Provincial variation in colorectal cancer screening adherence in Canada; evidence from the Canadian Partnership for Tomorrow's Health.

Introduction: Although colorectal cancer (CRC) screening program is proven to reduce CRC incidence and mortality, understanding patterns and predictors of suboptimal adherence in screening program requires further investigation in Canada. Methods: We used self-reported data from five regional cohorts of the Canadian Partnership for Tomorrow's Health (CanPath), namely the BC Generations Project (BCGP), Alberta's Tomorrow Project (ATP), the Ontario Health Study (OHS), Quebec's CARTaGENE, and the Atlantic Partnership for Tomorrow's Health Study (Atlantic PATH). We stratified participants into the following four risk categories: 1) age 50-74 years, 2) family history in a first-degree relative, 3) personal history of chronic inflammatory bowel disease and/or polyps, and 4) co-existence of personal risk and family history. Multivariable logistic regression was used to identify predictors of adherence to the screening guidelines. Results: Adherence to CRC screening varied considerably between regions, ranging from 16.6% in CARTaGENE to 47.7% in OHS. Compared to the largest cohort OHS, the likelihood of non-adherence to CRC screening was significantly higher in BCGP (OR 1.15, 95% CI 1.11-1.19), the Atlantic PATH (OR 1.90, 95% CI 1.82-1.99) and CARTaGENE (OR 5.10, 95% CI 4.85-5.36). Low physical activity, current smoking, presence of personal risk, family history of CRC significantly reduced the likelihood of adherence to screening recommendations. Discussion/conclusion: Compared to the national target of ≥ 60% for participation in CRC screening, adherence to regular CRC screening was suboptimal in this cohort of Canadians and varied by region. Further efforts are needed to identify the specific barriers to screening adherence in different provinces and across risk categories.

Response to Biologic Drugs in Patients With Rheumatoid Arthritis and Antidrug Antibodies.

Importance: There are conflicting data on the association of antidrug antibodies with response to biologic disease-modifying antirheumatic drugs (bDMARDs) in rheumatoid arthritis (RA). Objective: To analyze the association of antidrug antibodies with response to treatment for RA. Design, Setting, and Participants: This cohort study analyzed data from the ABI-RA (Anti-Biopharmaceutical Immunization: Prediction and Analysis of Clinical Relevance to Minimize the Risk of Immunization in Rheumatoid Arthritis Patients) multicentric, open, prospective study of patients with RA from 27 recruiting centers in 4 European countries (France, Italy, the Netherlands, and the UK). Eligible patients were 18 years or older, had RA diagnosis, and were initiating a new bDMARD. Recruitment spanned from March 3, 2014, to June 21, 2016. The study was completed in June 2018, and data were analyzed in June 2022. Exposures: Patients were treated with a new bDMARD: adalimumab, infliximab (grouped as anti-tumor necrosis factor [TNF] monoclonal antibodies [mAbs]), etanercept, tocilizumab, and rituximab according to the choice of the treating physician. Main Outcomes and Measures: The primary outcome was the association of antidrug antibody positivity with EULAR (European Alliance of Associations for Rheumatology; formerly, European League Against Rheumatism) response to treatment at month 12 assessed through univariate logistic regression. The secondary end points were the EULAR response at month 6 and at visits from month 6 to months 15 to 18 using generalized estimating equation models. Detection of antidrug antibody serum levels was performed at months 1, 3, 6, 12, and 15 to 18 using electrochemiluminescence (Meso Scale Discovery) and drug concentration for anti-TNF mAbs, and etanercept in the serum was measured using enzyme-linked immunosorbent assay. Results: Of the 254 patients recruited, 230 (mean [SD] age, 54.3 [13.7] years; 177 females [77.0%]) were analyzed. At month 12, antidrug antibody positivity was 38.2% in patients who were treated with anti-TNF mAbs, 6.1% with etanercept, 50.0% with rituximab, and 20.0% with tocilizumab. There was an inverse association between antidrug antibody positivity (odds ratio [OR], 0.19; 95% CI, 0.09-0.38; P < .001) directed against all biologic drugs and EULAR response at month 12. Analyzing all the visits starting at month 6 using generalized estimating equation models confirmed the inverse association between antidrug antibody positivity and EULAR response (OR, 0.35; 95% CI, 0.18-0.65; P < .001). A similar association was found for tocilizumab alone (OR, 0.18; 95% CI, 0.04-0.83; P = .03). In the multivariable analysis, antidrug antibodies, body mass index, and rheumatoid factor were independently inversely associated with response to treatment. There was a significantly higher drug concentration of anti-TNF mAbs in patients with antidrug antibody-negative vs antidrug antibody-positive status (mean difference, -9.6 [95% CI, -12.4 to -6.9] mg/L; P < 001). Drug concentrations of etanercept (mean difference, 0.70 [95% CI, 0.2-1.2] mg/L; P = .005) and adalimumab (mean difference, 1.8 [95% CI, 0.4-3.2] mg/L; P = .01) were lower in nonresponders vs responders. Methotrexate comedication at baseline was inversely associated with antidrug antibodies (OR, 0.50; 95% CI, 0.25-1.00; P = .05). Conclusions and Relevance: Results of this prospective cohort study suggest an association between antidrug antibodies and nonresponse to bDMARDs in patients with RA. Monitoring antidrug antibodies could be considered in the treatment of these patients, particularly nonresponders to biologic RA drugs.

Evaluation of the accuracy of the PLCOm2012 6-year lung cancer risk prediction model among smokers in the CARTaGENE population-based cohort.

BACKGROUND: The PLCOm2012 prediction tool for risk of lung cancer has been proposed for a pilot program for lung cancer screening in Quebec, but has not been validated in this population. We sought to validate PLCOm2012 in a cohort of Quebec residents, and to determine the hypothetical performance of different screening strategies. METHODS: We included smokers without a history of lung cancer from the population-based CARTaGENE cohort. To assess PLCOm2012 calibration and discrimination, we determined the ratio of expected to observed number of cases, as well as the sensitivity, specificity and positive predictive values of different risk thresholds. To assess the performance of screening strategies if applied between Jan. 1, 1998, and Dec. 31, 2015, we tested different thresholds of the PLCOm2012 detection of lung cancer over 6 years (1.51%, 1.70% and 2.00%), the criteria of Quebec's pilot program (for people aged 55-74 yr and 50-74 yr) and recommendations from 2021 United States and 2016 Canada guidelines. We assessed shift and serial scenarios of screening, whereby eligibility was assessed annually or every 6 years, respectively. RESULTS: Among 11 652 participants, 176 (1.51%) lung cancers were diagnosed in 6 years. The PLCOm2012 tool underestimated the number of cases (expected-to-observed ratio 0.68, 95% confidence interval [CI] 0.59-0.79), but the discrimination was good (C-statistic 0.727, 95% CI 0.679-0.770). From a threshold of 1.51% to 2.00%, sensitivities ranged from 52.3% (95% CI 44.6%-59.8%) to 44.9% (95% CI 37.4%-52.6%), specificities ranged from 81.6% (95% CI 80.8%-82.3%) to 87.7% (95% CI 87.0%-88.3%) and positive predictive values ranged from 4.2% (95% CI 3.4%-5.1%) to 5.3% (95% CI 4.2%-6.5%). Overall, 8938 participants had sufficient data to test performance of screening strategies. If eligibility was estimated annually, Quebec pilot criteria would have detected fewer cancers than PLCOm2012 at a 2.00% threshold (48.3% v. 50.2%) for a similar number of scans per detected cancer. If eligibility was estimated every 6 years, up to 26 fewer lung cancers would have been detected; however, this scenario led to higher positive predictive values (highest for PLCOm2012 with a 2.00% threshold at 6.0%, 95% CI 4.8%-7.3%). INTERPRETATION: In a cohort of Quebec smokers, the PLCOm2012 risk prediction tool had good discrimination in detecting lung cancer, but it may be helpful to adjust the intercept to improve calibration. The implementation of risk prediction models in some of the provinces of Canada should be done with caution.

Machine learning predicts response to TNF inhibitors in rheumatoid arthritis: results on the ESPOIR and ABIRISK cohorts.

OBJECTIVES: Around 30% of patients with rheumatoid arthritis (RA) do not respond to tumour necrosis factor inhibitors (TNFi). We aimed to predict patient response to TNFi using machine learning on simple clinical and biological data. METHODS: We used data from the RA ESPOIR cohort to train our models. The endpoints were the EULAR response and the change in Disease Activity Score (DAS28). We compared the performances of multiple models (linear regression, random forest, XGBoost and CatBoost) on the training set and cross-validated them using the area under the receiver operating characteristic curve (AUROC) or the mean squared error. The best model was then evaluated on a replication cohort (ABIRISK). RESULTS: We included 161 patients from ESPOIR and 118 patients from ABIRISK. The key selected features were DAS28, lymphocytes, ALT (aspartate aminotransferase), neutrophils, age, weight, and smoking status. When predicting EULAR response, CatBoost achieved the best performances of the four tested models. It reached an AUROC of 0.72 (0.68-0.73) on the train set (ESPOIR). Better results were obtained on the train set when etanercept and monoclonal antibodies were analysed separately. On the test set (ABIRISK), these models respectively achieved on AUROC of 0.70 (0.57-0.82) and 0.71 (0.55-0.86). Two decision thresholds were tested. The first prioritised a high confidence in identifying responders and yielded a confidence up to 90% for predicting response. The second prioritised a high confidence in identifying inadequate responders and yielded a confidence up to 70% for predicting non-response. The change in DAS28 was predicted with an average error of 1.1 DAS28 points. CONCLUSION: The machine learning models developed allowed predicting patient response to TNFi exclusively using data available in clinical routine.

A Genetic Association Test Accounting for Skewed X-Inactivation With Application to Biotherapy Immunogenicity in Patients With Autoimmune Diseases.

Despite being assayed on commercialized DNA chips, the X chromosome is commonly excluded from genome-wide association studies (GWAS). One of the reasons is the complexity to analyze the data taking into account the X-chromosome inactivation (XCI) process in women and in particular the XCI process with a potentially skewed pattern. This is the case when investigating the role of X-linked genetic variants in the occurrence of anti-drug antibodies (ADAs) in patients with autoimmune diseases treated by biotherapies. In this context, we propose a novel test statistic for selecting loci of interest harbored by the X chromosome that are associated with time-to-event data taking into account skewed X-inactivation (XCI-S). The proposed statistic relies on a semi-parametric additive hazard model and is straightforward to implement. Results from the simulation study show that the test provides higher power gains than the score tests from the Cox model (under XCI process or its escape) and the Xu et al.'s XCI-S likelihood ratio test. We applied the test to the data from the real-world observational multicohort study set-up by the IMI-funded ABIRISK consortium for identifying X chromosome susceptibility loci for drug immunogenicity in patients with autoimmune diseases treated by biotherapies. The test allowed us to select two single nucleotide polymorphisms (SNPs) with high linkage disequilibrium (rs5991366 and rs5991394) located in the cytoband Xp22.2 that would have been overlooked by the Cox score tests and the Xu et al.'s XCI-S likelihood ratio test. Both SNPs showed a similar protective effect for drug immunogenicity without any occurrence of ADA positivity for the homozygous females and hemizygous males for the alternative allele. To our knowledge, this is the first study to investigate the association between X chromosome loci and the occurrence of anti-drug antibodies. We think that more X-Chromosome GWAS should be performed and that the test is well-suited for identifying X-Chromosome SNPs, while taking into account all patterns of the skewed X-Chromosome inactivation process.

Analyzing cohort studies with interval-censored data: A new model-based linear rank-type test.

To compare two or more survival distributions with interval-censored data, various nonparametric tests have been proposed. Some are based on the G ρ $$ {G}^{\rho } $$ -family introduced by Harrington and Fleming (1991) that allows flexibility for situations in which the hazard ratio decreases monotonically to unity. However, it is unclear how to choose the appropriate value of the parameter ρ $$ \rho $$ . In this work, we propose a novel linear rank-type test for analyzing interval-censored data that derived from a proportional reversed hazard model. We show its relationship with decreasing hazard ratio. This test statistic provides an alternative to the G ρ $$ {G}^{\rho } $$ -based test statistics by bypassing the choice of the ρ $$ \rho $$ parameter. Simulation results show its good behavior. Two studies on breast cancer and drug users illustrate its practical uses and highlight findings that would have been overlooked if other tests had been used. The test is easy to implement with standard software and can be used for a wide range of situations with interval-censored data to test the equality of survival distributions between two or more independent groups.

Socio-Demographic Factors Associated With COVID-19 Vaccine Hesitancy Among Middle-Aged Adults During the Quebec's Vaccination Campaign.

Introduction: The objective of this study was to characterize the combinations of demographic and socioeconomic characteristics associated to the unwillingness to receive the COVID-19 vaccines during the 2021 Quebec's vaccination campaign. Materials and Methods: In March-June 2021, we conducted an online survey of the participants of the CARTaGENE population-based cohort, composed of middle-aged and older adults. After comparing the vaccinated and unvaccinated participants, we investigated vaccine hesitancy among participants who were unvaccinated. For identifying homogeneous groups of individuals with respect to vaccine hesitancy, we used a machine learning approach based on a hybrid tree-based model. Results: Among the 6,105 participants of the vaccine cohort, 3,553 (58.2%) had at least one dose of COVID-19 vaccine. Among the 2,552 participants, 221 (8.7%) did not want to be vaccinated (91) or were uncertain (130). The median age for the unvaccinated participants was 59.3 years [IQR 54.7-63.9]. The optimal hybrid tree-based model identified seven groups. Individuals having a household income lower than $100,000 and being born outside of Canada had the highest rate of vaccine hesitancy (28% [95% CI 19.8-36.3]). For those born in Canada, the vaccine hesitancy rate among the individuals who have a household income below $50,000 before the pandemic or are Non-retired was of 12.1% [95% CI 8.7-15.5] and 10.6% [95% CI 7.6-13.7], respectively. For the participants with a high household income before the pandemic (more than $100,000) and a low level of education, those who experienced a loss of income during the pandemic had a high level of hesitancy (19.2% [8.5-29.9]) whereas others who did not experience a loss of income had a lower level of hesitancy (6.0% [2.8-9.2]). For the other groups, the level of hesitancy was low of around 3% (3.2% [95% CI 1.9-4.4] and 3.4% [95% CI 1.5-5.2]). Discussion: Public health initiatives to tackle vaccine hesitancy should take into account these socio-economic determinants and deliver personalized messages toward people having socio-economic difficulties and/or being part of socio-cultural minorities.

Five-year absolute risk estimates of colorectal cancer based on CCRAT model and polygenic risk scores: A validation study using the Quebec population-based cohort CARTaGENE.

The objective was to evaluate the predictive performance of the Colorectal Cancer Risk Assessment Tool (CCRAT) and three polygenic risk scores (Hsu et al., 2015; Law et al., 2019, Archambault et al., 2020) to predict the occurrence of colorectal cancer at five years in a Quebec population-based cohort. By using the CARTaGENE cohort, we computed the absolute risk of colorectal cancer with the CCRAT model, the polygenic risk scores (PRS) and combined clinico-genetic models (CCRAT + PRS). We also tailored the CCRAT model by using the marginal age-specific colorectal incidence rates in Canada and the risk score distribution. We reported the calibration and the discrimination. Performances of the PRSs, combined and tailored CCRAT models were compared to the original CCRAT model. The expected-to-observed ratio of the original CCRAT model was 0.54 [0.43-0.68]. The c-index was 74.79 [68.3-80.5]. The tailored CCRAT model improved the expected-to-observed ratio (0.74 [0.59-0.94]) and c-index (76.39 [69.7-82.1]). All PRS improved the expected-to-observed ratios (around 0.83, confidence intervals including one). PRSs' c-indexes were not significantly different from CCRAT models. Results from the combined models were close to those from the PRS models, Archambault combined model's c-index being significantly higher than the original and tailored CCRAT models (78.67 [70.8-86.5]; p < 0.001 and p = 0.028, respectively). In this Quebec cohort, CCRAT model has a good discrimination with a poor calibration. While the tailored CCRAT provides some gain in calibration, clinico-genetic models improved both calibration and discrimination. However, better calibrations must be obtained before a practical use among the inhabitants of Quebec province.

Normal sex and age-specific parameters in a multi-ethnic population: a cardiovascular magnetic resonance study of the Canadian Alliance for Healthy Hearts and Minds cohort.

BACKGROUND: Despite the growing utility of cardiovascular magnetic resonance (CMR) for cardiac morphology and function, sex and age-specific normal reference values derived from large, multi-ethnic data sets are lacking. Furthermore, most available studies use a simplified tracing methodology. Using a large cohort of participants without history of cardiovascular disease (CVD) or risk factors from the Canadian Alliance for Healthy Heart and Minds, we sought to establish a robust set of reference values for ventricular and atrial parameters using an anatomically correct contouring method, and to determine the influence of age and sex on ventricular parameters. METHODS AND RESULTS: Participants (n = 3206, 65% females; age 55.2 ± 8.4 years for females and 55.1 ± 8.8 years for men) underwent CMR using standard methods for quantitative measurements of cardiac parameters. Normal ventricular and atrial reference values are provided: (1) for males and females, (2) stratified by four age categories, and (3) for different races/ethnicities. Values are reported as absolute, indexed to body surface area, or height. Ventricular volumes and mass were significantly larger for males than females (p < 0.001). Ventricular ejection fraction was significantly diminished in males as compared to females (p < 0.001). Indexed left ventricular (LV) end-systolic, end-diastolic volumes, mass and right ventricular (RV) parameters significantly decreased as age increased for both sexes (p < 0.001). For females, but not men, mean LV and RVEF significantly increased with age (p < 0.001). CONCLUSION: Using anatomically correct contouring methodology, we provide accurate sex and age-specific normal reference values for CMR parameters derived from the largest, multi-ethnic population free of CVD to date. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, NCT02220582. Registered 20 August 2014-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02220582 .

Integrating NGS-derived mutational profiling in the diagnosis of multiple lung adenocarcinomas.

MICROABSTRACT: Integration of Next Generation Sequencing (NGS) information for use in distinguishing between Multiple Primary Lung Cancer and intrapulmonary metastasis was evaluated. We used a probabilistic model, comprehensive histologic assessment and NGS to classify patients. Integrating NGS data confirmed initial diagnosis (n = 41), revised the diagnosis (n = 12), while resulted in non-informative data (n = 8). Accuracy of diagnosis can be significantly improved with integration of NGS data. BACKGROUND: Distinguishing between multiple primary lung cancers (MPLC) and intrapulmonary metastases (IPM) is challenging. The goal of this study was to evaluate how Next Generation Sequencing (NGS) information may be integrated in the diagnostic strategy. PATIENTS AND METHODS: Patients with multiple lung adenocarcinomas were classified using both the comprehensive histologic assessment and NGS. We computed the joint probability of each pair having independent mutations by chance (thus being classified as MPLC). These probabilities were computed using the marginal mutation rates of each mutation, and the known negative dependencies between driver genes and different gene loci. With these NGS-driven data, cases were re-classified as MPLC or IPM. RESULTS: We analyzed 61 patients with a total of 131 tumors. The most frequent mutation was KRAS (57.3%) which occured at a rate higher than expected (p < 0.001) in lung cancer. No mutation was detected in 25/131 tumors (19.1%). Discordant molecular findings between tumor sites were found in 46 patients (75.4%); 11 patients (18.0%) had concordant molecular findings, and 4 patients (6.6%) had concordant molecular findings at 2 of the 3 sites. After integration of the NGS data, the initial diagnosis was confirmed for 41 patients (67.2%), the diagnosis was revised for 12 patients (19.7%) or was considered as non-informative for 8 patients (13.1%). CONCLUSION: Integrating the information of NGS data may significantly improve accuracy of diagnosis and staging.

Effect of Cognitive Reserve on the Association of Vascular Brain Injury With Cognition: Analysis of the PURE and CAHHM Studies.

BACKGROUND AND OBJECTIVES: To determine whether cognitive reserve attenuates the association of vascular brain injury with cognition. METHODS: Cross-sectional data were analyzed from 2 harmonized studies: the Canadian Alliance for Healthy Hearts and Healthy Minds (CAHHM) and the Prospective Urban and Rural Epidemiology (PURE) study. Markers of cognitive reserve were education, involvement in social activities, marital status, height, and leisure physical activity, which were combined into a composite score. Vascular brain injury was defined as nonlacunar brain infarcts or high white matter hyperintensity (WMH) burden on MRI. Cognition was assessed using the Montreal Cognitive Assessment Tool (MoCA) and the Digit Symbol Substitution Test (DSST). RESULTS: There were 10,916 participants age 35-81. Mean age was 58.8 years (range 35-81) and 55.8% were female. Education, moderate leisure physical activity, being in a marital partnership, being taller, and participating in social groups were each independently associated with higher cognition, as was the composite cognitive reserve score. Vascular brain injury was associated with lower cognition (ß -0.35 [95% confidence interval [CI] -0.53 to -0.17] for MoCA and ß -2.19 [95% CI -3.22 to -1.15] for DSST) but the association was not modified by the composite cognitive reserve variable (interaction p = 0.59 for MoCA and p = 0.72 for DSST). CONCLUSIONS: Both vascular brain injury and markers of cognitive reserve are associated with cognition. However, the effects were independent such that the adverse effects of covert vascular brain injury were not attenuated by higher cognitive reserve. To improve cognitive brain health, interventions to both prevent cerebrovascular disease and promote positive lifestyles are needed.

Epidemiological characteristics of the COVID-19 spring outbreak in Quebec, Canada: a population-based study.

BACKGROUND: By mid-July 2020, more than 108,000 COVID-19 cases had been diagnosed in Canada with more than half in the province of Quebec. In this context, we launched a study to analyze the epidemiological characteristics and the socio-economic impact of the spring outbreak in the population. METHOD: We conducted an online survey of the participants of the CARTaGENE population-based cohort, composed of middle-aged and older adults. We collected information on socio-demographic, lifestyle, health condition, COVID-19 related symptoms and COVID-19 testing. We studied the association between these factors and two outcomes: the status of having been tested for SARS-CoV-2 and the status of having received a positive test. These associations were measured with univariate and multivariate analyses using a hybrid tree-based regression model. RESULTS: Among the 8,129 respondents from the CARTaGENE cohort, 649 were tested for COVID-19 and 41 were positive. Medical workers and individuals having a contact with a COVID-19 patient had the highest probabilities of being tested (32% and 42.4%, respectively) and of being positive (17.2% and 13.0%, respectively) among those tested. Approximately 8% of the participants declared that they have experienced at least one of the four COVID-19 related symptoms chosen by the Public Health authorities (fever, cough, dyspnea, anosmia) but were not tested. Results from the tree-based model analyses adjusted on exposure factors showed that the combination of dyspnea, dry cough and fever was highly associated with being tested whereas anosmia, fever, and headache were the most discriminant factors for having a positive test among those tested. During the spring outbreak, more than one third of the participants have experienced a decrease in access to health services. There were gender and age differences in the socio-economic and emotional impacts of the pandemic. CONCLUSION: We have shown some discrepancies between the symptoms associated with being tested and being positive. In particular, the anosmia is a major discriminant symptom for positivity whereas ear-nose-throat symptoms seem not to be COVID-19 related. The results also emphasize the need of increasing the accessibility of testing for the general population.

Validation of breast cancer risk assessment tools on a French-Canadian population-based cohort.

OBJECTIVES: Evaluate the accuracy of the Breast Cancer Risk Assessment Tool (BCRAT), International Breast Cancer Intervention Study risk evaluation tool (IBIS), Polygenic Risk Scores (PRS) and combined scores (BCRAT+PRS and IBIS +PRS) to predict the occurrence of invasive breast cancers at 5 years in a French-Canadian population. DESIGN: Population-based cohort study. SETTING: We used the population-based cohort CARTaGENE, composed of 43 037 Quebec residents aged between 40 and 69 years and broadly representative of the population recorded on the Quebec administrative health insurance registries. PARTICIPANTS: 10 200 women recruited in 2009-2010 were included for validating BCRAT and IBIS and 4555 with genetic information for validating the PRS and combined scores. OUTCOME MEASURES: We computed the absolute risks of breast cancer at 5 years using BCRAT, IBIS, four published PRS and combined models. We reported the overall calibration performance, goodness-of-fit test and discriminatory accuracy. RESULTS: 131 (1.28%) women developed a breast cancer at 5 years for validating BCRAT and IBIS and 58 (1.27%) for validating PRS and combined scores. Median follow-up was 5 years. BCRAT and IBIS had an overall expected-to-observed ratio of 1.01 (0.85-1.19) and 1.02 (0.86-1.21) but with significant differences when partitioning by risk groups (p<0.05). IBIS' c-index was significantly higher than BCRAT (63.42 (59.35-67.49) vs 58.63 (54.05-63.21), p=0.013). PRS scores had a global calibration around 0.82, with a CI including one, and non-significant goodness-of-fit tests. PRS' c-indexes were non-significantly higher than BCRAT and IBIS, the highest being 64.43 (58.23-70.63). Combined models did not improve the results. CONCLUSIONS: In this French-Canadian population-based cohort, BCRAT and IBIS have good mean calibration that could be improved for risk subgroups, and modest discriminatory accuracy. Despite this modest discriminatory power, these tools can be of interest for primary care physicians for delivering a personalised message to their high-risk patients, regarding screening and lifestyle counselling.

Diabetes, Brain Infarcts, Cognition, and Small Vessels in the Canadian Alliance for Healthy Hearts and Minds Study.

BACKGROUND: Diabetes is a risk factor for cerebrovascular disease and cognitive impairment. The anatomical basis for this is uncertain. METHODS: The Canadian Alliance for Healthy Hearts and Minds collected brain and carotid magnetic resonance imaging (MRI) and 2 cognitive tests (the Digit Symbol Substitution Test and the Montreal Cognitive Assessment test) in a cross-sectional sample of men and women. Brain MRIs identified brain infarcts (BI), lacunar BI, high white matter hyperintensity (WMH), vascular brain injury (VBI; BI or high WMH), and small vessel VBI (lacunar BI or high WMH). Carotid MRIs estimated carotid wall volume, a measure of subclinical atherosclerosis. Cognitive scores were standardized to each site's mean score, and cognitive impairment was identified by 1 or both test scores ≤1 standard deviation below the site's mean score on that test. RESULTS: The 7733 participants included 495 participants (6.4%) with diabetes, of whom 388 were taking diabetes drugs. After age and sex adjustment, diabetes was independently associated with BI (odds ratio [OR] 1.53, 95% confidence interval [CI] 1.05, 2.24), VBI (OR 1.64, 95% CI 1.26, 2.13), small vessel VBI (OR 1.67, 95% CI 1.28, 2.19), and cognitive impairment (OR 1.47, 95% CI 1.20, 1.80). The association between diabetes and small vessel VBI persisted after adjustment for cerebrovascular disease risk factors and nonlacunar infarcts (OR 1.52, 95% CI 1.15, 2.01), and the association with cognitive impairment persisted after adjustment for small vessel VBI (OR 1.27, 95% CI 1.03, 1.56). CONCLUSION: Small vessel disease characterizes much of the relationship between diabetes and VBI. However, additional factors are required to disentangle the relationship between diabetes and cognitive impairment.

The Canadian Alliance for Healthy Hearts and Minds: How Well Does It Reflect the Canadian Population?

BACKGROUND: The intent of the Canadian Alliance for Healthy Hearts and Minds (CAHHM) cohort is to understand the early determinants of subclinical cardiac and vascular disease and progression in adults selected from existing cohorts-the Canadian Partnership for Tomorrow's Health, the Prospective Urban and Rural Evaluation (PURE) cohort, and the Montreal Heart Institute Biobank. We evaluated how well the CAHHM-Health Services Research (CAHHM-HSR) subcohort reflects the Canadian population. METHODS: A cross-sectional design was used among a prospective cohort of community-dwelling adults aged 35-69 years who met the CAHHM inclusion criteria, and a cohort of adults aged 35-69 years who responded to the 2015 Canadian Community Health Survey-Rapid Response module. The INTERHEART risk score was calculated at the individual level with means and proportions reported at the overall and provincial level. RESULTS: There are modest differences between CAHHM-HSR study participants and the 2015 Canadian Community Health Survey-Rapid Response respondents in age (56.3 vs 51.7 mean years), proportion of men (44.9% vs 49.3%), and mean INTERHEART risk score (9.7 vs 10.1). Larger differences were observed in postsecondary education (86.8% vs 70.2%), Chinese ethnicity (11.0% vs 3.3%), obesity (23.2% vs 29.3%), current smoker status (6.1% vs 18.4%), and having no cardiac testing (30.4% vs 55.9%). CONCLUSIONS: CAHHM-HSR participants are older, of higher socioeconomic status, and have a similar mean INTERHEART risk score, compared with participants in the Canadian Community Health Survey. Differing sampling strategies and missing data may explain some differences between the CAHHM-HSR cohort and Canadian community-dwelling adults and should be considered when using the CAHHM-HSR for scientific research.

CONTEXTE: L'étude Alliance canadienne cœurs et cerveaux sains (CAHHM) vise à mieux comprendre les facteurs déterminants précoces et la progression de l'atteinte cardiovasculaire subclinique chez des adultes sélectionnés au sein de cohortes existantes ­ soit celles de l'étude menée par le Partenariat canadien pour la santé de demain, de l'étude PURE (Prospective Urban and Rural Evaluation) et de la biobanque de l'Institut de cardiologie de Montréal. Nous avons évalué la mesure dans laquelle la sous-cohorte du volet de recherche sur l'utilisation des services de santé de la CAHHM (CAHHM-HSR) représente la population canadienne. MÉTHODOLOGIE: Nous avons adopté une approche transversale pour étudier une cohorte prospective d'adultes vivant dans la communauté âgés de 35 à 69 ans et répondant aux critères d'inclusion de l'étude CAHHM, ainsi qu'une cohorte d'adultes âgés de 35 à 69 ans ayant participé au volet de réponse rapide de l'Enquête sur la santé dans les collectivités canadiennes (ESCC) de 2015. Le score de risque INTERHEART individuel des participants a été calculé à partir des moyennes et des proportions rapportées à l'échelle globale et à l'échelle provinciale. RÉSULTATS: Les différences entre les participants du volet CAHHM-HSR et ceux du volet de réponse rapide de l'ESCC de 2015 étaient minimes quant à l'âge (56,3 ans vs 51,7 ans en moyenne), à la proportion d'hommes (44,9 % vs 49,3 %) et au score de risque INTERHEART moyen (9,7 vs 10,1). On a toutefois noté des différences plus importantes en ce qui concerne les caractéristiques suivantes : éducation postsecondaire (86,8 % vs 70,2 %), origine ethnique chinoise (11,0 % vs 3,3 %), obésité (23,2 % vs 29,3 %), tabagisme actuel (6,1 % vs 18,4 %) et absence d'antécédents d'examen cardiaque (30,4 % vs 55,9 %). CONCLUSIONS: Les participants du volet CAHHM-HSR sont plus âgés et ont un statut socioéconomique plus élevé que ceux du volet de réponse rapide de l'ESCC, mais ont un score de risque INTERHEART moyen comparable. Les différences quant aux stratégies d'échantillonnage et des données manquantes pourraient expliquer certains des écarts observés entre la cohorte CAHHM-HSR et celle des adultes canadiens vivant dans la communauté; il conviendrait d'en tenir compte lorsqu'on utilise les données du volet CAHHM-HSR à des fins de recherche scientifique.

Quantifying the Predictive Accuracy of a Polygenic Risk Score for Predicting Incident Cancer Cases : Application to the CARTaGENE Cohort.

With the increasing use of polygenic risk scores (PRS) there is a need for adapted methods to evaluate the predictivity of these tools. In this work, we propose a new pseudo-R 2 criterion to evaluate PRS predictive accuracy for time-to-event data. This new criterion is related to the score statistic derived under a two-component mixture model. It evaluates the effect of the PRS on both the propensity to experience the event and on the dynamic of the event among the susceptible subjects. Simulation results show that our index has good properties. We compared our index to other implemented pseudo-R 2 for survival data. Along with our index, two other indices have comparable good behavior when the PRS has a non-null propensity effect, and our index is the only one to detect when the PRS has only a dynamic effect. We evaluated the 5-year predictivity of an 18-single-nucleotide-polymorphism PRS for incident breast cancer cases on the CARTaGENE cohort using several pseudo-R 2 indices. We report that our index, which summarizes both a propensity and a dynamic effect, had the highest predictive accuracy. In conclusion, our proposed pseudo-R 2 is easy to implement and well suited to evaluate PRS for predicting incident events in cohort studies.

A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort.

Predicting immunogenicity for biotherapies using patient and drug-related factors represents nowadays a challenging issue. With the growing ability to collect massive amount of data, machine learning algorithms can provide efficient predictive tools. From the bio-clinical data collected in the multi-cohort of autoimmune diseases treated with biotherapies from the ABIRISK consortium, we evaluated the predictive power of a custom-built random survival forest for predicting the occurrence of anti-drug antibodies. This procedure takes into account the existence of a population composed of immune-reactive and immune-tolerant subjects as well as the existence of a tiny expected proportion of relevant predictive variables. The practical application to the ABIRISK cohort shows that this approach provides a good predictive accuracy that outperforms the classical survival random forest procedure. Moreover, the individual predicted probabilities allow to separate high and low risk group of patients. To our best knowledge, this is the first study to evaluate the use of machine learning procedures to predict biotherapy immunogenicity based on bioclinical information. It seems that such approach may have potential to provide useful information for the clinical practice of stratifying patients before receiving a biotherapy.

Assessing the effect of genetic markers on drug immunogenicity from a mechanistic model-based approach.

BACKGROUND: With the growth in use of biotherapic drugs in various medical fields, the occurrence of anti-drug antibodies represents nowadays a serious issue. This immune response against a drug can be due either to pre-existing antibodies or to the novel production of antibodies from B-cell clones by a fraction of the exposed subjects. Identifying genetic markers associated with the immunogenicity of biotherapeutic drugs may provide new opportunities for risk stratification before the introduction of the drug. However, real-world investigations should take into account that the population under study is a mixture of pre-immune, immune-reactive and immune-tolerant subjects. METHOD: In this work, we propose a novel test for assessing the effect of genetic markers on drug immunogenicity taking into account that the population under study is a mixed one. This test statistic is derived from a novel two-part semiparametric improper survival model which relies on immunological mechanistic considerations. RESULTS: Simulation results show the good behavior of the proposed statistic as compared to a two-part logrank test. In a study on drug immunogenicity, our results highlighted findings that would have been discarded when considering classical tests. CONCLUSION: We propose a novel test that can be used for analyzing drug immunogenicity and is easy to implement with standard softwares. This test is also applicable for situations where one wants to test the equality of improper survival distributions of semi-continuous outcomes between two or more independent groups.

Risk stratification integrating genetic data for factor VIII inhibitor development in patients with severe hemophilia A.

Replacement therapy in severe hemophilia A leads to factor VIII (FVIII) inhibitors in 30% of patients. Factor VIII gene (F8) mutation type, a family history of inhibitors, ethnicity and intensity of treatment are established risk factors, and were included in two published prediction tools based on regression models. Recently investigated immune regulatory genes could also play a part in immunogenicity. Our objective is to identify bio-clinical and genetic markers for FVIII inhibitor development, taking into account potential genetic high order interactions. The study population consisted of 593 and 79 patients with hemophilia A from centers in Bonn and Frankfurt respectively. Data was collected in the European ABIRISK tranSMART database. A subset of 125 severely affected patients from Bonn with reliable information on first treatment was selected as eligible for risk stratification using a hybrid tree-based regression model (GPLTR). In the eligible subset, 58 (46%) patients developed FVIII inhibitors. Among them, 49 (84%) were "high risk" F8 mutation type. 19 (33%) had a family history of inhibitors. The GPLTR model, taking into account F8 mutation risk, family history of inhibitors and product type, distinguishes two groups of patients: a high-risk group for immunogenicity, including patients with positive HLA-DRB1*15 and genotype G/A and A/A for IL-10 rs1800896, and a low-risk group of patients with negative HLA-DRB1*15 / HLA-DQB1*02 and T/T or G/T for CD86 rs2681401. We show associations between genetic factors and the occurrence of FVIII inhibitor development in severe hemophilia A patients taking into account for high-order interactions using a generalized partially linear tree-based approach.

Incidence and risk factors for adalimumab and infliximab anti-drug antibodies in rheumatoid arthritis: A European retrospective multicohort analysis.

OBJECTIVES: To evaluate the incidence of anti-drug antibody (ADA) occurrences and ADA-related risk factors under adalimumab and infliximab treatment in rheumatoid arthritis (RA) patients. METHODS: The study combined retrospective cohorts from the ABIRISK project totaling 366 RA patients treated with adalimumab (n = 240) or infliximab (n = 126), 92.4% of them anti-TNF naive (n = 328/355) and 96.6% of them co-treated with methotrexate (n = 341/353) with up to 18 months follow-up. ADA positivity was measured by enzyme-linked immunosorbent assay. The cumulative incidence of ADA was estimated, and potential bio-clinical factors were investigated using a Cox regression model on interval-censored data. RESULTS: ADAs were detected within 18 months in 19.2% (n = 46) of the adalimumab-treated patients and 29.4% (n = 37) of the infliximab-treated patients. The cumulative incidence of ADA increased over time. In the adalimumab and infliximab groups, respectively, the incidence was 15.4% (5.2-20.2) and 0% (0-5.9) at 3 months, 17.6% (11.4-26.4) and 0% (0-25.9) at 6 months, 17.7% (12.6-37.5) and 34.1% (11.4-46.3) at 12 months, 50.0% (25.9-87.5) and 37.5% (25.9-77.4) at 15 months and 50.0% (25.9-87.5) and 66.7% (37.7-100) at 18 months. Factors associated with a higher risk of ADA development were: longer disease duration (1-3 vs. < 1 year; adalimumab: HR 3.0, 95% CI 1.0-8.7; infliximab: HR 2.7, 95% CI 1.1-6.8), moderate disease activity (DAS28 3.2-5.1 vs. < 3.2; adalimumab: HR 6.6, 95% CI 1.3-33.7) and lifetime smoking (infliximab: HR 2.7, 95% CI 1.2-6.3). CONCLUSIONS: The current study focusing on patients co-treated with methotrexate for more than 95% of them found a late occurrence of ADAs not previously observed, whereby the risk continued to increase over 18 months. Disease duration, DAS28 and lifetime smoking are clinical predictors of ADA development.