Digitizing clinical trials.

Clinical trials are a fundamental tool used to evaluate the efficacy and safety of new drugs and medical devices and other health system interventions. The traditional clinical trials system acts as a quality funnel for the development and implementation of new drugs, devices and health system interventions. The concept of a "digital clinical trial" involves leveraging digital technology to improve participant access, engagement, trial-related measurements, and/or interventions, enable concealed randomized intervention allocation, and has the potential to transform clinical trials and to lower their cost. In April 2019, the US National Institutes of Health (NIH) and the National Science Foundation (NSF) held a workshop bringing together experts in clinical trials, digital technology, and digital analytics to discuss strategies to implement the use of digital technologies in clinical trials while considering potential challenges. This position paper builds on this workshop to describe the current state of the art for digital clinical trials including (1) defining and outlining the composition and elements of digital trials; (2) describing recruitment and retention using digital technology; (3) outlining data collection elements including mobile health, wearable technologies, application programming interfaces (APIs), digital transmission of data, and consideration of regulatory oversight and guidance for data security, privacy, and remotely provided informed consent; (4) elucidating digital analytics and data science approaches leveraging artificial intelligence and machine learning algorithms; and (5) setting future priorities and strategies that should be addressed to successfully harness digital methods and the myriad benefits of such technologies for clinical research.

Continuous daily assessment of multiple sclerosis disability using remote step count monitoring.

Disability measures in multiple sclerosis (MS) rely heavily on ambulatory function, and current metrics fail to capture potentially important variability in walking behavior. We sought to determine whether remote step count monitoring using a consumer-friendly accelerometer (Fitbit Flex) can enhance MS disability assessment. 99 adults with relapsing or progressive MS able to walk ≥2-min were prospectively recruited. At 4 weeks, study retention was 97% and median Fitbit use was 97% of days. Substudy validation resulted in high interclass correlations between Fitbit, ActiGraph and manual step count tally during a 2-minute walk test, and between Fitbit and ActiGraph (ICC = 0.76) during 7-day home monitoring. Over 4 weeks of continuous monitoring, daily steps were lower in progressive versus relapsing MS (mean difference 2546 steps, p < 0.01). Lower average daily step count was associated with greater disability on the Expanded Disability Status Scale (EDSS) (p < 0.001). Within each EDSS category, substantial variability in step count was apparent (i.e., EDSS = 6.0 range 1097-7152). Step count demonstrated moderate-strong correlations with other walking measures. Lower average daily step count is associated with greater MS disability and captures important variability in real-world walking activity otherwise masked by standard disability scales, including the EDSS. These results support remote step count monitoring as an exploratory outcome in MS trials.

Incidence and clinical implications of isolation of Mycobacterium kansasii: results of a 5-year, population-based study.

BACKGROUND: Mycobacterium kansasii, an unusual pathogen in the pre-AIDS era, is increasingly reported to cause infection among patients with HIV infection. Little is known about the epidemiology and clinical implications of M. kansasii infection in the AIDS era. OBJECTIVE: To compare the incidence, demographic characteristics, and clinical features of M. kansasii infection in HIV-positive and HIV-negative persons. DESIGN: Population-based laboratory surveillance. SETTING: Three counties in northern California. PATIENTS: All persons who had a positive culture for M. kansasii between 1 January 1992 and 31 December 1996. MEASUREMENTS: Cumulative incidence rates were calculated for each year by dividing the number of adult patients by the annual estimated adult population. Demographic and socioeconomic data for a single county were obtained by linkage with the 1990 U.S. Census report. RESULTS: 270 patients (69.3% of whom were HIV positive) were identified, for an incidence of 2.4 cases per 100,000 adults per year (95% CI, 2.1 to 2.7), 115 cases per 100,000 HIV-positive persons per year (CI, 99 to 133), and 647 cases per 100,000 persons with AIDS per year (CI, 554 to 751). Indicators of lower socioeconomic status were common among patients: Median incomes were $32,317 in census tracts in which cases were identified and $38,048 in census tracts without cases (P = 0.001), and 35.7% of patients had unstable housing situations. Ninety-four percent of cases were from respiratory isolates, and 87.5% of patients had evidence of infection. Persons with HIV infection differed from those without HIV infection with respect to mycobacteremia (9.6% compared with 0%; P = 0.001), need for hospitalization (77.4% compared with 51.9%; P < 0.001), and smear positivity (41.7% compared with 20.7%; P = 0.005). Chronic diseases were common among HIV-negative persons; however, 40.3% had no predisposing medical condition. CONCLUSIONS: Mycobacterium kansasii isolation is more common in HIV-positive persons, but most patients with M. kansasii infection have clinical and radiologic evidence of infection regardless of HIV status. Persons infected with HIV and M. kansasii have a higher rate of hospitalization and a greater burden of organisms. A possible association with poverty suggests mechanisms of transmission and requires further study.