Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

While the SARS-CoV-2 (COVID-19) pandemic has led to an impressive and unprecedented initiation of clinical research, it has also led to considerable disruption of clinical trials in other disease areas, with around 80% of non-COVID-19 trials stopped or interrupted during the pandemic. In many cases the disrupted trials will not have the planned statistical power necessary to yield interpretable results. This paper describes methods to compensate for the information loss arising from trial disruptions by incorporating additional information available from auxiliary data sources. The methods described include the use of auxiliary data on baseline and early outcome data available from the trial itself and frequentist and Bayesian approaches for the incorporation of information from external data sources. The methods are illustrated by application to the analysis of artificial data based on the Primary care pediatrics Learning Activity Nutrition (PLAN) study, a clinical trial assessing a diet and exercise intervention for overweight children, that was affected by the COVID-19 pandemic. We show how all of the methods proposed lead to an increase in precision relative to use of complete case data only. [ FROM AUTHOR] Copyright of Statistics in Biopharmaceutical Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Impact of early corticosteroids on 60-day mortality in critically ill patients with COVID-19: A multicenter cohort study of the OUTCOMEREA network.

OBJECTIVES: In severe COVID-19 pneumonia, the appropriate timing and dosing of corticosteroids (CS) is not known. Patient subgroups for which CS could be more beneficial also need appraisal. The aim of this study was to assess the effect of early CS in COVID-19 pneumonia patients admitted to the ICU on the occurrence of 60-day mortality, ICU-acquired-bloodstream infections(ICU-BSI), and hospital-acquired pneumonia and ventilator-associated pneumonia(HAP-VAP). METHODS: We included patients with COVID-19 pneumonia admitted to 11 ICUs belonging to the French OutcomeReaTM network from January to May 2020. We used survival models with ponderation with inverse probability of treatment weighting (IPTW). RESULTS: The study population comprised 303 patients having a median age of 61.6 (53-70) years of whom 78.8% were male and 58.6% had at least one comorbidity. The median SAPS II was 33 (25-44). Invasive mechanical ventilation was required in 34.8% of the patients. Sixty-six (21.8%) patients were in the Early-C subgroup. Overall, 60-day mortality was 29.4%. The risks of 60-day mortality (IPTWHR = 0.86;95% CI 0.54 to 1.35, p = 0.51), ICU-BSI and HAP-VAP were similar in the two groups. Importantly, early CS treatment was associated with a lower mortality rate in patients aged 60 years or more (IPTWHR, 0.53;95% CI, 0.3-0.93; p = 0.03). In contrast, CS was associated with an increased risk of death in patients younger than 60 years without inflammation on admission (IPTWHR = 5.01;95% CI, 1.05, 23.88; p = 0.04). CONCLUSION: For patients with COVID-19 pneumonia, early CS treatment was not associated with patient survival. Interestingly, inflammation and age can significantly influence the effect of CS.

The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia.

The treatment of respiratory tract infections is threatened by the emergence of bacterial resistance. Immunomodulatory drugs, which enhance airway innate immune defenses, may improve therapeutic outcome. In this concept paper, we aim to highlight the utility of pharmacometrics and Bayesian inference in the development of immunomodulatory therapeutic agents as an adjunct to antibiotics in the context of pneumonia. For this, two case studies of translational modelling and simulation frameworks are introduced for these types of drugs up to clinical use. First, we evaluate the pharmacokinetic/pharmacodynamic relationship of an experimental combination of amoxicillin and a TLR4 agonist, monophosphoryl lipid A, by developing a pharmacometric model accounting for interaction and potential translation to humans. Capitalizing on this knowledge and associating clinical trial extrapolation and statistical modelling approaches, we then investigate the TLR5 agonist flagellin. The resulting workflow combines expert and prior knowledge on the compound with the in vitro and in vivo data generated during exploratory studies in order to construct high-dimensional models considering the pharmacokinetics and pharmacodynamics of the compound. This workflow can be used to refine preclinical experiments, estimate the best doses for human studies, and create an adaptive knowledge-based design for the next phases of clinical development.

Multistate Modeling of COVID-19 Patients Using a Large Multicentric Prospective Cohort of Critically Ill Patients.

The mortality of COVID-19 patients in the intensive care unit (ICU) is influenced by their state at admission. We aimed to model COVID-19 acute respiratory distress syndrome state transitions from ICU admission to day 60 outcome and to evaluate possible prognostic factors. We analyzed a prospective French database that includes critically ill COVID-19 patients. A six-state multistate model was built and 17 transitions were analyzed either using a non-parametric approach or a Cox proportional hazard model. Corticosteroids and IL-antagonists (tocilizumab and anakinra) effects were evaluated using G-computation. We included 382 patients in the analysis: 243 patients were admitted to the ICU with non-invasive ventilation, 116 with invasive mechanical ventilation, and 23 with extracorporeal membrane oxygenation. The predicted 60-day mortality was 25.9% (95% CI: 21.8%-30.0%), 44.7% (95% CI: 48.8%-50.6%), and 59.2% (95% CI: 49.4%-69.0%) for a patient admitted in these three states, respectively. Corticosteroids decreased the risk of being invasively ventilated (hazard ratio (HR) 0.59, 95% CI: 0.39-0.90) and IL-antagonists increased the probability of being successfully extubated (HR 1.8, 95% CI: 1.02-3.17). Antiviral drugs did not impact any transition. In conclusion, we observed that the day-60 outcome in COVID-19 patients is highly dependent on the first ventilation state upon ICU admission. Moreover, we illustrated that corticosteroid and IL-antagonists may influence the intubation duration.

Association Between Early Invasive Mechanical Ventilation and Day-60 Mortality in Acute Hypoxemic Respiratory Failure Related to Coronavirus Disease-2019 Pneumonia.

OBJECTIVES: About 5% of patients with coronavirus disease-2019 are admitted to the ICU for acute hypoxemic respiratory failure. Opinions differ on whether invasive mechanical ventilation should be used as first-line therapy over noninvasive oxygen support. The aim of the study was to assess the effect of early invasive mechanical ventilation in coronavirus disease-2019 with acute hypoxemic respiratory failure on day-60 mortality. DESIGN: Multicenter prospective French observational study. SETTING: Eleven ICUs of the French OutcomeRea network. PATIENTS: Coronavirus disease-2019 patients with acute hypoxemic respiratory failure (Pao2/Fio2 ≤ 300 mm Hg), without shock or neurologic failure on ICU admission, and not referred from another ICU or intermediate care unit were included. INTERVENTION: We compared day-60 mortality in patients who were on invasive mechanical ventilation within the first 2 calendar days of the ICU stay (early invasive mechanical ventilation group) and those who were not (nonearly invasive mechanical ventilation group). We used a Cox proportional-hazard model weighted by inverse probability of early invasive mechanical ventilation to determine the risk of death at day 60. MEASUREMENT AND MAIN RESULTS: The 245 patients included had a median (interquartile range) age of 61 years (52-69 yr), a Simplified Acute Physiology Score II score of 34 mm Hg (26-44 mm Hg), and a Pao2/Fio2 of 121 mm Hg (90-174 mm Hg). The rates of ICU-acquired pneumonia, bacteremia, and the ICU length of stay were significantly higher in the early (n = 117 [48%]) than in the nonearly invasive mechanical ventilation group (n = 128 [52%]), p < 0.01. Day-60 mortality was 42.7% and 21.9% in the early and nonearly invasive mechanical ventilation groups, respectively. The weighted model showed that early invasive mechanical ventilation increased the risk for day-60 mortality (weighted hazard ratio =1.74; 95% CI, 1.07-2.83, p=0.03). CONCLUSIONS: In ICU patients admitted with coronavirus disease-2019-induced acute hypoxemic respiratory failure, early invasive mechanical ventilation was associated with an increased risk of day-60 mortality. This result needs to be confirmed.