Polymyalgia rheumatica is a risk factor for more recalcitrant disease in giant cell arteritis: A retrospective cohort study.

OBJECTIVES: To evaluate differences in presentation and outcome of giant cell arteritis (GCA) patients with and without polymyalgia rheumatica (PMR) symptoms. METHODS: Consecutive patients diagnosed with GCA between 2000 and 2020 and followed for ≥12 months at the University Hospitals Leuven (Belgium), were included retrospectively. RESULTS: We included 398 GCA patients, of which 181 (45%) with PMR symptoms. Patients with PMR symptoms had a longer symptom duration (11 vs 6 weeks, p < 0.001). They less frequently reported fever (19% vs 28%, p = 0.030) and fatigue (52% vs 64%, p = 0.015) and tended to have less permanent vision loss (12% vs 19%, p = 0.052). There was no difference in the cumulative oral GC dose at 2 years (4.4 vs 4.3 g methylprednisolone, p = 0.571). However, those with PMR symptoms were treated with higher GC doses during subsequent follow-up (p < 0.05 from 38 months after diagnosis) and had a lower probability of stopping GC (62% vs 71%, HR 0.74 [95%CI 0.58-0.94], p = 0.018) with a longer median duration of GC treatment (29 vs 23 months, p = 0.021). In addition, presence of PMR symptoms was associated with an increased risk of relapse (64% vs 51%, HR 1.38 [95%CI 1.06-1.79], p = 0.017) with a higher number of relapses (1.47 [95%CI 1.30-1.65] vs 1.16 relapses [95%CI 1.02-1.31], p = 0.007). Patients with PMR symptoms less frequently developed thoracic aortic aneurysms during follow-up (3% vs 11%, p = 0.005). CONCLUSION: GCA patients with PMR symptoms had more recalcitrant disease with a higher risk of relapse and longer duration of GC treatment with need for higher GC doses.

Trends in cardiovascular disease incidence among 22 million people in the UK over 20 years: population based study.

OBJECTIVE: To investigate the incidence of cardiovascular disease (CVD) overall and by age, sex, and socioeconomic status, and its variation over time, in the UK during 2000-19. DESIGN: Population based study. SETTING: UK. PARTICIPANTS: 1 650 052 individuals registered with a general practice contributing to Clinical Practice Research Datalink and newly diagnosed with at least one CVD from 1 January 2000 to 30 June 2019. MAIN OUTCOME MEASURES: The primary outcome was incident diagnosis of CVD, comprising acute coronary syndrome, aortic aneurysm, aortic stenosis, atrial fibrillation or flutter, chronic ischaemic heart disease, heart failure, peripheral artery disease, second or third degree heart block, stroke (ischaemic, haemorrhagic, and unspecified), and venous thromboembolism (deep vein thrombosis or pulmonary embolism). Disease incidence rates were calculated individually and as a composite outcome of all 10 CVDs combined and were standardised for age and sex using the 2013 European standard population. Negative binomial regression models investigated temporal trends and variation by age, sex, and socioeconomic status. RESULTS: The mean age of the population was 70.5 years and 47.6% (n=784 904) were women. The age and sex standardised incidence of all 10 prespecified CVDs declined by 19% during 2000-19 (incidence rate ratio 2017-19 v 2000-02: 0.80, 95% confidence interval 0.73 to 0.88). The incidence of coronary heart disease and stroke decreased by about 30% (incidence rate ratios for acute coronary syndrome, chronic ischaemic heart disease, and stroke were 0.70 (0.69 to 0.70), 0.67 (0.66 to 0.67), and 0.75 (0.67 to 0.83), respectively). In parallel, an increasing number of diagnoses of cardiac arrhythmias, valve disease, and thromboembolic diseases were observed. As a result, the overall incidence of CVDs across the 10 conditions remained relatively stable from the mid-2000s. Age stratified analyses further showed that the observed decline in coronary heart disease incidence was largely restricted to age groups older than 60 years, with little or no improvement in younger age groups. Trends were generally similar between men and women. A socioeconomic gradient was observed for almost every CVD investigated. The gradient did not decrease over time and was most noticeable for peripheral artery disease (incidence rate ratio most deprived v least deprived: 1.98 (1.87 to 2.09)), acute coronary syndrome (1.55 (1.54 to 1.57)), and heart failure (1.50 (1.41 to 1.59)). CONCLUSIONS: Despite substantial improvements in the prevention of atherosclerotic diseases in the UK, the overall burden of CVDs remained high during 2000-19. For CVDs to decrease further, future prevention strategies might need to consider a broader spectrum of conditions, including arrhythmias, valve diseases, and thromboembolism, and examine the specific needs of younger age groups and socioeconomically deprived populations.

Correction: The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic.

[This corrects the article DOI: 10.1371/journal.pgph.0002601.].

Geospatial patterns of excess mortality in Belgium: Insights from the first year of the COVID-19 pandemic.

OBJECTIVES: Belgium experienced multiple COVID-19 waves that hit various groups in the population, which changed the mortality pattern compared to periods before the pandemic. In this study, we investigated the geographical excess mortality trend in Belgium during the first year of the COVID-19 pandemic. METHODS: We retrieved the number of deaths and population data in 2020 based on gender, age, and municipality of residence, and we made a comparison with the mortality data in 2017-2019 using a spatially discrete model. RESULTS: Excess mortality was significantly associated with age, gender, and COVID-19 incidence, with larger effects in the second half of 2020. Most municipalities had higher risks of mortality with a number of exceptions in the northeastern part of Belgium. Some discrepancies in excess mortality were observed between the north and south regions. CONCLUSIONS: This study offers useful insight into excess mortality and will aid local and regional authorities in monitoring mortality trends.

A joint normal-ordinal (probit) model for ordinal and continuous longitudinal data.

In biomedical studies, continuous and ordinal longitudinal variables are frequently encountered. In many of these studies it is of interest to estimate the effect of one of these longitudinal variables on the other. Time-dependent covariates have, however, several limitations; they can, for example, not be included when the data is not collected at fixed intervals. The issues can be circumvented by implementing joint models, where two or more longitudinal variables are treated as a response and modeled with a correlated random effect. Next, by conditioning on these response(s), we can study the effect of one or more longitudinal variables on another. We propose a normal-ordinal(probit) joint model. First, we derive closed-form formulas to estimate the model-based correlations between the responses on their original scale. In addition, we derive the marginal model, where the interpretation is no longer conditional on the random effects. As a consequence, we can make predictions for a subvector of one response conditional on the other response and potentially a subvector of the history of the response. Next, we extend the approach to a high-dimensional case with more than two ordinal and/or continuous longitudinal variables. The methodology is applied to a case study where, among others, a longitudinal ordinal response is predicted with a longitudinal continuous variable.

A joint penalized spline smoothing model for the number of positive and negative COVID-19 tests.

One of the key tools to understand and reduce the spread of the SARS-CoV-2 virus is testing. The total number of tests, the number of positive tests, the number of negative tests, and the positivity rate are interconnected indicators and vary with time. To better understand the relationship between these indicators, against the background of an evolving pandemic, the association between the number of positive tests and the number of negative tests is studied using a joint modeling approach. All countries in the European Union, Switzerland, the United Kingdom, and Norway are included in the analysis. We propose a joint penalized spline model in which the penalized spline is reparameterized as a linear mixed model. The model allows for flexible trajectories by smoothing the country-specific deviations from the overall penalized spline and accounts for heteroscedasticity by allowing the autocorrelation parameters and residual variances to vary among countries. The association between the number of positive tests and the number of negative tests is derived from the joint distribution for the random intercepts and slopes. The correlation between the random intercepts and the correlation between the random slopes were both positive. This suggests that, when countries increase their testing capacity, both the number of positive tests and negative tests will increase. A significant correlation was found between the random intercepts, but the correlation between the random slopes was not significant due to a wide credible interval.

Translating the COVID-19 epidemiological situation into policies and measures: the Belgian experience.

The COVID-19 pandemic led to sustained surveillance efforts, which made unprecedented volumes and types of data available. In Belgium, these data were used to conduct a targeted and regular assessment of the epidemiological situation. In addition, management tools were developed, incorporating key indicators and thresholds, to define risk levels and offer guidance to policy makers. Categorizing risk into various levels provided a stable framework to monitor the COVID-19 epidemiological situation and allowed for clear communication to authorities. Although translating risk levels into specific public health measures has remained challenging, this experience was foundational for future evaluation of the situation for respiratory infections in general, which, in Belgium, is now based on a management tool combining different data sources.

Identity Functioning in Patients with an Eating Disorder: Developmental Trajectories throughout Treatment.

Increasing research has indicated a strong association between identity functioning and eating disorder (ED) symptomatology. However, a detailed investigation of identity throughout ED treatment is lacking. The present longitudinal study examined identity in inpatients with an ED and explored its simultaneous change with ED symptomatology throughout treatment. A total of 225 female patients completed questionnaires at admission. From these 225 patients participating at admission (Wave 1), 110 also participated in at least one additional measurement wave, with 43.64% (n = 48) participating at admission and during treatment, 16.36% (n = 18) participating at admission and at discharge, and 40% (n = 44) participating at admission, during treatment and at discharge. Questionnaires on identity synthesis, identity confusion, identity processes, and ED symptomatology were completed. Latent growth curve modeling was used to address the research questions. Throughout treatment, a decrease in identity confusion and an increase in identity synthesis and adaptive identity processes were found. Accordingly, increases in identity synthesis and identification with commitment were related to general decreases in the drive for thinness and body dissatisfaction. Similarly, such decreases in ED symptoms were related to general decreases in identity confusion and ruminative exploration. The present study points to an increase in identity functioning throughout treatment, and longitudinal associations between identity functioning and ED symptomatology were found. Helping patients to decrease their ruminative exploration and to increase their identification with previously made life commitments and treating body/weight concerns could both be helpful in ED treatment.

Istore: a project on innovative statistical methodologies to improve rare diseases clinical trials in limited populations.

BACKGROUND: The conduct of rare disease clinical trials is still hampered by methodological problems. The number of patients suffering from a rare condition is variable, but may be very small and unfortunately statistical problems for small and finite populations have received less consideration. This paper describes the outline of the iSTORE project, its ambitions, and its methodological approaches. METHODS: In very small populations, methodological challenges exacerbate. iSTORE's ambition is to develop a comprehensive perspective on natural history course modelling through multiple endpoint methodologies, subgroup similarity identification, and improving level of evidence. RESULTS: The methodological approaches cover methods for sound scientific modeling of natural history course data, showing similarity between subgroups, defining, and analyzing multiple endpoints and quantifying the level of evidence in multiple endpoint trials that are often hampered by bias. CONCLUSION: Through its expected results, iSTORE will contribute to the rare diseases research field by providing an approach to better inform about and thus being able to plan a clinical trial. The methodological derivations can be synchronized and transferability will be outlined.

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.

Prevalence, characteristics, and outcome of subclinical vasculitis in polymyalgia rheumatica: a retrospective cohort study.

OBJECTIVES: Two recent meta-analyses reported subclinical vasculitis in 22-23% of patients with polymyalgia rheumatica (PMR). We aimed to evaluate the prevalence, characteristics, and outcome of subclinical vasculitis among our PMR patients. METHODS: Consecutive patients with GCA/PMR spectrum disease with isolated PMR symptoms who underwent FDG PET imaging between 2003-2020 and who were followed for ≥6 months, were included retrospectively. Vasculitis was defined as FDG uptake ≥ grade 2 in any vessel. RESULTS: We included 337 patients, of whom 31 (9%) with subclinical vasculitis. Among those with subclinical vasculitis, 21 (58%) had isolated large vessel vasculitis, 3 (10%) had isolated cranial vasculitis and 7 (23%) had both cranial and large vessel vasculitis. The glucocorticoid (GC) starting dose and GC doses during follow-up were higher in those with subclinical vasculitis until 12 months after diagnosis (p< 0.001). There was no difference in the duration of GC treatment (25 vs 20 months, p= 0.187). Cox proportional hazard regression analyses showed no difference in the proportion of patients able to stop GC (HR 0.78 [95% CI 0.49-1.25], p= 0.303) and in the proportion of patients with relapse (HR 0.82 [95%CI 0.50-1.36], p= 0.441). CONCLUSION: Only 9% of our PMR patients had subclinical vasculitis with a predilection for large vessel vasculitis. There were no differences in relapse rate and duration of GC treatment, however those with subclinical vasculitis received higher GC doses until 12 months after diagnosis. Prospective interventional trials are needed to evaluate the outcome of PMR patients with and without subclinical vasculitis treated with similar GC protocol.

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.

Oxytocin and state attachment responses to secure base support after stress in middle childhood.

We tried to replicate the finding that receiving care increases children's oxytocin and secure state attachment levels, and tested whether secure trait attachment moderates the oxytocin and state attachment response to care. 109 children (9-11 years old; M = 9.59; SD = 0.63; 34.9% boys) participated in a within-subject experiment. After stress induction (Trier Social Stress Test), children first remained alone and then received maternal secure base support. Salivary oxytocin was measured eight times. Secure trait and state attachment were measured with questionnaires, and Secure Base Script knowledge was assessed. Oxytocin levels increased after receiving secure base support from mother after having been alone. Secure state attachment changed less. Trait attachment and Secure Base Script knowledge did not moderate oxytocin or state attachment responses to support. This might mean that, regardless of the attachment history, in-the-moment positive attachment experiences might have a beneficial effect on trait attachment development in middle childhood.

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.

A neutral comparison of statistical methods for analyzing longitudinally measured ordinal outcomes in rare diseases.

Ordinal data in a repeated measures design of a crossover study for rare diseases usually do not allow for the use of standard parametric methods, and hence, nonparametric methods should be considered instead. However, only limited simulation studies in settings with small sample sizes exist. Therefore, starting from an Epidermolysis Bullosa simplex trial with the above-mentioned design, a rank-based approach using the R package nparLD and different generalized pairwise comparisons (GPC) methods were compared impartially in a simulation study. The results revealed that there was not one single best method for this particular design, because a trade-off exists between achieving high power, accounting for period effects, and for missing data. Specifically, nparLD as well as the unmatched GPC approaches do not address crossover aspects, and the univariate GPC variants partly ignore the longitudinal information. The matched GPC approaches, on the other hand, take the crossover effect into account in the sense of incorporating the within-subject association. Overall, the prioritized unmatched GPC method achieved the highest power in the simulation scenarios, although this may be due to the specified prioritization. The rank-based approach yielded good power even at a sample size of N = 6 $N=6$ , whereas the matched GPC method could not control the type I error.

Longitudinal trajectory of medial gastrocnemius muscle growth in the first years of life.

AIM: To define the longitudinal trajectory of gastrocnemius muscle growth in 6- to 36-month-old children with and without spastic cerebral palsy (SCP) and to compare trajectories by levels of gross motor function (Gross Motor Function Classification System, GMFCS) and presumed brain-lesion timing. METHOD: Twenty typically developing children and 24 children with SCP (GMFCS levels I-II/III-IV = 15/9), were included (28/16 females/males; mean age at first scan 15.4 months [standard deviation 4.93, range 6.24-23.8]). Three-dimensional freehand ultrasound was used to repeatedly assess muscle volume, length, and cross-sectional area (CSA), resulting in 138 assessments (mean interval 7.9 months). Brain lesion timing was evaluated with magnetic resonance imaging classification. Linear mixed-effects models defined growth rates, adjusted for GMFCS levels and presumed brain-lesion timing. RESULTS: At age 12 months, children with SCP showed smaller morphological muscle size than typically developing children (5.8 mL vs 9.8 mL, p < 0.001), while subsequently no differences in muscle growth were found between children with and without SCP (muscle volume: 0.65 mL/month vs 0.74 mL/month). However, muscle volume and CSA growth rates were lower in children classified in GMFCS levels III and IV than typically developing children and those classified in GMFCS levels I and II, with differences ranging from -56% to -70% (p < 0.001). INTERPRETATION: Muscle growth is already hampered during infancy in SCP. Muscle size growth further reduces with decreasing functional levels, independently from the brain lesion. Early monitoring of muscle growth combined with early intervention is needed.

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.

The value of point-of-care tests for the detection of SARS-CoV-2 RNA or antigen in bronchoalveolar lavage fluid.

BACKGROUND: Transmission of SARS-CoV-2 from donor to recipient is a clinically relevant risk for developing severe COVID-19 after lung transplantation (LTx). This risk of iatrogenic transmission can be reduced by timely detection of viral RNA or antigen in samples of bronchoalveolar lavage (BAL) fluid obtained at the time of lung procurement. We aimed to retrospectively evaluate the detection of SARS-CoV-2 RNA or antigen in BAL fluid samples using three point-of-care tests (POCTs). METHODS: BAL fluid samples came from patients hospitalized in an intensive care unit during the COVID-19 pandemic. These pandemic samples were scored as positive or negative for SARS-CoV-2 by a RT-qPCR comparator assay for orf1ab. Three commercially available POCTs were then evaluated: cobas SARS-CoV-2 & Influenza A/B assay with the cobas Liat RT-qPCR system (Roche Diagnostics), ID NOW COVID-19 and COVID-19 2.0 (Abbott), and SARS-CoV-2 Rapid Antigen Test (RAT) (Roche Diagnostics). Samples from the pre-pandemic era served as negative controls. RESULTS: We analyzed a total of 98 BAL fluid samples, each from a different patient: 58 positive pandemic samples (orf1ab Ct<38), 20 putatively negative pandemic samples (orf1ab Ct≥38), and 20 pre-pandemic samples. Univariate logistic regression shows that the probability of detection was highest for cobas Liat, followed by ID NOW, and then RAT. Of clinical relevance, cobas Liat detected SARS-CoV-2 RNA in 30 of the 31 positive pandemic samples that were collected within 10 days after RT-qPCR diagnosis of SARS-CoV-2 infection. None of the 20 pre-pandemic samples had a false-positive result for any POCT. CONCLUSIONS: POCTs enable the detection of SARS-CoV-2 RNA or antigen in BAL fluid samples and may provide additional information to decide if donor lungs are suitable for transplantation. Detection of respiratory pathogens with POCTs at the time of donor lung procurement is a potential strategy to increase safety in LTx by preventing iatrogenic transmission and severe postoperative infections.

Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials.

BACKGROUND: Recommendations for statistical methods in rare disease trials are scarce, especially for cross-over designs. As a result various state-of-the-art methodologies were compared as neutrally as possible using an illustrative data set from epidermolysis bullosa research to build recommendations for count, binary, and ordinal outcome variables. For this purpose, parametric (model averaging), semiparametric (generalized estimating equations type [GEE-like]) and nonparametric (generalized pairwise comparisons [GPC] and a marginal model implemented in the R package nparLD) methods were chosen by an international consortium of statisticians. RESULTS: It was found that there is no uniformly best method for the aforementioned types of outcome variables, but in particular situations, there are methods that perform better than others. Especially if maximizing power is the primary goal, the prioritized unmatched GPC method was able to achieve particularly good results, besides being appropriate for prioritizing clinically relevant time points. Model averaging led to favorable results in some scenarios especially within the binary outcome setting and, like the GEE-like semiparametric method, also allows for considering period and carry-over effects properly. Inference based on the nonparametric marginal model was able to achieve high power, especially in the ordinal outcome scenario, despite small sample sizes due to separate testing of treatment periods, and is suitable when longitudinal and interaction effects have to be considered. CONCLUSION: Overall, a balance has to be found between achieving high power, accounting for cross-over, period, or carry-over effects, and prioritizing clinically relevant time points.