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Parents and physicians were eager for AP applications to be available for younger children, but stressed that a modified system could better serve this group's needs for safety and improved diabetes-related communication. The diverse and emerging needs of 5-8-year olds require flexible and customizable systems for T1D management.

Background Automated closed-loop control delivery of insulin, also known as the artificial pancreas, is emerging as a treatment option for type 1 diabetes, which is considered generally superior to sensor-augmented insulin pump therapy. Evening and overnight closed-loop control might account for most of the benefits of 24/7 continuous closedloop control; however, to our knowledge, no direct comparison of these systems has yet been done. In this study, we sought to compare different configurations of a closed-loop control system with activation of automated insulin delivery during the evening and overnight hours compared with continuous 24/7 closed-loop control, and both treatment modalities compared with sensor-augmented insulin pump use. MethodsIn this randomised crossover trial at the University of Virginia (Charlottesville, VA, USA), we randomly assigned (1:1) participants aged 18 to up to 70 years with a diagnosis of type 1 diabetes for at least 1 year to one of two sequences of four 8-week treatment sessions: sensor-augmented insulin pump therapy, evening and overnight closedloop control, continuous 24/7 closed-loop control, and evening and overnight closed-loop control (group A) or evening and overnight closed-loop control, continuous 24/7 closed-loop control, evening and overnight closed-loop control, and sensor-augmented pump therapy (group B). Randomisation was done by non-clinical study staff using a computer-generated sequence of Bernoulli trials. The primary outcome of the study was the difference in the percentage of time continuous glucose monitored glucose was less than 3•9 mmol/L (70 mg/dL) during sensoraugmented pump therapy compared with evening and overnight closed-loop control. Our overall analysis followed a modified intention-to-treat approach, with all available data for participants who had completed at least two study sessions included in the analysis, regardless of actual use of a closed-loop control device. The trial was registered with ClinicalTrials.gov, NCT02679287.

SummaryBackground: The Centers for Medicare and Medicaid Services' Readmissions Reduction Program adjusts payments to hospitals based on 30-day readmission rates for patients with acute myocardial infarction, heart failure, and pneumonia. This holds hospitals accountable for a complex phenomenon about which there is little evidence regarding effective interventions. Further study may benefit from a method for efficiently and inexpensively identifying patients at risk of readmission. Several models have been developed to assess this risk, many of which may not translate to a U.S. community hospital setting. Objective: To develop a real-time, automated tool to stratify risk of 30-day readmission at a semirural community hospital. Methods: A derivation cohort was created by extracting demographic and clinical variables from the data repository for adult discharges from calendar year 2010. Multivariate logistic regression identified variables that were significantly associated with 30-day hospital readmission. Those variables were incorporated into a formula to produce a Risk of Readmission Score (RRS). A validation cohort from 2011 assessed the predictive value of the RRS. A SQL stored procedure was created to calculate the RRS for any patient and publish its value, along with an estimate of readmission risk and other factors, to a secure intranet site. Results: Eleven variables were significantly associated with readmission in the multivariate analysis of each cohort. The RRS had an area under the receiver operating characteristic curve (c-statistic) of 0.74 (95% CI 0.73-0.75) in the derivation cohort and 0.70 (95% CI 0.69-0.71) in the validation cohort. Conclusion: Clinical and administrative data available in a typical community hospital database can be used to create a validated, predictive scoring system that automatically assigns a probability of 30-day readmission to hospitalized patients. This does not require manual data extraction or manipulation and uses commonly available systems. Additional study is needed to refine and confirm the findings.

Medication for Opioid Use Disorder (MOUD) is recommended, but not always accessible to those who desire treatment. This study assessed the impact of expanding access to buprenorphine through federally qualified health centers (FQHCs) in Arizona. We calculated mean drive-times to Arizona opioid treatment (OTP) locations, office-based opioid treatment (OBOT) locations, and FQHCs clinics using January 2020 location data. FQHCs were designated as OBOT or non-OBOT clinics to explore opportunities to expand treatment access to non-OBOT clinics (potential OBOTs) to further reduce drive-times for rural and underserved populations. We found that OTPs had the largest mean drive times (16.4 minutes), followed by OBOTs (7.1 minutes) and potential OBOTs (6.1 minutes). Drive times were shortest in urban block groups for all treatment types and the largest differences existed between OTPs and OBOTs (50.6 minutes) in small rural and in isolated rural areas. OBOTs are essential points of care for opioid use disorder treatment. They reduce drive times by over 50% across all urban and rural areas. Expanding buprenorphine through rural potential OBOT sites may further reduce drive times to treatment and address a critical need among underserved populations.