hierarchical random effects analysis, data from exchangeable trials will be combined into a study prior and updated with PRIMACY into a posterior probability of efficacy. RESULTS: A total of 305 participants were randomized across 15 centres in France and Canada between April 2014 and September 2017. At baseline, the median age of participants was 59 years, 81% were male, and 3% had a history of percutaneous coronary intervention. Results from PRIMACY will be updated from the patient-level data of 1568 participants enrolled in the Deferred Stent Trial in STEMI (DEFER; United Kingdom), Minimalist Immediate Mechanical Intervention (MIMI; France), Danish Trial in Acute Myocardial Infarction-3 (DANAMI-3; Denmark), and Impact of Immediate Stent Implantation Versus Deferred Stent Implantation on Infarct Size and Microvascular Perfusion in Patients With ST Segment-Elevation Myocardial Infarction (INNOVATION, South Korea) trials. CONCLUSIONS: We expect to clarify whether delayed stenting can safely reduce the occurrence of adverse cardiovascular end points compared with immediate stenting in patients with STEMI.

Antiphospholipid antibodies predict imminent vascular events independently from other risk factors in a prospective cohort.

Antiphospholipid antibodies (aPL) are associated with vascular events, but the magnitude of this risk, alone, or in combination with other atherogenic and thrombophilic risk factors, remains unclear. A prospective cohort of 415 persons was studied for arterial and venous events (AE and VE) over a median time of 7.4 years. aPL and coagulation abnormalities were measured upon beginning of the study and annually for the first four years. Within the cohort, a nested case-control study was conducted to investigate the role of endothelial and inflammatory markers in predicting new vascular events. Forty-five individuals had new vascular events: 18 occurred during the first year of follow-up. The proportion of event-free survivors at eight years was 90% (95%CI = 87%, 94%) for aPL-negative and 72% (60%, 85%) for aPL-positive individuals, respectively. Predictors for new AE were previous AE (HR = 5.7 [2.7, 12.0]), diabetes (5.6 [2.4, 13.2]), aPL positivity (2.6 ([1.2, 5.9]), and age (1.04 [1.01, 1.07]). New VE were predicted by previous VE (6.1 [1.9, 19.9]), anti-beta2-glycoprotein I (abeta2GPI) positivity (5.8 [1.4, 24.1]), activated protein C resistance (APCR) (4.1 [1.1, 15.1]), and gender (3.7 [1.1, 12.9]). In the nested case-control study, similar predictors were observed for AE, while abnormal APCR (OR = 5.5 [1.1, 26.6]) and elevated von Willebrand factor (vWF) (OR = 5.0 [1.2, 19.8]) best predicted VE. We demonstrate that aPL independently predict new vascular events and discriminate between individuals with and without events in the first two years of follow-up, indicating that aPL are associated with a short-term risk of developing new and recurrent vascular events.

Multi-host transmission dynamics of Schistosoma japonicum in Samar province, the Philippines.

BACKGROUND: Among the 6.7 million people living in areas of the Philippines where infection with Schistosoma japonicum is considered endemic, even within small geographical areas levels of infection vary considerably. In general, the ecological drivers of this variability are not well described. Unlike other schistosomes, S. japonicum is known to infect several mammalian hosts. However, the relative contribution of different hosts to the transmission cycle is not well understood. Here, we characterize the transmission dynamics of S. japonicum using data from an extensive field study and a mathematical transmission model. METHODS AND FINDINGS: In this study, stool samples were obtained from 5,623 humans and 5,899 potential nonhuman mammalian hosts in 50 villages in the Province of Samar, the Philippines. These data, with variable numbers of samples per individual, were adjusted for known specificities and sensitivities of the measurement techniques before being used to estimate the parameters of a mathematical transmission model, under the assumption that the dynamic transmission processes of infection and recovery were in a steady state in each village. The model was structured to allow variable rates of transmission from different mammals (humans, dogs, cats, pigs, domesticated water buffalo, and rats) to snails and from snails to mammals. First, we held transmission parameters constant for all villages and found that no combination of mammalian population size and prevalence of infectivity could explain the observed variability in prevalence of infection between villages. We then allowed either the underlying rate of transmission (a) from snails to mammals or (b) from mammals to snails to vary by village. Our data provided substantially more support for model structure (a) than for model structure (b). Fitted values for the village-level transmission intensity from snails to mammals appeared to be strongly spatially correlated, which is consistent with results from descriptive hierarchical analyses. CONCLUSIONS: Our results suggest that the process of acquiring mammalian S. japonicum infection is more important in explaining differences in prevalence of infection between villages than the process of snails becoming infected. Also, the contribution from water buffaloes to human S. japonicum infection in the Philippines is less important than has been recently observed for bovines in China. These findings have implications for the prioritization of mitigating interventions against S. japonicum transmission.