Follow-up of SARS-CoV-2 positive subgroup from the Asymptomatic novel CORonavirus iNFection study.

A nested longitudinal study within theAsymptomatic novel CORonavirus iNFfection study followed participants with positive nasopharyngeal swab to query for development of symptoms and assess duration of positive reverse transcription-polymerase chain reaction (RT-PCR) test results. Of the 91 participants initially testing positive, 86 participated in follow-up approximately 14 days after study enrollment; of those 86 participants, 19 (22.1%) developed at least one symptom at any time after the initial positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test result. The median number of days to symptom development after their initial positive test result was 6 (range 1-29 days). No participants reported a SARS-CoV-2-related hospitalization. The most frequently reported symptoms were fatigue or muscle aches (10.5%), headache (9.3%), fever (5.8%), and shortness of breath (5.8%). Of the 78 participants who submitted a nasopharyngeal swab for repeat RT-PCR testing, 17 (21.8%) remained positive at Day 14, 4 of which continued to test positive at Day 28. These findings reinforce the probable role of silent SARS-CoV-2 infections in community transmission, and that reliance on symptom development will miss a large proportion of infections. Broad testing programs not limited to individuals presenting with symptoms are critical for identifying persons with SARS-CoV-2 infection and ultimately slowing transmission.

A cross-sectional community-based observational study of asymptomatic SARS-CoV-2 prevalence in the greater Indianapolis area.

The Asymptomatic novel CORonavirus iNfection (ACORN) study was designed to investigate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the asymptomatic adult population of the Indianapolis metropolitan area, to follow individuals testing positive for the development of symptoms, and to understand duration of positive test results. ACORN is a cross-sectional community-based observational study of adult residents presenting asymptomatic for COVID-like illness, defined as the self-reported absence of the following three symptoms in the last 7 days: fever (≥100°F), new-onset or worsening cough, and new-onset or worsening shortness of breath. SARS-CoV-2 infection was determined by real-time reverse transcription-polymerase chain reaction in nasopharyngeal swab samples. SARS-CoV-2 infection prevalence was expressed as a point estimate with 95% confidence interval (CI). Test results are reported for 2953 participants who enrolled and underwent nasopharyngeal swab testing between 7 April 2020 and 16 May 2020. Among tested participants, 91 (3.1%; 95% CI: 2.5%-3.7%) were positive for SARS-CoV-2. Overall, baseline characteristics, medical history, and infection risk factors were comparable between SARS-CoV-2 positive and negative participants. Within the ongoing 14-day follow-up period for positive participants, 58 (71.6%) of 81 assessed participants remained asymptomatic while others (n = 23, 28.4%) reported one or more symptoms. Indiana had "Stay-at-Home" orders in place during nearly the entire test period reported here, yet 3.1% of asymptomatic participants tested positive for SARS-CoV-2. These results indicate screening questions had limited predictive utility for testing in an asymptomatic population and suggest broader testing strategies are needed. Importantly, these findings underscore that more research is needed to understand the viral transmission and the role asymptomatic and presymptomatic individuals play in this global pandemic.

Restricted mean survival time for the analysis of cardiovascular outcome trials assessing non-inferiority: Case studies from antihyperglycemic drug development.

Cardiovascular outcome trials (CVOTs) have been employed in multiple therapeutic areas to explore whether a noncardiovascular drug increases the risk for cardiovascular events. These studies are now a central part of drug development programs for antihyperglycemic drugs. These programs are expected to demonstrate that new antihyperglycemic drugs for patients with Type 2 diabetes do not have unacceptable cardiovascular risk. The hazard ratio, which is usually provided as evidence that patients receiving the investigational treatment are not at statistically significantly greater cardiovascular risk than patients on the control treatment, can be difficult to interpret for various reasons. Therefore, an alternative approach known as the Restricted Mean Survival Time (RMST) or τ-year mean survival time is presented, and its ability to overcome interpretation challenges with the hazard ratio discussed. The RMST approach is applied to five completed CVOTs and is compared with the corresponding hazard ratios. Additionally, detailed considerations are given on how to design a non-inferiority CVOT using the RMST approach. The RMST methodology is shown to be a practical alternative to the hazard ratio methodology for designing a non-inferiority CVOT.

Perspectives on Some Controversies in Cardiovascular Disease Risk Assessment in the Pharmaceutical Development of Glucose-Lowering Medications.

The U.S. Food and Drug Administration (FDA) issued guidance on requirements to assess cardiovascular disease (CVD) risk with drugs being developed for approval for clinical use. The guidance was triggered by a meta-analysis published by Nissen and Wolski that suggested an increased risk for myocardial infarction with the use of rosiglitazone. This article discusses controversies around CVD trials in diabetes beginning with the University Group Diabetes Program. This is followed by a brief description of the FDA guidance for evaluating CVD risk with glucose-lowering medications. Limitations of meta-analyses of data from phase 2 and 3 (phase 2/3) trials to inform CVD risk are highlighted. These include the differences between patient characteristics in phase 2/3 trials and those in cardiovascular outcome trials (CVOTs) and the relatively short exposure time in phase 2/3 trials. The differences may partly explain the observed disparity between phase 2/3 meta-analyses and the results of completed CVOTs. Approaches to understanding CVD risk with a new medication should get to the answer about risk as efficiently as possible to minimize any potential harm to patients. In that context, we discuss options for clinical trial design and an alternative approach for statistical analyses.

A clinical utility index for selecting an optimal insulin dosing algorithm for LY2605541 in patients with type 2 diabetes pretreated with basal insulin.

BACKGROUND: Because insulin dosing requires optimization of glycemic control, it is important to use a single metric of benefit and risk to determine best insulin dosing practices. We retrospectively applied a multiplicative clinical utility index (CUI) to a study of LY2605541 (Eli Lilly and Company, Indianapolis, IN), a novel, long-acting basal insulin. MATERIALS AND METHODS: A CUI was developed to transform the multidimensional problem of assessing benefit/risk of multiple dosing algorithms into a single decision-making metric to evaluate two LY2605541 dosing algorithms relative to the insulin glargine (GL) dosing algorithm. The CUI was applied to data in a 12-week, open-label, Phase 2 trial in patients with type 2 diabetes mellitus who were randomized to one of two dosing algorithms for LY2605541 (LY1 or LY2) or GL (algorithm similar to LY1). The CUI was created (via expert input) by weighing the relative benefit/risk of four components (glycosylated hemoglobin [HbA1c], percentage of patients with HbA1c ≤ 7%, hypoglycemia rate, and time to steady-state dose); individual utility values were multiplied to compute CUI values for LY1 and LY2 relative to GL, and bootstrap samples were used to determine variability. RESULTS: The mean CUI was 0.82 for LY1 and 1.35 for LY2. Based on 3,000 bootstrap samples, there was a 48% probability of LY2 performing better than LY1 and a 28% probability of LY1 performing better than LY2. CONCLUSIONS: CUI methodology, and in particular this CUI, is a useful tool for comparing dosing algorithms. Based on this CUI, LY2 is likely to be a better dosing algorithm than LY1.