Molnupiravir plus usual care versus usual care alone as early treatment for adults with COVID-19 at increased risk of adverse outcomes (PANORAMIC): an open-label, platform-adaptive randomised controlled trial.

BACKGROUND: The safety, effectiveness, and cost-effectiveness of molnupiravir, an oral antiviral medication for SARS-CoV-2, has not been established in vaccinated patients in the community at increased risk of morbidity and mortality from COVID-19. We aimed to establish whether the addition of molnupiravir to usual care reduced hospital admissions and deaths associated with COVID-19 in this population. METHODS: PANORAMIC was a UK-based, national, multicentre, open-label, multigroup, prospective, platform adaptive randomised controlled trial. Eligible participants were aged 50 years or older-or aged 18 years or older with relevant comorbidities-and had been unwell with confirmed COVID-19 for 5 days or fewer in the community. Participants were randomly assigned (1:1) to receive 800 mg molnupiravir twice daily for 5 days plus usual care or usual care only. A secure, web-based system (Spinnaker) was used for randomisation, which was stratified by age (<50 years vs ≥50 years) and vaccination status (yes vs no). COVID-19 outcomes were tracked via a self-completed online daily diary for 28 days after randomisation. The primary outcome was all-cause hospitalisation or death within 28 days of randomisation, which was analysed using Bayesian models in all eligible participants who were randomly assigned. This trial is registered with ISRCTN, number 30448031. FINDINGS: Between Dec 8, 2021, and April 27, 2022, 26 411 participants were randomly assigned, 12 821 to molnupiravir plus usual care, 12 962 to usual care alone, and 628 to other treatment groups (which will be reported separately). 12 529 participants from the molnupiravir plus usual care group, and 12 525 from the usual care group were included in the primary analysis population. The mean age of the population was 56·6 years (SD 12·6), and 24 290 (94%) of 25 708 participants had had at least three doses of a SARS-CoV-2 vaccine. Hospitalisations or deaths were recorded in 105 (1%) of 12 529 participants in the molnupiravir plus usual care group versus 98 (1%) of 12 525 in the usual care group (adjusted odds ratio 1·06 [95% Bayesian credible interval 0·81-1·41]; probability of superiority 0·33). There was no evidence of treatment interaction between subgroups. Serious adverse events were recorded for 50 (0·4%) of 12 774 participants in the molnupiravir plus usual care group and for 45 (0·3%) of 12 934 in the usual care group. None of these events were judged to be related to molnupiravir. INTERPRETATION: Molnupiravir did not reduce the frequency of COVID-19-associated hospitalisations or death among high-risk vaccinated adults in the community. FUNDING: UK National Institute for Health and Care Research.

Design and logistical considerations for the randomized adaptive non-inferiority storage-duration-ranging CHIlled Platelet Study.

BACKGROUND: Platelet transfusion is a potentially life-saving therapy for actively bleeding patients, ranging from those undergoing planned surgical procedures to those suffering unexpected traumatic injuries. Platelets are currently stored at room temperature (20°C-24°C) with a maximum storage duration of 7 days after donation. The CHIlled Platelet Study trial will compare the efficacy and safety of standard room temperature-stored platelets with platelets that are cold-stored (1°C-6°C), that is, chilled, with a maximum of storage up to 21 days in adult and pediatric patients undergoing complex cardiac surgical procedures. METHODS/RESULTS: CHIlled Platelet Study will use a Bayesian adaptive design to identify the range of cold storage durations for platelets that are non-inferior to standard room temperature-stored platelets. If cold-stored platelets are non-inferior at durations greater than 7 days, a gated superiority analysis will identify durations for which cold-stored platelets may be superior to standard platelets. We present example simulations of the CHIlled Platelet Study design and discuss unique challenges in trial implementation. The CHIlled Platelet Study trial has been funded and will be implemented in approximately 20 clinical centers. Early randomization to enable procurement of cold-stored platelets with different storage durations will be required, as well as a platelet tracking system to eliminate platelet wastage and maximize trial efficiency and economy. DISCUSSION: The CHIlled Platelet Study trial will determine whether cold-stored platelets are non-inferior to platelets stored at room temperature, and if so, will determine the maximum duration (up to 21 days) of storage that maintains non-inferiority. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04834414.

The Bayesian Time Machine: Accounting for temporal drift in multi-arm platform trials.

BACKGROUND: Multi-arm platform trials investigate multiple agents simultaneously, typically with staggered entry and exit of experimental treatment arms versus a shared control arm. In such settings, there is considerable debate whether to limit analyses for a treatment arm to concurrent randomized control subjects or to allow comparisons to both concurrent and non-concurrent (pooled) control subjects. The potential bias from temporal drift over time is at the core of this debate. METHODS: We propose time-adjusted analyses, including a "Bayesian Time Machine," to model potential temporal drift in the entire study population, such that primary analyses can incorporate all randomized control subjects from the platform trial. We conduct a simulation study to assess performance relative to utilizing concurrent or pooled controls. RESULTS: In multi-arm platform trials with staggered entry, analyses adjusting for temporal drift (either Bayesian or frequentist) have superior estimation of treatment effects and favorable testing properties compared to analyses using either concurrent or pooled controls. The Bayesian Time Machine generally provides estimates with greater precision and smaller mean square error than alternative approaches, at the risk of small bias and small Type I error inflation. CONCLUSIONS: The Bayesian Time Machine provides a compromise between bias and precision by smoothing estimates across time and leveraging all available data for the estimation of treatment effects. Prior distributions controlling the behavior of dynamic smoothing across time must be pre-specified and carefully calibrated to the unique context of each trial, appropriately accounting for the population, disease, and endpoints.

An adaptive clinical trial design to identify the target dose of tenecteplase for treatment of acute pulmonary embolism.

BACKGROUND/AIMS: Fibrinolytic therapy with tenecteplase has been proposed for patients with pulmonary embolism but the optimal dose is unknown. Higher-than-necessary dosing is likely to cause excess bleeding. We designed an adaptive clinical trial to identify the minimum and assumed safest dose of tenecteplase that maintains efficacy. METHODS: We propose a Bayesian adaptive, placebo-controlled, group-sequential dose-finding trial using response-adaptive randomization to preferentially allocate subjects to the most promising doses, dual analyses strategies (continuous and dichotomized) using a gatekeeping approach to maximize clinical impact, and interim stopping rules to efficiently address competing trial objectives. The operating characteristics of the proposed design were evaluated using Monte Carlo simulation across multiple hypothetical efficacy scenarios. RESULTS: Simulation demonstrated response-adaptive randomization can preferentially allocate subjects to doses which appear to be performing well based on interim data. Interim decision-making, including the interim evaluation of both analysis strategies with gatekeeping, allows the trial to continue enrollment when success with the dichotomized analysis strategy appears sufficiently likely and to stop enrollment and declare superiority based on the continuous analysis strategy when there is little chance of ultimately declaring superiority with the dichotomized analysis. CONCLUSION: The proposed design allows evaluation of a greater number of dose levels than would be possible with a non-adaptive design and avoids the need to choose either the continuous or the dichotomized analysis strategy for the primary endpoint.

A clustering method for graphical handwriting components and statistical writership analysis.

Handwritten documents can be characterized by their content or by the shape of the written characters. We focus on the problem of comparing a person's handwriting to a document of unknown provenance using the shape of the writing, as is done in forensic applications. To do so, we first propose a method for processing scanned handwritten documents to decompose the writing into small graphical structures, often corresponding to letters. We then introduce a measure of distance between two such structures that is inspired by the graph edit distance, and a measure of center for a collection of the graphs. These measurements are the basis for an outlier tolerant K-means algorithm to cluster the graphs based on structural attributes, thus creating a template for sorting new documents. Finally, we present a Bayesian hierarchical model to capture the propensity of a writer for producing graphs that are assigned to certain clusters. We illustrate the methods using documents from the Computer Vision Lab dataset. We show results of the identification task under the cluster assignments and compare to the same modeling, but with a less flexible grouping method that is not tolerant of incidental strokes or outliers.

Development and validation of a prognostic 40-day mortality risk model among hospitalized patients with COVID-19.

OBJECTIVES: The development of a prognostic mortality risk model for hospitalized COVID-19 patients may facilitate patient treatment planning, comparisons of therapeutic strategies, and public health preparations. METHODS: We retrospectively reviewed the electronic health records of patients hospitalized within a 13-hospital New Jersey USA network between March 1, 2020 and April 22, 2020 with positive polymerase chain reaction results for SARS-CoV-2, with follow-up through May 29, 2020. With death or hospital discharge by day 40 as the primary endpoint, we used univariate followed by stepwise multivariate proportional hazard models to develop a risk score on one-half the data set, validated on the remainder, and converted the risk score into a patient-level predictive probability of 40-day mortality based on the combined dataset. RESULTS: The study population consisted of 3123 hospitalized COVID-19 patients; median age 63 years; 60% were men; 42% had >3 coexisting conditions. 713 (23%) patients died within 40 days of hospitalization for COVID-19. From 22 potential candidate factors 6 were found to be independent predictors of mortality and were included in the risk score model: age, respiratory rate ≥25/minute upon hospital presentation, oxygenation <94% on hospital presentation, and pre-hospital comorbidities of hypertension, coronary artery disease, or chronic renal disease. The risk score was highly prognostic of mortality in a training set and confirmatory set yielding in the combined dataset a hazard ratio of 1.80 (95% CI, 1.72, 1.87) for one unit increases. Using observed mortality within 20 equally sized bins of risk scores, a predictive model for an individual's 40-day risk of mortality was generated as -14.258 + 13.460*RS + 1.585*(RS-2.524)^2-0.403*(RS-2.524)^3. An online calculator of this 40-day COVID-19 mortality risk score is available at www.HackensackMeridianHealth.org/CovidRS. CONCLUSIONS: A risk score using six variables is able to prognosticate mortality within 40-days of hospitalization for COVID-19. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT04347993.

Hydroxychloroquine and tocilizumab therapy in COVID-19 patients-An observational study.

Hydroxychloroquine has been touted as a potential COVID-19 treatment. Tocilizumab, an inhibitor of IL-6, has also been proposed as a treatment of critically ill patients. In this retrospective observational cohort study drawn from electronic health records we sought to describe the association between mortality and hydroxychloroquine or tocilizumab therapy among hospitalized COVID-19 patients. Patients were hospitalized at a 13-hospital network spanning New Jersey USA between March 1, 2020 and April 22, 2020 with positive polymerase chain reaction results for SARS-CoV-2. Follow up was through May 5, 2020. Among 2512 hospitalized patients with COVID-19 there have been 547 deaths (22%), 1539 (61%) discharges and 426 (17%) remain hospitalized. 1914 (76%) received at least one dose of hydroxychloroquine and 1473 (59%) received hydroxychloroquine with azithromycin. After adjusting for imbalances via propensity modeling, compared to receiving neither drug, there were no significant differences in associated mortality for patients receiving any hydroxychloroquine during the hospitalization (HR, 0.99 [95% CI, 0.80-1.22]), hydroxychloroquine alone (HR, 1.02 [95% CI, 0.83-1.27]), or hydroxychloroquine with azithromycin (HR, 0.98 [95% CI, 0.75-1.28]). The 30-day unadjusted mortality for patients receiving hydroxychloroquine alone, azithromycin alone, the combination or neither drug was 25%, 20%, 18%, and 20%, respectively. Among 547 evaluable ICU patients, including 134 receiving tocilizumab in the ICU, an exploratory analysis found a trend towards an improved survival association with tocilizumab treatment (adjusted HR, 0.76 [95% CI, 0.57-1.00]), with 30 day unadjusted mortality with and without tocilizumab of 46% versus 56%. This observational cohort study suggests hydroxychloroquine, either alone or in combination with azithromycin, was not associated with a survival benefit among hospitalized COVID-19 patients. Tocilizumab demonstrated a trend association towards reduced mortality among ICU patients. Our findings are limited to hospitalized patients and must be interpreted with caution while awaiting results of randomized trials. Trial Registration: Clinicaltrials.gov Identifier: NCT04347993.