Ethics committees in the time of COVID-19.

BACKGROUND: ethics committees (ECs) protect the rights, safety, and well-being of research participants and ensure the scientific correctness of clinical research. COVID-19 pandemic and the lockdown from 9 March to 16 May 2020 have potentially influenced several activities, including ECs. OBJECTIVES: to assess the impact of COVID-19 outbreak on Italian ECs and their performance during the lockdown. DESIGN: cross-sectional survey. SETTING AND PARTICIPANTS: the survey was conducted in mid-June 2020 in Italy contacting all the 90 local ECs. MAIN OUTCOME MEASURES: amount and kind of activities performed during the lockdown, characteristics of submitted studies and adoption of standard protocols of evaluation of research applications during the pandemic. Chi-square test was used to estimate the differences between territories with higher incidence (HI) and lower incidence (LI) of COVID-19. RESULTS: 258 questionnaires were collected from 46 ECs that participated in the study. Ten were excluded due to missing substantial data. Responses were divided into two groups according to location of EC: the HI (125 responses) and the LI (123 responses). Seventy-five percent of the HI describe an increase in the number of studies submitted, while 53% of the LI does not (p=0.001). Due to the pandemic and its effects on research, the 15% of participants belonging to HI territories reported that consideration and respect of research-related and general ethical principles could have decreased, as well the adoption of standard protocols of evaluation of research applications. EC secretariats located in HI Regions moved to smart working more than in LI ones (75% vs 59%; p=0.001). Where the EC workload increased significantly, it was reported that it was impossible to perform an accurate analysis of the submitted documentation, with the effect of providing a favorable opinion to studies of not excellent quality, though always ensuring the respect of ethical principles and patients' safety. CONCLUSIONS: COVID-19 impact on ECs has been heavier in HI territories, but smart working has been effective in ensuring EC activities and the subsequent activation of clinical studies potentially useful to face the pandemic. Clear differences arise between ECs belonging to the Italian Regions that have recorded a HI of COVID-19 cases compared to those located in Regions with a LI of cases. In some EC members' perception, the high number of studies in the most affected Regions together with the emergency experienced during the lockdown may have exposed ECs to the risk of decreasing the adoption of ethical principles and standard protocols of evaluation of research applications.

Monitoring Patients Reported Outcomes after Valve Replacement Using Wearable Devices: Insights on Feasibility and Capability Study: Feasibility Results.

Wearable devices (WDs) can objectively assess patient-reported outcomes (PROMs) in clinical trials. In this study, the feasibility and acceptability of using commercial WDs in elderly patients undergoing transcatheter aortic valve replacement (TAVR) or surgical aortic valve replacement (SAVR) will be explored. This is a prospective observational study. Participants were trained to use a WD and a smartphone to collect data on their physical activity, rest heart rate and number of hours of sleep. Validated questionnaires were also used to evaluate these outcomes. A technology acceptance questionnaire was used at the end of the follow up. In our participants an overall good compliance in wearing the device (75.1% vs. 79.8%, SAVR vs. TAVR) was assessed. Half of the patients were willing to continue using the device. Perceived ease of use is one of the domains that scored higher in the technology acceptance questionnaire. In this study we observed that the use of a WD is accepted in our frail population for an extended period. Even though commercial WDs are not tailored for clinical research, they can produce useful information on patient behavior, especially when coordinated with intervention tailored to the single patient.

Using Social Networks to Estimate the Number of COVID-19 Cases: The Incident (Hidden COVID-19 Cases Network Estimation) Study Protocol.

Recent literature has reported a high percentage of asymptomatic or paucisymptomatic cases in subjects with COVID-19 infection. This proportion can be difficult to quantify; therefore, it constitutes a hidden population. This study aims to develop a proof-of-concept method for estimating the number of undocumented infections of COVID-19. This is the protocol for the INCIDENT (Hidden COVID-19 Cases Network Estimation) study, an online, cross-sectional survey with snowball sampling based on the network scale-up method (NSUM). The original personal network size estimation method was based on a fixed-effects maximum likelihood estimator. We propose an extension of previous Bayesian estimation methods to estimate the unknown network size using the Markov chain Monte Carlo algorithm. On 6 May 2020, 1963 questionnaires were collected, 1703 were completed except for the random questions, and 1652 were completed in all three sections. The algorithm was initialized at the first iteration and applied to the whole dataset. Knowing the number of asymptomatic COVID-19 cases is extremely important for reducing the spread of the virus. Our approach reduces the number of questions posed. This allows us to speed up the completion of the questionnaire with a subsequent reduction in the nonresponse rate.