Establishing Optimal Control Cohorts for Phase 1 Trials: Retrospective Analysis of Clinical and Biological Outcomes in Neonates and Infants Undergoing Two-Ventricle Repair.

Phase 1 trials are primarily conducted to evaluate the safety and feasibility of new interventions, usually without recruiting control patients. This retrospective study aims to characterize clinical and biological outcomes in historical and contemporary cases of neonates and infants undergoing two-ventricle repair to facilitate future secondary endpoint analyses for such trials. This retrospective study included neonates/infants (ages ≤ 6 months) who underwent two-ventricle repair between 2015 and 2021 using the same criteria as our phase 1 trial (n = 199). Patients were allocated into the ventricular septal defect (n = 61), the Tetralogy of Fallot (TOF, n = 88), and the transposition of the great arteries (n = 50) groups with an additional comparison between two eras (2015-2019 vs. 2020-2021). Patient characteristics and most variables assessed were different between the three diagnostic groups indicating the importance of diagnostic matching for secondary analyses. Although the era did not alter cerebral/somatic oxygenation, ventricular function, neuroimaging findings, and complication rates, we observed improvement of inotropic and/or vasoactive-inotropic scores in all groups during the more recent era. In 2020-2021, the age and the body weight at the operation were higher, and hospital stay was shorter in the TOF group, suggesting the possible impact of the pandemic. Results also indicated that matching altered characteristics such as age at operation that may limit the temporal effects and optimize secondary analyses. Using optimal contemporary cases and historical data based on this study will assist in developing a comprehensive study design for a future efficacy/effectiveness trial.

Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monitor: A Pilot Study.

BACKGROUND: Self-monitoring of glucose is important to the successful management of diabetes; however, existing monitoring methods require a degree of invasive measurement which can be unpleasant for users. This study investigates the accuracy of a noninvasive glucose monitoring system that analyses spectral variations in microwave signals. METHODS: An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values. RESULTS: Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones. CONCLUSIONS: These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.