Insights into treatment of complex Crohn's perianal fistulas.

Complex perianal fistula is a common complication of Crohn's disease (CD) which leads to negative impact on patient's quality of life. Successful management of the disease requires a multidisciplinary approach, including a gastroenterologist and a colorectal surgeon, applying combined surgical and medical therapy. One of frequently practiced surgical procedures is seton placement in the fistula tract, which is used to control perianal sepsis and drain the fistula, while preventing recurrent abscess formation.Darvadstrocel, a suspension of expanded, allogeneic, adipose-derived, mesenchymal stem cells, is safe and effective for treatment-refractory complex perianal fistulas in patients with Crohn's disease. Following approval of darvadstrocel, the INSPIRE registry is being conducted in order to evaluate long-term safety and effectiveness of the drug on a large, heterogenous population.An online expert meeting was held from March 20 to March 30, 2023, which provided relevant insights into the decision-making process regarding seton use and obtained feedback on the first experiences with darvadstrocel. The aim of this article is to present the perspectives from gastroenterologists and colorectal surgeons practicing in Czechia, Hungary, Israel, Lithuania, Serbia, and Slovenia in topics such as diagnosis and treatment options for patients with complex Crohn's perianal fistulas (CPF), specifically focusing on the use of setons and darvadstrocel.During this virtual session, unavailability of comprehensive data on safety and efficacy of available treatment procedures was emphasized as an important obstacle towards development of standardized recommendations and improvement of outcomes in treatment of (CPF). Furthermore, achieving consensus in seton use, duration of its placement, and frequency of change is recognized as one of CPF treatments major challenges. Despite these issues, it is important to promote better understanding and treatment of complex perianal fistulas in order to improve the quality of life of those affected by this condition.

Cardiac glycosides inhibit early and late vaccinia virus protein expression.

Cardiac glycosides (CGs) are natural steroid glycosides, which act as inhibitors of the cellular sodium-potassium ATPase pump. Although traditionally considered toxic to human cells, CGs are widely used as drugs for the treatment of cardiovascular-related medical conditions. More recently, CGs have been explored as potential anti-viral drugs and inhibit replication of a range of RNA and DNA viruses. Previously, a compound screen identified CGs that inhibited vaccinia virus (VACV) infection. However, no further investigation of the inhibitory potential of these compounds was performed, nor was there investigation of the stage(s) of the poxvirus lifecycle they impacted. Here, we investigated the anti-poxvirus activity of a broad panel of CGs. We found that all CGs tested were potent inhibitors of VACV replication. Our virological experiments showed that CGs did not impact virus infectivity, binding, or entry. Rather, experiments using recombinant viruses expressing reporter proteins controlled by VACV promoters and arabinoside release assays demonstrated that CGs inhibited early and late VACV protein expression at different concentrations. Lack of virus assembly in the presence of CGs was confirmed using electron microscopy. Thus, we expand our understanding of compounds with anti-poxvirus activity and highlight a yet unrecognized mechanism by which poxvirus replication can be inhibited.

Handling missing data when estimating causal effects with Targeted Maximum Likelihood Estimation.

Targeted Maximum Likelihood Estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive approaches is unclear. Based on the Victorian Adolescent Health Cohort Study, we conducted a simulation study to evaluate eight missing data methods in this context: complete-case analysis, extended TMLE incorporating outcome-missingness model, missing covariate missing indicator method, five multiple imputation (MI) approaches using parametric or machine-learning models. Six scenarios were considered, varying in exposure/outcome generation models (presence of confounder-confounder interactions) and missingness mechanisms (whether outcome influenced missingness in other variables and presence of interaction/non-linear terms in missingness models). Complete-case analysis and extended TMLE had small biases when outcome did not influence missingness in other variables. Parametric MI without interactions had large bias when exposure/outcome generation models included interactions. Parametric MI including interactions performed best in bias and variance reduction across all settings, except when missingness models included a non-linear term. When choosing a method to handle missing data in the context of TMLE, researchers must consider the missingness mechanism and, for MI, compatibility with the analysis method. In many settings, a parametric MI approach that incorporates interactions and non-linearities is expected to perform well.

Increasing non-cosmetic exposure and sensitization to isothiazolinones require action for prevention: Review.

Frequent use of methylchloroisothiazolinone/methylisothiazolinone (MCI/MI) and MI in cosmetic products has been the main cause of widespread sensitization and allergic contact dermatitis to these preservatives (biocides). Their use in non-cosmetic products is also an important source of sensitization. Less is known about sensitization rates and use of benzisothiazolinone (BIT), octylisothiazolinone (OIT), and dichlorooctylisothiazolinone (DCOIT), which have never been permitted in cosmetic products in Europe. BIT and OIT have occasionally been routinely patch-tested. These preservatives are often used together in chemical products and articles. In this study, we review the occurrence of contact allergy to MI, BIT, OIT, and DCOIT over time, based on concomitant patch testing in large studies, and case reports. We review EU legislations, and we discuss the role of industry, regulators, and dermatology in prevention of sensitization and protection of health. The frequency of contact allergy to MI, BIT, and OIT has increased. The frequency of contact allergy to DCOIT is not known because it has seldom been patch-tested. Label information on isothiazolinones in chemical products and articles, irrespective of concentration, is required for assessment of relevance, information to patients, and avoidance of exposure and allergic contact dermatitis.

Methodological review of NMA bias concepts provides groundwork for the development of a list of concepts for potential inclusion in a new risk of bias tool for network meta-analysis (RoB NMA Tool).

INTRODUCTION: Network meta-analyses (NMAs) have gained popularity and grown in number due to their ability to provide estimates of the comparative effectiveness of multiple treatments for the same condition. The aim of this study is to conduct a methodological review to compile a preliminary list of concepts related to bias in NMAs. METHODS AND ANALYSIS: We included papers that present items related to bias, reporting or methodological quality, papers assessing the quality of NMAs, or method papers. We searched MEDLINE, the Cochrane Library and unpublished literature (up to July 2020). We extracted items related to bias in NMAs. An item was excluded if it related to general systematic review quality or bias and was included in currently available tools such as ROBIS or AMSTAR 2. We reworded items, typically structured as questions, into concepts (i.e. general notions). RESULTS: One hundred eighty-one articles were assessed in full text and 58 were included. Of these articles, 12 were tools, checklists or journal standards; 13 were guidance documents for NMAs; 27 were studies related to bias or NMA methods; and 6 were papers assessing the quality of NMAs. These studies yielded 99 items of which the majority related to general systematic review quality and biases and were therefore excluded. The 22 items we included were reworded into concepts specific to bias in NMAs. CONCLUSIONS: A list of 22 concepts was included. This list is not intended to be used to assess biases in NMAs, but to inform the development of items to be included in our tool.

HIGHLIGHTS: • Our research aimed to develop a preliminary list of concepts related to bias with the goal of developing the first tool for assessing the risk of bias in the results and conclusions of a network meta-analysis (NMA).• We followed the methodology proposed by Whiting (2017) and Sanderson (2007) for creating systematically developed lists of quality items, as a first step in the development of a risk of bias tool for network meta-analysis (RoB NMA Tool).• We included items related to biases in NMAs and excluded items that are equally applicable to all systematic reviews as they are covered by other tools (e.g. ROBIS, AMSTAR 2).• Fifty-seven studies were included generating 99 items, which when screened, yielded 22 included items. These items were then reworded into concepts in preparation for a Delphi process for further vetting by external experts.• A limitation of our study is the challenge in retrieving methods studies as methods collections are not regularly updated.

A comparison of strategies for selecting auxiliary variables for multiple imputation.

Multiple imputation (MI) is a popular method for handling missing data. Auxiliary variables can be added to the imputation model(s) to improve MI estimates. However, the choice of which auxiliary variables to include is not always straightforward. Several data-driven auxiliary variable selection strategies have been proposed, but there has been limited evaluation of their performance. Using a simulation study we evaluated the performance of eight auxiliary variable selection strategies: (1, 2) two versions of selection based on correlations in the observed data; (3) selection using hypothesis tests of the "missing completely at random" assumption; (4) replacing auxiliary variables with their principal components; (5, 6) forward and forward stepwise selection; (7) forward selection based on the estimated fraction of missing information; and (8) selection via the least absolute shrinkage and selection operator (LASSO). A complete case analysis and an MI analysis using all auxiliary variables (the "full model") were included for comparison. We also applied all strategies to a motivating case study. The full model outperformed all auxiliary variable selection strategies in the simulation study, with the LASSO strategy the best performing auxiliary variable selection strategy overall. All MI analysis strategies that we were able to apply to the case study led to similar estimates, although computational time was substantially reduced when variable selection was employed. This study provides further support for adopting an inclusive auxiliary variable strategy where possible. Auxiliary variable selection using the LASSO may be a promising alternative when the full model fails or is too burdensome.

Phases of methodological research in biostatistics-Building the evidence base for new methods.

Although new biostatistical methods are published at a very high rate, many of these developments are not trustworthy enough to be adopted by the scientific community. We propose a framework to think about how a piece of methodological work contributes to the evidence base for a method. Similar to the well-known phases of clinical research in drug development, we propose to define four phases of methodological research. These four phases cover (I) proposing a new methodological idea while providing, for example, logical reasoning or proofs, (II) providing empirical evidence, first in a narrow target setting, then (III) in an extended range of settings and for various outcomes, accompanied by appropriate application examples, and (IV) investigations that establish a method as sufficiently well-understood to know when it is preferred over others and when it is not; that is, its pitfalls. We suggest basic definitions of the four phases to provoke thought and discussion rather than devising an unambiguous classification of studies into phases. Too many methodological developments finish before phase III/IV, but we give two examples with references. Our concept rebalances the emphasis to studies in phases III and IV, that is, carefully planned method comparison studies and studies that explore the empirical properties of existing methods in a wider range of problems.

Mesenchymal stem cell therapy for Crohn's perianal fistula-a real-world experience.

AIM: Remission rates of medically and surgically treated complex perianal fistulas in Crohn's disease are low. Recently, trials have demonstrated the potential for long-term remission with local injection of allogeneic adipose-derived mesenchymal stem cells (darvadstrocel). Our aim was to analyse outcomes from our real-world experience with this new treatment. METHODS: All patients with Crohn's disease suffering complex perianal fistulas who consecutively underwent administration of darvadstrocel at two centres were followed up and evaluated. Patients were assessed for clinical remission, response, failure, and any complications during follow-up. The results of all patients with a minimum of 3 months' follow-up are presented. RESULTS: Thirty-three patients with Crohn's disease and complex perianal fistulas were included. Of these, 20 (61%) experienced clinical remission that was maintained for a mean follow-up of 14 (3-32) months. A total of 24 of 33 (73%) experienced at least 3 months of clinical remission, with four later having recurrence (3-12 months). Among the remaining nine patients who did not experience clinical remission, two (6%) had partial remission (such as one of two fistulas closing), two (6%) showed signs of response but not remission, and five (15%) showed no signs of healing. The mean time to maintained clinical remission was 6 weeks (range 2 weeks to 6 months), and there were no severe adverse events. CONCLUSION: In this real-world experience, treatment of Crohn's disease complex perianal fistulas with darvadstrocel had a 61% success rate for maintained clinical remission.

How to check a simulation study.

Simulation studies are powerful tools in epidemiology and biostatistics, but they can be hard to conduct successfully. Sometimes unexpected results are obtained. We offer advice on how to check a simulation study when this occurs, and how to design and conduct the study to give results that are easier to check. Simulation studies should be designed to include some settings in which answers are already known. They should be coded in stages, with data-generating mechanisms checked before simulated data are analysed. Results should be explored carefully, with scatterplots of standard error estimates against point estimates surprisingly powerful tools. Failed estimation and outlying estimates should be identified and dealt with by changing data-generating mechanisms or coding realistic hybrid analysis procedures. Finally, we give a series of ideas that have been useful to us in the past for checking unexpected results. Following our advice may help to prevent errors and to improve the quality of published simulation studies.

Use of multiple covariates in assessing treatment-effect modifiers: A methodological review of individual participant data meta-analyses.

Individual participant data (IPD) meta-analyses of randomised trials are considered a reliable way to assess participant-level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored one covariate at a time, which gives rise to the possibility that evidence of treatment-covariate interaction may be due to confounding from a different, related covariate. We aimed to evaluate current practice when estimating treatment-covariate interactions in IPD meta-analysis, specifically focusing on involvement of additional covariates in the models. We reviewed 100 IPD meta-analyses of randomised trials, published between 2015 and 2020, that assessed at least one treatment-covariate interaction. We identified four approaches to handling additional covariates: (1) Single interaction model (unadjusted): No additional covariates included (57/100 IPD meta-analyses); (2) Single interaction model (adjusted): Adjustment for the main effect of at least one additional covariate (35/100); (3) Multiple interactions model: Adjustment for at least one two-way interaction between treatment and an additional covariate (3/100); and (4) Three-way interaction model: Three-way interaction formed between treatment, the additional covariate and the potential effect modifier (5/100). IPD is not being utilised to its fullest extent. In an exemplar dataset, we demonstrate how these approaches lead to different conclusions. Researchers should adjust for additional covariates when estimating interactions in IPD meta-analysis providing they adjust their main effects, which is already widely recommended. Further, they should consider whether more complex approaches could provide better information on who might benefit most from treatments, improving patient choice and treatment policy and practice.

When should factorial designs be used for late-phase randomised controlled trials?

BACKGROUND: A 2×2 factorial design evaluates two interventions (A versus control and B versus control) by randomising to control, A-only, B-only or both A and B together. Extended factorial designs are also possible (e.g. 3×3 or 2×2×2). Factorial designs often require fewer resources and participants than alternative randomised controlled trials, but they are not widely used. We identified several issues that investigators considering this design need to address, before they use it in a late-phase setting. METHODS: We surveyed journal articles published in 2000-2022 relating to designing factorial randomised controlled trials. We identified issues to consider based on these and our personal experiences. RESULTS: We identified clinical, practical, statistical and external issues that make factorial randomised controlled trials more desirable. Clinical issues are (1) interventions can be easily co-administered; (2) risk of safety issues from co-administration above individual risks of the separate interventions is low; (3) safety or efficacy data are wanted on the combination intervention; (4) potential for interaction (e.g. effect of A differing when B administered) is low; (5) it is important to compare interventions with other interventions balanced, rather than allowing randomised interventions to affect the choice of other interventions; (6) eligibility criteria for different interventions are similar. Practical issues are (7) recruitment is not harmed by testing many interventions; (8) each intervention and associated toxicities is unlikely to reduce either adherence to the other intervention or overall follow-up; (9) blinding is easy to implement or not required. Statistical issues are (10) a suitable scale of analysis can be identified; (11) adjustment for multiplicity is not required; (12) early stopping for efficacy or lack of benefit can be done effectively. External issues are (13) adequate funding is available and (14) the trial is not intended for licensing purposes. An overarching issue (15) is that factorial design should give a lower sample size requirement than alternative designs. Across designs with varying non-adherence, retention, intervention effects and interaction effects, 2×2 factorial designs require lower sample size than a three-arm alternative when one intervention effect is reduced by no more than 24%-48% in the presence of the other intervention compared with in the absence of the other intervention. CONCLUSIONS: Factorial designs are not widely used and should be considered more often using our issues to consider. Low potential for at most small to modest interaction is key, for example, where the interventions have different mechanisms of action or target different aspects of the disease being studied.

Comment on Oberman & Vink: Should we fix or simulate the complete data in simulation studies evaluating missing data methods?

For simulation studies that evaluate methods of handling missing data, we argue that generating partially observed data by fixing the complete data and repeatedly simulating the missingness indicators is a superficially attractive idea but only rarely appropriate to use.

Early Initiation of Adalimumab Significantly Diminishes Postoperative Crohn's Disease Endoscopic Recurrence and Is Superior to 6-Mercaptopurine Therapy: An Open-Label, Randomized Controlled Study.

Postoperative recurrence (POR) is the rule in patients with Crohn's disease (CD), mitigated with prophylactic therapy. The evidence for therapeutic choice and timing of intervention is lacking. We aimed to compare the rates of POR in patients treated early with prophylactic 6-mercaptopurine (6-MP) or adalimumab. We conducted a prospective single-center randomized open-label clinical study in which patients in surgical remission following their first ileocecectomy were randomized to receive early treatment with 6-MP or adalimumab. Patients were followed up clinically every 3 months and underwent endoscopy at weeks 32 and 58 postoperatively. The primary endpoint was endoscopic recurrence (ePOR) at 1 year (week 58), defined as a Rutgeerts score ≥ i2. We enrolled 35 patients (25 males, mean age 35 ± 1.4 years, median disease duration 5 ± 6.1 years) following ileocecectomy. Of these, seven (20%) were current smokers and nine (26%) biologics-experienced. Patients allocated to adalimumab had significantly less ePOR than patients treated with 6MP at week 32 (21% vs. 69%, p = 0.004) and 58 (47% vs. 75%), (p = 0.03, HR = 0.39, 95% CI = 0.16-0.93). POR was associated with an increased diameter of the resected small bowel surgical specimen, lower baseline body mass index (BMI), increased week 18 fecal calprotectin, increased week 18 serum alanine aminotransferase and decreased week 18 hemoglobin level. Adalimumab was more effective than 6-MP in preventing ePOR. Increased operative small bowel diameter and lower postoperative BMI were associated with ePOR. At eighteen weeks, serum hemoglobin, ALT and fecal calprotectin levels were predictive of endoscopic disease recurrence. (ClinicalTrials.gov ID NCT01629628).

How important is the linearity assumption in a sample size calculation for a randomised controlled trial where treatment is anticipated to affect a rate of change?

BACKGROUND: For certain conditions, treatments aim to lessen deterioration over time. A trial outcome could be change in a continuous measure, analysed using a random slopes model with a different slope in each treatment group. A sample size for a trial with a particular schedule of visits (e.g. annually for three years) can be obtained using a two-stage process. First, relevant (co-) variances are estimated from a pre-existing dataset e.g. an observational study conducted in a similar setting. Second, standard formulae are used to calculate sample size. However, the random slopes model assumes linear trajectories with any difference in group means increasing proportionally to follow-up time. The impact of these assumptions failing is unclear. METHODS: We used simulation to assess the impact of a non-linear trajectory and/or non-proportional treatment effect on the proposed trial's power. We used four trajectories, both linear and non-linear, and simulated observational studies to calculate sample sizes. Trials of this size were then simulated, with treatment effects proportional or non-proportional to time. RESULTS: For a proportional treatment effect and a trial visit schedule matching the observational study, powers are close to nominal even for non-linear trajectories. However, if the schedule does not match the observational study, powers can be above or below nominal levels, with the extent of this depending on parameters such as the residual error variance. For a non-proportional treatment effect, using a random slopes model can lead to powers far from nominal levels. CONCLUSIONS: If trajectories are suspected to be non-linear, observational data used to inform power calculations should have the same visit schedule as the proposed trial where possible. Additionally, if the treatment effect is expected to be non-proportional, the random slopes model should not be used. A model allowing trajectories to vary freely over time could be used instead, either as a second line analysis method (bearing in mind that power will be lost) or when powering the trial.