Studies of closed-loop control (CLC) in patients with type 1 diabetes (T1D) consistently demonstrate improvements in glycemic control as measured by increased time-in-range (TIR) 70-180 mg/dL. However, clinical predictors of TIR in users of CLC systems are needed. We analyzed data from 100 children aged 6-13 years with T1D using the Tandem Control-IQ CLC system during a randomized trial or subsequent extension phase. Continuous glucose monitor data were collected at baseline and during 12-16 weeks of CLC use. Participants were stratified into quartiles of TIR on CLC to compare clinical characteristics. TIR for those in the first, second, third, and fourth quartiles was 54%, 65%, 71%, and 78%, respectively. Lower baseline TIR was associated with lower TIR on CLC ( = 0.69, < 0.001). However, lower baseline TIR was also associated with greater improvement in TIR on CLC ( = -0.81, < 0.001). During CLC, participants in the highest versus lowest TIR-quartile administered more user-initiated boluses daily (8.5 ± 2.8 vs. 5.8 ± 2.6, < 0.001) and received fewer automated boluses (3.5 ± 1.0 vs. 6.0 ± 1.6, < 0.001). Participants in the lowest (vs. the highest) TIR-quartile received more insulin per body weight (1.13 ± 0.27 vs. 0.87 ± 0.20 U/kg/d, = 0.008). However, in a multivariate model adjusting for baseline TIR, user-initiated boluses and insulin-per-body-weight were no longer significant. Higher baseline TIR is the strongest predictor of TIR on CLC in children with T1D. However, lower baseline TIR is associated with the greatest improvement in TIR. As with open-loop systems, user engagement is important for optimal glycemic control.
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http://dx.doi.org/10.1089/dia.2020.0646 | DOI Listing |
PLoS One
January 2025
Institute of Visual Informatics, The National University of Malaysia (UKM), Bangi, Malaysia.
Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enable adaptive insulin dosing control, they face numerous challenges, including safety and training efficiency, which have hindered their clinical application. This paper proposes a safe and efficient adaptive insulin delivery controller based on DRL.
View Article and Find Full Text PDFDiabetes Technol Ther
January 2025
Department of Paediatrics, University of Otago, Christchurch, New Zealand.
This study evaluated a next-generation automated insulin delivery (AID) algorithm for Omnipod in type 1 and type 2 diabetes across multiple phases: 14-day run-in with usual therapy, 48-h AID use in a hotel setting (type 1 only), and up to 6 weeks of outpatient AID use. Participants did, or did not, deliver manual boluses at alternating periods. Twelve adults with type 1 diabetes completed the hotel phase; 9 of those 12 plus 8 adults with type 2 diabetes completed the subsequent outpatient phase.
View Article and Find Full Text PDFArtif Organs
January 2025
Division of Life Science and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei, China.
Background: Membrane oxygenators facilitate extracorporeal gas exchange, necessitating the monitoring of blood gas. Recent advances in normothermic machine perfusion (NMP) for ex vivo liver offer solutions to the shortage of donor liver. However, maintaining physiological blood gas levels during prolonged NMP is complex and costly.
View Article and Find Full Text PDFACS Cent Sci
January 2025
Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States.
Photothermal conversion can promote plastic depolymerization (chemical recycling to a monomer) through light-to-heat conversion. The highly localized temperature gradient near the photothermal agent surface allows selective heating with spatial control not observed with bulk pyrolysis. However, identifying and incorporating practical photothermal agents into plastics for end-of-life depolymerization have not been realized.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
This paper proposes a hierarchical framework-based solution to address the challenges of vehicle state estimation and lateral stability control in four-wheel independent drive electric vehicles. First, based on a three-degrees-of-freedom four-wheel vehicle model combined with the Magic Formula Tire model (MF-T), a hierarchical estimation method is designed. The upper layer employs the Kalman Filter (KF) and Extended Kalman Filter (EKF) to estimate the vertical load of the wheels, while the lower layer utilizes EKF in conjunction with the upper-layer results to further estimate the lateral forces, longitudinal velocity, and lateral velocity, achieving accurate vehicle state estimation.
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