Development of an External Control Arm Using Electronic Health Record-Based Real-World Data to Evaluate the Efficacy of COVID-19 Treatment.

To protect people from severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection, tremendous research efforts have been made toward coronavirus disease 19 (COVID-19) treatment development. Externally controlled trials (ECTs) may help reduce their development time. To evaluate whether ECT using real-world data (RWD) of patients with COVID-19 is feasible enough to be used for regulatory decision making, we built an external control arm (ECA) based on RWD as a control arm of a previously conducted randomized controlled trial (RCT), and compared it to the control arm of the RCT. The electronic health record (EHR)-based COVID-19 cohort dataset was used as RWD, and three Adaptive COVID-19 Treatment Trial (ACTT) datasets were used as RCTs. Among the RWD datasets, eligible patients were evaluated as a pool of external control subjects of the ACTT-1, ACTT-2, and ACTT-3 trials, respectively. The ECAs were built using propensity score matching, and the balance of age, sex, and baseline clinical status ordinal scale as covariates between the treatment arms of Asian patients in each ACTT and the pools of external control subjects was assessed before and after 1:1 matching. There was no statistically significant difference in time to recovery between ECAs and the control arms of each ACTT. Among the covariates, the baseline status ordinal score had the greatest influence on the building of ECA. This study demonstrates that ECA based on EHR data of COVID-19 patients could sufficiently replace the control arm of an RCT, and it is expected to help develop new treatments faster in emergency situations, such as the COVID-19 pandemic.

Influenza viral infection is a risk factor for severe illness in COVID-19 patients: a nationwide population-based cohort study.

In order to prepare for the twindemic of influenza and SARS-CoV-2 infection, we investigated the association between influenza infection and subsequent severity of SARS-CoV-2 infection. A population-based nationwide cohort study was performed using data from the National Health Insurance Service (NHIS) in the Republic of Korea. This study included 274,126 individuals who underwent SARS-CoV-2 PCR testing between 20 January 2020 and 1 October 2020. Among these patients, 28,338 tested positive for SARS-CoV-2, and 4,003 of these individuals had a history of influenza. The control group was selected through 1:1 propensity score matching. In the group of 4,003 COVID-19-positive individuals with no history of influenza, 192 (4.8%) experienced severe illness from COVID-19 infection. In the group of 4,003 COVID-19-positive individuals with a history of influenza, 260 (6.5%) had severe illness from COVID-19, and the overall adjusted odds ratio (aOR) was 1.29 (95% confidence interval 1.04-1.59). Among the 4,003 COVID-19-positive individuals with a history of influenza, severe COVID-19 infection was experienced by 143 of 1,760 (8.1%) with an influenza history within 1 year before the onset of COVID-19, 48 of 1,129 (4.3%) between 1 and 2 years, and 69 of 1,114 (6.2%) between 2 and 3 years before COVID-19 onset, and the aORs were 1.54 (1.20-1.98), 1.19 (0.84-1.70), and 1.00 (0.73-1.37), respectively. In conclusion, individuals who had an influenza infection less than 1 year before COVID-19 infection were at an increased risk of experiencing severe illness from the SARS-CoV-2 infection. To control the public health burden, it is essential that effective public health control measures, which include influenza vaccination, hand washing, cough etiquette, and mask use are in place.

Evaluation of a blockchain-based dynamic consent platform (METORY) in a decentralized and multicenter clinical trial using virtual drugs.

Blockchain is a novel data architecture characterized by a chronological sequence of blocks in a decentralized manner. We aimed to evaluate the real-world feasibility of a blockchain-based dynamic consent platform (METORY) in a decentralized and multicenter trial. The study consisted of three visits (i.e., screening and 2 follow-up visits) with a 2-week interval. Each subject was required to report the self-measured body temperatures and take a virtual investigational drug by entering the unique drug code on the application. To simulate real-world study settings, two major (i.e., changes in the schedule of body temperature measurement) and three minor protocol amendments (i.e., nonsignificant changes without any changes in the procedures) were set. Overall study completion rates, proportion of consent, and response time to each protocol amendment and adherence were evaluated. A total of 60 subjects (30 in each center) were enrolled in two study centers. All subjects completed the study, and the overall proportion of consent to each protocol amendment was 95.7 ± 13.7% (mean ± SD), with a median response time of 0.2 h. Overall, subjects took 90.8% ± 19.2% of the total drug, whereas compliance with the schedule was 69.1% ± 27.0%. Subjects reported 96.7% ± 4.2% of the total body temperature measurements whereas the adherence to the schedule was 59.0% ± 25.0%, which remarkably decreased after major protocol amendments. In conclusion, we evaluated a blockchain-based dynamic consent platform in real clinical trial settings. The results suggested that major changes should be avoided unless subjects' proper understanding is warranted.