An Overview of Regression Models for Adverse Events Analysis.

Over the last few years, several review articles described the adverse events analysis as sub-optimal in clinical trials. Indeed, the context surrounding adverse events analyses often imply an overwhelming number of events, a lack of power to find associations, but also a lack of specific training regarding those complex data. In randomized controlled trials or in observational studies, comparing the occurrence of adverse events according to a covariable of interest (e.g., treatment) is a recurrent question in the analysis of drug safety data, and adjusting other important factors is often relevant. This article is an overview of the existing regression models that may be considered to compare adverse events and to discuss model choice regarding the characteristics of the adverse events of interest. Many dimensions may be relevant to compare the adverse events between patients, (e.g., timing, recurrence, and severity). Recent efforts have been made to cover all of them. For chronic treatments, the occurrence of intercurrent events during the patient follow-up usually needs the modeling approach to be adapted (at least with regard to their interpretation). Moreover, analysis based on regression models should not be limited to the estimation of relative effects. Indeed, absolute risks stemming from the model should be presented systematically to help the interpretation, to validate the model, and to encourage comparison of studies.

Anesthesia practices for management of labor pain and cesarean delivery in France (EPIDOL): A cross-sectional survey.

BACKGROUND: This study aimed to collect obstetric anesthesia practice and patient-reported outcomes as an update to the last French Obstetric Anesthesia survey from 1996. METHODS: Maternity units were randomly selected across France and surveyed for 7 consecutive days from February, 2016, to January, 2017. Data was gathered prospectively by questionnaires filled out by patients and anesthesia providers. RESULTS: There were 1885 questionnaires received from 56 units, with 379 cesarean delivery (CD) and 1506 vaginal delivery (VD) cases analyzed. The overall neuraxial labor analgesia (NLA) rate was 82.5% (95% CI [82.4-82.6]), with 70.3% (95% CI [71.4-71.6]) receiving automated administration (PCEA/PIEB). NLA was effective throughout labor in 68.2% of cases, however, severe pain was reported by 29.4% of patients. The overall rate of alternative approaches for labor analgesia was 19.5% (95%CI [19.2-19.7]). Obesity (OR 2.8; 95% CI [1.0-7.5], p < 0.04) and delivery in level I units (OR 0.6; 95% CI [0.5-0.9], p < 0.01) were associated with severe pain during VD. Satisfaction was found to be similar in patients delivering with or without NLA. The incidence of pain during CD was similar in scheduled versus non-scheduled CD. Failure of NLA during CD was associated with severe pain (OR 10.0; 95% CI [3.1-31.9], p < 0.01) and dissatisfaction (OR 26.2; 95% CI [3.0-225.1], p < 0.01). CONCLUSION: Despite the high NLA rate in France, a significant proportion of women experience severe pain during labor and delivery. This study emphasizes the need for further practice guidelines in obstetric anesthesia to ensure optimal pain management and improve patients' experience during childbirth. CLINICALTRIALS: govNCT02853890.

Monitoring multidimensional aspects of quality of life after cancer immunotherapy: protocol for the international multicentre, observational QUALITOP cohort study.

INTRODUCTION: Immunotherapies, such as immune checkpoint inhibitors and chimeric antigen receptor T-cell therapy, have significantly improved the clinical outcomes of various malignancies. However, they also cause immune-related adverse events (irAEs) that can be challenging to predict, prevent and treat. Although they likely interact with health-related quality of life (HRQoL), most existing evidence on this topic has come from clinical trials with eligibility criteria that may not accurately reflect real-world settings. The QUALITOP project will study HRQoL in relation to irAEs and its determinants in a real-world study of patients treated with immunotherapy. METHODS AND ANALYSIS: This international, observational, multicentre study takes place in France, the Netherlands, Portugal and Spain. We aim to include about 1800 adult patients with cancer treated with immunotherapy in a specifically recruited prospective cohort, and to additionally obtain data from historical real-world databases (ie, databiobanks) and medical administrative registries (ie, national cancer registries) in which relevant data regarding other adult patients with cancer treated with immunotherapy has already been stored. In the prospective cohort, clinical health status, HRQoL and psychosocial well-being will be monitored until 18 months after treatment initiation through questionnaires (at baseline and 3, 6, 12 and 18 months thereafter), and by data extraction from electronic patient files. Using advanced statistical methods, including causal inference methods, artificial intelligence algorithms and simulation modelling, we will use data from the QUALITOP cohort to improve the understanding of the complex relationships among treatment regimens, patient characteristics, irAEs and HRQoL. ETHICS AND DISSEMINATION: All aspects of the QUALITOP project will be conducted in accordance with the Declaration of Helsinki and with ethical approval from a suitable local ethics committee, and all patients will provide signed informed consent. In addition to standard dissemination efforts in the scientific literature, the data and outcomes will contribute to a smart digital platform and medical data lake. These will (1) help increase knowledge about the impact of immunotherapy, (2) facilitate improved interactions between patients, clinicians and the general population and (3) contribute to personalised medicine. TRIAL REGISTRATION NUMBER: NCT05626764.

Evolution of the proportion of colistin-resistant isolates in animal clinical Escherichia coli over time - A hierarchical mixture model approach.

Colistin resistance has been the subject of much attention since mcr genes encoding plasmid-mediated colistin resistance description in 2015. To date, surveillance data about resistance levels encountered in food-producing animals are scarce. In France, the Resapath dataset, consisting in a large collection of disk diffusion antibiogram results transmitted by a network of laboratories. It offers a unique opportunity to study the evolution of resistance towards colistin over the past 15 years in Escherichia coli isolated from diseased food-producing animals. This study used a Bayesian hierarchical Gaussian mixture model to estimate the resistant proportions from those data. This non-classical approach deals with the colistin-specific problem of overlapping distributions of diameters measured for susceptible and resistant isolates that makes the definition of epidemiological cut-off very hard. This model also considers the variability observed between the measurements performed by different laboratories. Proportion of resistant isolates has been calculated for several food-producing animals and most encountered diseases. From those estimations, a marked evolution of the proportions of resistant isolates is noticeable, for swine suffering from digestive disorders. In this group, an increase over the 2006-2011 period from 0.1% [ 0.0%, 1.2%] in 2006-28.6% [25.1%, 32.3%] in 2011 was followed by a decrease to reach 3.6% [2.3%;5.3%] in 2018. For isolates related to digestive disorders in calves, percentages increased and reached 7% in 2009 then decreased as for swine. In contrast, for poultry productions, estimated proportions and credibility intervals were constantly very close to zero.