Pairwise fitting of piecewise mixed models for the joint modeling of multivariate longitudinal outcomes, in a randomized crossover trial.

Many statistical models have been proposed in the literature for the analysis of longitudinal data. One may propose to model two or more correlated longitudinal processes simultaneously, with a goal of understanding their association over time. Joint modeling is then required to carefully study the association structure among the outcomes as well as drawing joint inferences about the different outcomes. In this study, we sought to model the associations among six nutrition outcomes while circumventing the computational challenge posed by their clustered and high-dimensional nature. We analyzed data from a 2 × $\times$ 2 randomized crossover trial conducted in Kenya, to compare the effect of high-dose and low-dose iodine in household salt on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in women of reproductive age and their household matching pair of school-aged children. Two additional outcomes, namely, urinary iodine concentration (UIC) in women and children were measured repeatedly to monitor the amount of iodine excreted through urine. We extended the model proposed by Mwangi et al. (2021, Communications in Statistics: Case Studies, Data Analysis and Applications, 7(3), 413-431) allowing flexible piecewise joint models for six outcomes to depend on separate random effects, which are themselves correlated. This entailed fitting 15 bivariate general linear mixed models and deriving inference for the joint model using pseudo-likelihood theory. We analyzed the outcomes separately and jointly using piecewise linear mixed-effects (PLME) model and further validated the results using current state-of-the-art Jones and Kenward methodology (JKME model) used for analyzing randomized crossover trials. The results indicate that high-dose iodine in salt significantly reduced blood pressure (BP) compared to low-dose iodine in salt. Estimates for the random effects and residual error components showed that SBP and DBP had strong positive correlation, with effect of the random slope indicating that significantly related outcomes are strongly associated in their evolution. There was a moderately strong inverse relationship between evolutions of UIC and BP both in women and children. These findings confirmed the original hypothesis that high-dose iodine salt has significant lowering effect on BP. We further sought to evaluate the performance of our proposed PLME model against the widely used JKME model, within the multivariate joint modeling framework through a simulation study mimicking a 2 × 2 $2\times 2$ crossover design. From our findings, the multivariate joint PLME model performed exceptionally well both in estimation of random-effects matrix (G) and Hessian matrix (H), allowing satisfactory model convergence during estimation. It allowed a more complex fit to the data with both random intercepts and slopes effects compared to the multivariate joint JKME model that allowed for random intercepts only. When a hierarchical viewpoint is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive definite. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters. The key highlight in this evaluation shows that multivariate joint JKME model is a powerful tool especially while fitting mixed models with random intercepts only, in crossover design settings. Addition of random slopes may lead to model complexities in most cases, resulting in unsatisfactory model convergence during estimation. To circumvent convergence pitfalls, extention of JKME model to PLME model allows a more flexible fit to the data (generated from crossover design settings), especially in the multivariate joint modeling framework.

Gout and incidence of 12 cardiovascular diseases: a case-control study including 152 663 individuals with gout and 709 981 matched controls.

BACKGROUND: Gout, a common crystal arthropathy, is associated with increased risk of cardiovascular disease. We aimed to identify how this risk varies by individual cardiovascular disease across a broad spectrum of conditions. METHODS: In this matched case-control study, we used linked primary and secondary electronic health records from the UK Clinical Practice Research Datalink to assemble a cohort of individuals with a first-time diagnosis of gout between Jan 1, 2000 and Dec 31, 2017, who were aged 80 years or younger at diagnosis, and free of cardiovascular diseases up to 12 months after diagnosis. The control cohort comprised up to five control individuals per patient with gout, matched on age, sex, socioeconomic status, geographical region, and calendar time, randomly selected among individuals free of gout at any time before and during the study period. The cohorts were followed up until June 30, 2019. We investigated the incidence of 12 cardiovascular diseases and used Cox proportional hazards models to examine differences in people with and without gout, overall and by subgroups of sex, age, socioeconomic status, and year of study inclusion. We further adjusted models for known cardiovascular risk factors (blood pressure, BMI, smoking status, cholesterol, type 2 diabetes, chronic kidney disease, and history of hypertension). FINDINGS: We identified 152 663 individuals with gout (mean age 56·2 years [SD 13·3]; 120 324 [78·8%] men and 32 339 [21·2%] women) and 709 981 matched controls (mean age 56·5 years [13·2]; 561 002 [79·0%] men and 148 979 [21·0%] women). Of these individuals, 31 479 (20·6%) with gout and 106 520 (15·0%) without gout developed cardiovascular disease during a median follow-up of 6·5 years (IQR 3·1-10·5). Patients with gout had higher risk of cardiovascular diseases than matched controls (hazard ratio [HR] 1·58 [95% CI 1·52-1·63]). Excess risk of cardiovascular disease in gout was greater in women than men (women: HR 1·88 [1·75-2·02]; men: HR 1·49 [1·43-1·56]), and, among all age groups, was highest in younger individuals (HR in people aged <45 years: 2·22 [1·92-2·57]). Excess risk was observed across all 12 cardiovascular diseases investigated. Patients with gout had higher BMI than matched controls (mean difference 2·90 kg/m2 [95% CI 2·87-2·93]) and higher prevalence of chronic kidney disease, dyslipidaemia, history of hypertension, obesity, and type 2 diabetes. Adjusting for known cardiovascular risk factors attenuated but did not eliminate the excess risk of cardiovascular disease related to gout (adjusted HR 1·31 [1·27-1·36]). INTERPRETATION: Patients with gout had an excess risk of developing a broad range of cardiovascular diseases that extend beyond atherosclerotic diseases and include heart failure, arrhythmias, valve disease, and thromboembolic diseases. Excess risk was highest in women and younger individuals. These findings suggest that strategies to reduce cardiovascular risk in patients with gout need to evolve and be implemented in clinical practice. FUNDING: Research Foundation Flanders.

An information-theoretic approach for the assessment of a continuous outcome as a surrogate for a binary true endpoint based on causal inference: Application to vaccine evaluation.

Within the causal association paradigm, a method is proposed to assess the validity of a continuous outcome as a surrogate for a binary true endpoint. The methodology is based on a previously introduced information-theoretic definition of surrogacy and has two main steps. In the first step, a new model is proposed to describe the joint distribution of the potential outcomes associated with the putative surrogate and the true endpoint of interest. The identifiability issues inherent to this type of models are handled via sensitivity analysis. In the second step, a metric of surrogacy new to this setting, the so-called individual causal association is presented. The methodology is studied in detail using theoretical considerations, some simulations, and data from a randomized clinical trial evaluating an inactivated quadrivalent influenza vaccine. A user-friendly R package Surrogate is provided to carry out the evaluation exercise.

Evaluation of a flexible piecewise linear mixed-effects model in the analysis of randomized cross-over trials.

Cross-over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment. They have received a lot of attention, particularly in connection with regulatory requirements for new drugs. The main advantage of using cross-over designs over conventional parallel designs is increased precision, thanks to within-subject comparisons. In the statistical literature, more recent developments are discussed in the analysis of cross-over trials, in particular regarding repeated measures. A piecewise linear model within the framework of mixed effects has been proposed in the analysis of cross-over trials. In this article, we report on a simulation study comparing performance of a piecewise linear mixed-effects (PLME) model against two commonly cited models-Grizzle's mixed-effects (GME) and Jones & Kenward's mixed-effects (JKME) models-used in the analysis of cross-over trials. Our simulation study tried to mirror real-life situation by deriving true underlying parameters from empirical data. The findings from real-life data confirmed the original hypothesis that high-dose iodine salt have significantly lowering effect on diastolic blood pressure (DBP). We further sought to evaluate the performance of PLME model against GME and JKME models, within univariate modeling framework through a simulation study mimicking a 2 × 2 cross-over design. The fixed-effects, random-effects and residual error parameters used in the simulation process were estimated from DBP data, using a PLME model. The initial results with full specification of random intercept and slope(s), showed that the univariate PLME model performed better than the GME and JKME models in estimation of variance-covariance matrix (G) governing the random effects, allowing satisfactory model convergence during estimation. When a hierarchical view-point is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive-definite. The PLME model is preferred especially in modeling an increased number of random effects, compared to the GME and JKME models that work equally well with random intercepts only. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters.

Reflections on the concept of optimality of single decision point treatment regimes.

In many areas, applied researchers as well as practitioners have to choose between different solutions for a problem at hand; this calls for optimal decision rules to settle the choices involved. As a key example, one may think of the search for optimal treatment regimes (OTRs) in clinical research, that specify which treatment alternative should be administered to each patient under study. Motivated by the fact that the concept of optimality of decision rules in general and treatment regimes in particular has received so far relatively little attention and discussion, we will present a number of reflections on it, starting from the basics of any optimization problem. Specifically, we will analyze the search space and the to be optimized criterion function underlying the search of single decision point OTRs, along with the many choice aspects that show up in their specification. Special attention is paid to formal characteristics and properties as well as to substantive concerns and hypotheses that may guide these choices. We illustrate with a few empirical examples taken from the literature. Finally, we discuss how the presented reflections may help sharpen statistical thinking about optimality of decision rules for treatment assignment and to facilitate the dialogue between the statistical consultant and the applied researcher in search of an OTR.

A longitudinal transition imputation model for categorical data applied to a large registry dataset.

Imputation of longitudinal categorical covariates with several waves and many predictors is cumbersome in terms of implausible transitions, colinearity, and overfitting. We designed a simulation study with data obtained from a general practitioners' morbidity registry in Belgium for three waves, with smoking as the longitudinal covariate of interest. We set varying proportions of data on smoking to missing completely at random and missing not at random with proportions of missingness equal to 10%, 30%, 50%, and 70%. This study proposed a 3-stage approach that allows flexibility when imputing time-dependent categorical covariates. First, multiple imputation using fully conditional specification or multiple imputation for the predictor variables was deployed using the wide format such that previous and future information of the same patient was utilized. Second, a joint Markov transition model for initial, forward, backward, and intermittent probabilities was developed for each imputed dataset. Finally, this transition model was used for imputation. We compared the performance of this methodology with an analyses of the complete data and with listwise deletion in terms of bias and root mean square error. Next, we applied this methodology in a clinical case for years 2017 to 2021, where we estimated the effect of several covariates on the pneumococcal vaccination. This methodological framework ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. Finally, a companion R package was developed to enable the replication and easy application of this methodology.

An observational cohort study of histological screening for BK polyomavirus nephropathy following viral replication in plasma.

Systematic screening for BKPyV-DNAemia has been advocated to aid prevention and treatment of polyomavirus associated nephropathy (PyVAN), an important cause of kidney graft failure. The added value of performing a biopsy at time of BKPyV-DNAemia, to distinguish presumptive PyVAN (negative SV40 immunohistochemistry) and proven PyVAN (positive SV40) has not been established. Therefore, we studied an unselected cohort of 950 transplantations, performed between 2008-2017. BKPyV-DNAemia was detected in 250 (26.3%) transplant recipients, and positive SV40 in 91 cases (9.6%). Among 209 patients with a concurrent biopsy at time of first BKPyV-DNAemia, 60 (28.7%) biopsies were SV40 positive. Plasma viral load showed high diagnostic value for concurrent SV40 positivity (ROC-AUC 0.950, 95% confidence interval 0.916-0.978) and the semiquantitatively scored percentage of tubules with evidence of polyomavirus replication (pvl score) (0.979, 0.968-0.988). SV40 positivity was highly unlikely when plasma viral load is below 4 log10 copies/ml (negative predictive value 0.989, 0.979-0.994). In SV40 positive patients, higher plasma BKPyV-DNA load and higher pvl scores were associated with slower viral clearance from the blood (hazard ratio 0.712, 95% confidence interval 0.604-0.839, and 0.327, 0.161-0.668, respectively), whereas the dichotomy positivity/negativity of SV40 immunohistochemistry did not predict viral clearance. Although the pvl score offers some prognostic value for viral clearance on top of plasma viral load, the latter provided good guidance for when a biopsy was unnecessary to exclude PyVAN. Thus, the distinction between presumptive and proven PyVAN, based on SV40 immunohistochemistry, has limited clinical value. Hence, management of BKPyV-DNAemia and immunosuppression reduction should be weighed against the risk of occurrence of rejection, or exacerbation of rejection observed concomitantly.

A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba.

To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.

Incidence, prevalence, and co-occurrence of autoimmune disorders over time and by age, sex, and socioeconomic status: a population-based cohort study of 22 million individuals in the UK.

BACKGROUND: A rise in the incidence of some autoimmune disorders has been described. However, contemporary estimates of the overall incidence of autoimmune diseases and trends over time are scarce and inconsistent. We aimed to investigate the incidence and prevalence of 19 of the most common autoimmune diseases in the UK, assess trends over time, and by sex, age, socioeconomic status, season, and region, and we examine rates of co-occurrence among autoimmune diseases. METHODS: In this UK population-based study, we used linked primary and secondary electronic health records from the Clinical Practice Research Datalink (CPRD), a cohort that is representative of the UK population in terms of age and sex and ethnicity. Eligible participants were men and women (no age restriction) with acceptable records, approved for Hospital Episodes Statistics and Office of National Statistics linkage, and registered with their general practice for at least 12 months during the study period. We calculated age and sex standardised incidence and prevalence of 19 autoimmune disorders from 2000 to 2019 and used negative binomial regression models to investigate temporal trends and variation by age, sex, socioeconomic status, season of onset, and geographical region in England. To characterise co-occurrence of autoimmune diseases, we calculated incidence rate ratios (IRRs), comparing incidence rates of comorbid autoimmune disease among individuals with a first (index) autoimmune disease with incidence rates in the general population, using negative binomial regression models, adjusted for age and sex. FINDINGS: Among the 22 009 375 individuals included in the study, 978 872 had a new diagnosis of at least one autoimmune disease between Jan 1, 2000, and June 30, 2019 (mean age 54·0 years [SD 21·4]). 625 879 (63·9%) of these diagnosed individuals were female and 352 993 (36·1%) were male. Over the study period, age and sex standardised incidence rates of any autoimmune diseases increased (IRR 2017-19 vs 2000-02 1·04 [95% CI 1·00-1·09]). The largest increases were seen in coeliac disease (2·19 [2·05-2·35]), Sjogren's syndrome (2·09 [1·84-2·37]), and Graves' disease (2·07 [1·92-2·22]); pernicious anaemia (0·79 [0·72-0·86]) and Hashimoto's thyroiditis (0·81 [0·75-0·86]) significantly decreased in incidence. Together, the 19 autoimmune disorders examined affected 10·2% of the population over the study period (1 912 200 [13·1%] women and 668 264 [7·4%] men). A socioeconomic gradient was evident across several diseases, including pernicious anaemia (most vs least deprived area IRR 1·72 [1·64-1·81]), rheumatoid arthritis (1·52 [1·45-1·59]), Graves' disease (1·36 [1·30-1·43]), and systemic lupus erythematosus (1·35 [1·25-1·46]). Seasonal variations were observed for childhood-onset type 1 diabetes (more commonly diagnosed in winter) and vitiligo (more commonly diagnosed in summer), and regional variations were observed for a range of conditions. Autoimmune disorders were commonly associated with each other, particularly Sjögren's syndrome, systemic lupus erythematosus, and systemic sclerosis. Individuals with childhood-onset type 1 diabetes also had significantly higher rates of Addison's disease (IRR 26·5 [95% CI 17·3-40·7]), coeliac disease (28·4 [25·2-32·0]), and thyroid disease (Hashimoto's thyroiditis 13·3 [11·8-14·9] and Graves' disease 6·7 [5·1-8·5]), and multiple sclerosis had a particularly low rate of co-occurrence with other autoimmune diseases. INTERPRETATION: Autoimmune diseases affect approximately one in ten individuals, and their burden continues to increase over time at varying rates across individual diseases. The socioeconomic, seasonal, and regional disparities observed among several autoimmune disorders in our study suggest environmental factors in disease pathogenesis. The inter-relations between autoimmune diseases are commensurate with shared pathogenetic mechanisms or predisposing factors, particularly among connective tissue diseases and among endocrine diseases. FUNDING: Research Foundation Flanders.

A longitudinal perspective on perceived vulnerability to disease during the COVID-19 pandemic in Belgium.

During the coronavirus disease 2019 pandemic, individuals relied heavily on media sources to stay informed about the disease and public health measures. However, differences exist in the type and frequency of news media consumption, which can be linked to their perceived vulnerability to disease. In this longitudinal study, 1000 Flemish (Belgium) individuals were followed from March 2020 until September 2020, focussing on the evolution in perceived vulnerability to disease (i.e. perceived infectability and germ aversion). Media consumption significantly impacts perceived germ aversion; heavy consumers of commercial media reported greater germ aversion than light consumers of these media. The evolution of germ aversion among individuals from March to August depends on their gender, living environment, age and possibility to work from home. Furthermore, the evolution of perceived infectability depends on the age and living environment of the respondent. These findings may interest policy makers and media professionals to anticipate how anxieties regarding contracting an infectious disease evolve over time and how individual characteristics affect this evolution.

Do optimism and moralization predict vaccination? A five-wave longitudinal study.

OBJECTIVE: To examine if personal and comparative optimism, perceived effectiveness, and moralization of vaccination predict people's decision to get vaccinated. METHODS: We measured self-reported vaccination decisions in a five-wave longitudinal study (N ≍ 5,000/wave) in Belgium over a six months period (December 2020-May 2021) during the COVID-19 pandemic. Among the predictors were demographic factors, personal and comparative optimism for three aspects of COVID-19 (infection, severe disease, good outcome), perceived effectiveness of vaccination, and the extent to which vaccination is being viewed in prosocial terms (altruism, civic spirit) versus as instrumental in one's self-interest (common sense, concern about one's health). RESULTS: The actual availability of vaccines changed people's outlook on vaccination. Marked differences emerged in vaccination decision between linguistic-cultural regions (Flemish Region, Walloon Region, Brussels Capital Region). Personal and comparative optimism predicted vaccination decisions to different extents depending on participants' age and on whether the optimism was for infection, severe disease, or a good outcome. In older participants, vaccination decision was mostly predicted by personal optimism; in younger participants, it was mostly predicted by comparative optimism. Moralizing vaccination predicted a lower likelihood of a positive vaccination decision, that is, higher vaccine hesitancy or refusal, particularly in older participants. CONCLUSIONS: Assessments of risk perception serving to inform vaccination campaigns should differentiate between expectations concerning the risk of infection and expectations concerning the outcome of an infection. Public health messages should address comparative optimism, particularly when targeting younger populations. Contrary to popular belief, moralizing vaccination may reduce the willingness to get vaccinated. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A linear mixed model to estimate COVID-19-induced excess mortality.

The Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID-19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the year-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.

Self-uniqueness beliefs and adherence to recommended precautions. A 5-wave longitudinal COVID-19 study.

RATIONALE: Research on health-related self-uniqueness beliefs suggested that these beliefs might predict adherence to precautions against COVID-19. OBJECTIVE: We examined if comparative optimism (believing that one is less at less than others), self-superiority (believing that one already adheres better to precautions than others), and egocentric impact perception (believing that adverse events affect oneself more than others) predicted intended adherence to precautions. METHOD: We measured self-reported intentions, optimism for self and others, perceived past adherence by self and others, and perceived impact of the measures and the disease on self and others in a 5-wave longitudinal study in December 2020-May 2021 (N ≈ 5000/wave). The sample was in key respects representative for the Belgian population. We used joint models to examine the relationship between self-uniqueness beliefs and intended adherence to the precautions. RESULTS: Believing that COVID-19 would affect one's own life more than average (egocentric impact perception) was associated with higher intentions to adhere to precautions, as was believing that the precautions affected one's life less than average (allocentric impact perception). Self-superiority concerning past adherence to precautions and comparative optimism concerning infection with COVID-19 were associated with higher intended adherence, regardless of whether their non-comparative counterparts (descriptive norm, i.e., perceived adherence to precautions by others, and personal optimism, respectively) were controlled for. Comparative optimism for severe disease and for good outcome were associated with lower intended adherence if personal optimism was not controlled for, but with higher intended adherence if it was controlled for. CONCLUSION: Self-uniqueness beliefs predict intended adherence to precautions against COVID-19, but do so in different directions.

Autoimmune diseases and cardiovascular risk: a population-based study on 19 autoimmune diseases and 12 cardiovascular diseases in 22 million individuals in the UK.

BACKGROUND: Some autoimmune diseases are associated with an increased risk of cardiovascular disease. We aimed to determine whether or not this is true, and to what extent, for a broad range of autoimmune conditions. METHODS: In this population-based study, we used linked primary and secondary care records from the Clinical Practice Research Datalink (CPRD), GOLD and Aurum datasets, to assemble a cohort of individuals across the UK who were newly diagnosed with any of 19 autoimmune diseases between Jan 1, 2000, and Dec 31, 2017, younger than 80 years at diagnosis, and free of cardiovascular diseases up to 12 months after diagnosis. We also assembled a matched cohort with up to five individuals matched on age, sex, socioeconomic status, region, and calendar year, who were free of autoimmune disease and free of cardiovascular diseases up to 12 months after study entry. Both cohorts were followed up until June 30, 2019. We investigated the incidence of 12 cardiovascular outcomes and used Cox proportional hazards models to examine differences in patients with and without autoimmune diseases. FINDINGS: Of 22 009 375 individuals identified from the CPRD databases, we identified 446 449 eligible individuals with autoimmune diseases and 2 102 830 matched controls. In the autoimmune cohort, mean age at diagnosis was 46·2 years (SD 19·8), and 271 410 (60·8%) were women and 175 039 (39·2%) were men. 68 413 (15·3%) people with and 231 410 (11·0%) without autoimmune diseases developed incident cardiovascular disease during a median of 6·2 years (IQR 2·7-10·8) of follow-up. The incidence rate of cardiovascular disease was 23·3 events per 1000 patient-years among patients with autoimmune disease and 15·0 events per 1000 patient-years among those without an autoimmune disease (hazard ratio [HR] 1·56 [95% CI 1·52-1·59]). An increased risk of cardiovascular disease with autoimmune disease was seen for every individual cardiovascular disease and increased progressively with the number of autoimmune diseases present (one disease: HR 1·41 [95% CI 1·37-1·45]; two diseases: 2·63 [2·49-2·78]); three or more diseases: 3·79 [3·36-4·27]), and in younger age groups (age <45 years: 2·33 [2·16-2·51]; 55-64 years: 1·76 [1·67-1·85]; ≥75 years: 1·30 [1·24-1·36]). Among autoimmune diseases, systemic sclerosis (3·59 [2·81-4·59]), Addison's disease (2·83 [1·96-4·09]), systemic lupus erythematosus (2·82 [2·38-3·33]), and type 1 diabetes (2·36 [2·21-2·52]) had the highest overall cardiovascular risk. INTERPRETATION: These findings warrant targeted cardiovascular prevention measures, in particular in younger patients with autoimmune diseases, and further research into pathophysiological mechanisms underlying these complications. FUNDING: Horizon 2020 Marie Sklodowska-Curie Actions and European Society of Cardiology.

High Security Settings in Flanders: An Analysis of Discharged and Long-Term Forensic Psychiatric Patients.

Background: Two Forensic Psychiatric Centres (FPC) were implemented the last decade in Flanders in Ghent (2014) and Antwerp (2017). FPCs are forensic institutions for forensic psychiatric patients with a high recidivism risk and a high security need. The objective of FPCs is to create a care process with sufficient flow (from high to lower forms of security), and transitions (from specialized forensic care to regular psychiatric care). Aims: To examine the characteristics of the high security population in FPCs, treatment length, number of discharges, and discharge locations and to determine the profile of long-term patients within an FPC. Methods: A retrospective file study of an admission cohort of 654 patients admitted to FPC Ghent or FPC Antwerp was conducted. Sociodemographic, clinical, judicial and risk characteristics were analyzed. Bivariate analyses were used to test the difference between two groups: the group that was discharged to a lower security level vs. the group of long-term patients. Results: Most patients had psychosis and personality disorders, while comorbidity was also high. Judicial histories were extensive, with many sexual index offenses. During a 6-year follow-up period, the number of referrals back to prison was low. Nearly a third of the population was discharged to a setting with a lower security level. Long-term patients typically presented with more personality disorders, higher psychopathy traits and higher risk scores and were more frequently subjected to coercive measures during treatment. Conclusions: The Flemish FPC population is characterized by a high proportion of sex offenders as well as a high proportion of personality-disordered patients. It is this last group, and the group with elevated psychopathy traits, who remain for longer than expected and is difficult to resocialize. This study further highlights the need for clear criteria to assess the conditions of these long-term patients in Flanders.

Role for Granulocyte Colony-Stimulating Factor in Neutrophilic Extramedullary Myelopoiesis in a Murine Model of Systemic Juvenile Idiopathic Arthritis.

OBJECTIVE: Systemic juvenile idiopathic arthritis (JIA) is a systemic inflammatory disease with childhood onset. Systemic JIA is associated with neutrophilia, including immature granulocytes, potentially driven by the growth factor granulocyte-colony stimulating factor (G-CSF). This study was undertaken to investigate the role of G-CSF in the pathology of systemic JIA. METHODS: Injection of Freund's complete adjuvant (CFA) in BALB/c mice induces mild inflammation and neutrophilia in wild-type (WT) mice and a more pronounced disease, reminiscent to that of JIA patients, in interferon-γ-knockout (IFNγ-KO) mice. Extramedullary myelopoiesis was studied in CFA-immunized mice by single-cell RNA sequencing, and the effect of G-CSF receptor (G-CSFR) blockage on neutrophil development and systemic JIA pathology was evaluated. Additionally, plasma G-CSF levels were measured in patients. RESULTS: Both in systemic JIA patients and in a corresponding mouse model, plasma G-CSF levels were increased. In the mouse model, we demonstrated that G-CSF is responsible for the observed neutrophilia and extramedullary myelopoiesis and the induction of immature neutrophils and myeloid-derived suppressor-like cells. Administration of a G-CSFR antagonizing antibody blocked the maturation and differentiation of neutrophils in CFA-immunized mice. In IFNγ-KO mice, treatment was associated with almost complete inhibition of arthritis due to reduced neutrophilia and osteoclast formation. Disease symptoms were ameliorated, but slight increases in interleukin-6 (IL-6), tumor necrosis factor, and IL-17 were detected upon G-CSFR inhibition in the IFNγ-KO mice, and were associated with mild increases in weight loss, tail damage, and immature red blood cells. CONCLUSION: We describe the role of G-CSF in a mouse model of systemic JIA and suggest an important role for G-CSF-induced myelopoiesis and neutrophilia in regulating the development of arthritis.

Implementing the meta-analytic approach for the evaluation of surrogate endpoints in SAS and R: a word of caution.

The meta-analytic approach has become the gold-standard methodology for the evaluation of surrogate endpoints and several implementations are currently available in SAS and R. The methodology is based on hierarchical models that are numerically demanding and, when the amount of data is limited, maximum likelihood algorithms may not converge or may converge to an ill-conditioned maximum such as a boundary solution. This may produce misleading conclusions and have negative implications for the evaluation of new drugs. In the present work, we explore the use of two distinct functions in R (lme and lmer) and the MIXED procedure in SAS to assess the validity of putative surrogate endpoints in the meta-analytic framework, via simulations and the analysis of a real case study. We describe some problems found with the lmer function in R that led to a poorer performance as compared with the lme function and MIXED procedure.

The evolution of histological changes suggestive of antibody-mediated injury, in the presence and absence of donor-specific anti-HLA antibodies.

The interplay between donor-specific anti-HLA antibodies (HLA-DSA), histology of active antibody-mediated rejection (aABMRh ), transplant glomerulopathy (cg), and graft failure in kidney transplantation remains insufficiently understood. We performed a single-center cohort study (n = 1000) including 2761 protocol and 833 indication biopsies. Patients with pretransplant HLA-DSA were more prone to develop aABMRh (OR 22.7, 95% CI, 11.8-43.7, P < 0.001), cg (OR 5.76, 95% CI, 1.67-19.8, P = 0.006), and aABMRh/cg (OR 19.5, 95% CI, 10.6-35.9, P < 0.001). The negative impact of pre-transplant HLA-DSA on graft survival (HR 2.12, 95% CI, 1.41-3.20, P < 0.001) was partially mediated through aABMRh and cg occurrence. When adjusted for time-dependent HLA-DSA (HR 4.03, 95% CI, 2.21-7.15, P = 0.002), graft failure was only affected by aABMRh when cg was evident. In HLA-DSA negative patients, aABMRh was associated with impaired graft outcome only when evolving to cg (HR 1.32, 95% CI, 1.07-1.61, P = 0.008). We conclude that the kinetics of HLA-DSA are important to estimate the rate of graft failure, and that histological follow-up is necessary to discover, often subclinical, ABMR and cg. In the absence of HLA-DSA, patients experience similar histological lesions and the evolution to transplant glomerulopathy associates with impaired graft outcome.