Estimating the Inter- and Intra-Rater Reliability for NASH Fibrosis Staging in the Presence of Bridge Ordinal Ratings with Hierarchical Bridge Category Models

The public health burden of non-alcoholic steatohepatitis (NASH), a liver condition characterized by excessive lipid accumulation and subsequent tissue inflammation and fibrosis, has burgeoned with the spread of western lifestyle habits. Progression of fibrosis into cirrhosis is assessed using histological staging scales (e.g., NASH Clinical Research Network (NASH CRN)). These scales are used to monitor disease progression as well as to evaluate the effectiveness of therapies. However, clinical drug trials for NASH are typically underpowered due to lower than expected inter-/intra-rater reliability, which impacts measurements at screening, baseline, and endpoint. Bridge ratings represent a phenomenon where pathologists assign two adjacent stages simultaneously during assessment and may further complicate these analyses when ad hoc procedures are applied. Statistical techniques, dubbed Bridge Category Models, have been developed to account for bridge ratings, but not for the scenario where multiple pathologists assess biopsies across time points. Here, we develop hierarchical Bayesian extensions for these statistical methods to account for repeat observations and use these methods to assess the impact of bridge ratings on the inter-/intra-rater reliability of the NASH CRN staging scale. We also report on how pathologists may differ in their assignment of bridge ratings to highlight different staging practices. Our findings suggest that Bridge Category Models can capture additional fibrosis staging heterogeneity with greater precision, which translates to potentially higher reliability estimates in contrast to the information lost through ad hoc approaches.

Analysis of Suspension of Clinical Trials for Drug Registration in China

Suspension is an important risk control measure during clinical trials. We investigated the use of this in China and identified common reasons for suspension by analyzing trends, hold issues, outcomes, background and design characteristics of suspended clinical drug trials from January 1, 2013 to December 1, 2019. A total of 298 clinical trials during the study timeframe were registered, accounting for 3.1% of all clinical drug trials. Numbers and proportion of clinical trials suspended based on benefit/risk factors have been increasing without holds on registrations by Center for Drug Evaluation. Reasons for suspension vary among trial phases, benefit and risk factors, protocol issues etc. 67% of trials that have been on hold for >1 year were still on hold at the time of this analysis. Children and the elderly were enrolled in 4.1% and 41% of the suspended trials, respectively. Strengthening regulation of pre-market pharmacovigilance through optimizing reporting and monitoring of safety information during clinical trial is thus needed. Establishing a closed-loop treatment mechanism for trial suspension is also important. Examination of potential risks, such as the quality of protocols, the ability of the institution to support the trial, and the adequacy of supplies of the investigational product is needed before beginning clinical trials. More careful evaluation at the drug registration phase will reduce the frequency of suspension and protect subjects after suspension occurs.

COVID-19: can we treat the mother without harming her baby?

Medical care is predicated on ‘do no harm’, yet the urgency to find drugs and vaccines to treat or prevent COVID-19 has led to an extraordinary effort to develop and test new therapies. Whilst this is an essential cornerstone of a united global response to the COVID-19 pandemic, the absolute requirements for meticulous efficacy and safety data remain. This is especially pertinent to the needs of pregnant women; a group traditionally poorly represented in drug trials, yet a group at heightened risk of unintended adverse materno-fetal consequences due to the unique physiology of pregnancy and the life course implications of fetal or neonatal drug exposure. However, due to the complexities of drug trial participation when pregnant (be they vaccines or therapeutics for acute disease), many clinical drug trials will exclude them. Clinicians must determine the best course of drug treatment with a dearth of evidence from either clinical or preclinical studies, where at least in the short term they may be more focused on the outcome of the mother than of her offspring.

RAINFOREST: a random forest approach to predict treatment benefit in data from (failed) clinical drug trials

Motivation When phase III clinical drug trials fail their endpoint, enormous resources are wasted. Moreover, even if a clinical trial demonstrates a significant benefit, the observed effects are often small and may not outweigh the side effects of the drug. Therefore, there is a great clinical need for methods to identify genetic markers that can identify subgroups of patients which are likely to benefit from treatment as this may (i) rescue failed clinical trials and/or (ii) identify subgroups of patients which benefit more than the population as a whole. When single genetic biomarkers cannot be found, machine learning approaches that find multivariate signatures are required. For single nucleotide polymorphism (SNP) profiles, this is extremely challenging owing to the high dimensionality of the data. Here, we introduce RAINFOREST (tReAtment benefIt prediction using raNdom FOREST), which can predict treatment benefit from patient SNP profiles obtained in a clinical trial setting. Results We demonstrate the performance of RAINFOREST on the CAIRO2 dataset, a phase III clinical trial which tested the addition of cetuximab treatment for metastatic colorectal cancer and concluded there was no benefit. However, we find that RAINFOREST is able to identify a subgroup comprising 27.7% of the patients that do benefit, with a hazard ratio of 0.69 (P = 0.04) in favor of cetuximab. The method is not specific to colorectal cancer and could aid in reanalysis of clinical trial data and provide a more personalized approach to cancer treatment, also when there is no clear link between a single variant and treatment benefit. Availability and implementation The R code used to produce the results in this paper can be found at github.com/jubels/RAINFOREST. A more configurable, user-friendly Python implementation of RAINFOREST is also provided. Due to restrictions based on privacy regulations and informed consent of participants, phenotype and genotype data of the CAIRO2 trial cannot be made freely available in a public repository. Data from this study can be obtained upon request. Requests should be directed toward Prof. Dr. H.J. Guchelaar ([email protected]). Supplementary information Supplementary data are available at Bioinformatics online.

Course and treatment of diabetes mellitus in the context of COVID-19

In 2020, the world is facing a historically unparalleled public health challenge associated with the invasion of the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This is also a challenge for the healthcare systems worldwide. Patients with diabetes mellitus (DM) are most vulnerable to COVID-19 because of the peculiarities of their immune response to a virus attack and due to their high susceptibility to viral activity because of hyperglycemia and other comorbid conditions and obesity that often accompany DM. The severity of the COVID-19 disease requires a mandatory review of the usual anti-hyperglycemic therapy. Maintaining optimal glycemic control and preventing the development of ketoacidosis remain extremely important; therefore, insulin becomes the priority drug for glycemic control in most cases. The search for new drugs to fight against the coronavirus infection continues with new randomised clinical drug trials being launched. Innovative anti-diabetic agents are also being tested as candidates for potentially effective anti-coronavirus agents.