Aim: To test whether a screening approach with more flexible urinary albumin creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) thresholds would decrease screen failure rate without negatively impacting on the event rate and overall study duration. Methods: We performed a post-hoc analysis of the ALTITUDE trial. We selected participants randomized to placebo with a UACR of >300 mg/g and an eGFR between 30 mL/min/1.73 m2 and 60 mL/min/1.73 m2 at the first visit (pre-screening) for the trial. We then used less stringent lower UACR and higher eGFR thresholds for the following qualifying visit. For each scenario we calculated the number of eligible participants, the number of renal and cardiovascular endpoints, and the event rates. Based on this, we performed simulations for a future trial and estimated the duration of enrolment and total duration of this trial. Results: The base scenario consisted of 848 participants (median UACR 1239 mg/g; median eGFR 44 mL/min/1.73 m2). Lowering the UACR and/or raising eGFR qualification thresholds increased the number of eligible participants, decreased screen failures and resulted in only a modest decrease in renal and cardiovascular event rates. For example, relaxing the UACR criterion from 300 mg/g to 210 mg/g at the qualifying visit, increased the number of eligible patients from 848 to 923, and increased the number of renal events from 117 to 122 events. The event rate showed a moderate decrease from 5.6 (4.6-6.7) events per 100 patient-years to 5.3 (4.4-6.4) events per 100 patient-years. In simulations, lowering the UACR and raising eGFR thresholds for inclusion accelerated patient enrolment and did not increase in the overall trial duration. Conclusion: More flexible albuminuria and eGFR-based inclusion criteria, in participants who met the inclusion criteria of a trial based on pre-screening values prior to the clinical trial, decreases screen failure rates and accelerated patient enrolment leading to more efficient trial conduct without impacting the overall trial duration. ... Read more

A digital twin (DT), originally defined as a virtual representation of a physical asset, system, or process, is a new concept in health care. A DT in health care is not a single technology but a domain-adapted multimodal modeling approach incorporating the acquisition, management, analysis, prediction, and interpretation of data, aiming to improve medical decision-making. However, there are many challenges and barriers that must be overcome before a DT can be used in health care. In this viewpoint paper, we build on the current literature, address these challenges, and describe a dynamic DT in health care for optimizing individual patient health care journeys, specifically for women at risk for cardiovascular complications in the preconception and pregnancy periods and across the life course. We describe how we can commit multiple domains to developing this DT. With our cross-domain definition of the DT, we aim to define future goals, trade-offs, and methods that will guide the development of the dynamic DT and implementation strategies in health care. ... Read more