Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach that directly accounts for intra-and interpatient variability with a stochastically derived maximum 5% risk of blood glucose (BG) below 72 mg/dl. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in virtual and clinical pilot trials. Methods: Clinically validated virtual trials using data from 370 patients in the SPRINT (Specialized Relative Insulin and Nutrition Titration) study were used to design the STAR protocol and test its safety, performance, and required clinical effort prior to clinical pilot trials. Insulin and nutrition interventions were given every 1-3 h as chosen by the nurse to allow them to manage workload. Interventions were designed to maximize the overlap of the model-predicted (5-95 th percentile) range of BG outcomes with the 72-117 mg/dl band and thus provide a maximum 5% risk of BG <72 mg/dl. Interventions were calculated using clinically validated computer models of human metabolism and its variability in critical illness. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of the American College of Chest Physicians/Society of Critical Care Medicine (ACCP/SCCM) goal (25 kg/kcal/h). Insulin doses were limited (8 U/h maximum), with limited increases based on current rate (0.5-2.0 U/h). Initial clinical pilot trials involved 3 patients covering ~450 h. Approval was granted by the Upper South A Regional Ethics Committee.

IntroductionTight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly accounting for intra- and inter- patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) < 4.0 mmol/L. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in pilot trials.MethodsSeven patients covering 660 hours. Insulin and nutrition interventions are given 1-3 hourly as chosen by the nurse to allow them to manage workload. Interventions are calculated by using clinically validated computer models of human metabolism and its variability in critical illness to maximize the overlap of the model-predicted (5-95th percentile) range of BG outcomes with the 4.0-6.5 mmol/L band while ensuring a maximum 5% risk of BG < 4.0 mmol/L. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of SCCM/ACCP goal (25 kg/kcal/h). Maximum insulin doses and dose changes were limited for safety. Measurements were made with glucometers. Results are compared to those for the SPRINT study, which reduced mortality 25-40% for length of stay ≥3 days. Written informed consent was obtained for all patients, and approval was granted by the NZ Upper South A Regional Ethics Committee.ResultsA total of 402 measurements were taken over 660 hours (~14/day), because nurses showed a preference for 2-hourly measurements. Median [interquartile range, (IQR)] cohort BG was 5.9 mmol/L [5.2-6.8]. Overall, 63.2%, 75.9%, and 89.8% of measurements were in the 4.0-6.5, 4.0-7.0, and 4.0-8.0 mmol/L bands. There were no hypoglycemic events (BG < 2.2 mmol/L), and the minimum BG was 3.5 mmol/L with 4.5% < 4.4 mmol/L. Per patient, the median [IQR] hours of TGC was 92 h [29-113] using 53 [19-62] measurements (median, ~13/day). Median [IQR] results: BG, 5.9 mmol/L [5.8-6.3]; carbohydrate nutrition, 6.8 g/h [5.5-8.7] (~70% goal feed median); insulin, 2.5 U/h [0.1-5.1]. All patients achieved BG < 6.1 mmol/L. These results match or exceed SPRINT and clinical workload is reduced more than 20%.ConclusionsSTAR TGC modulating insulin and nutrition inputs provided very tight control with minimal variability by managing intra- and inter- patient variability. Performance and safety exceed that of SPRINT, which reduced mortality and cost in the Christchurch ICU. The use of glucometers did not appear to impact the quality of TGC. Finally, clinical workload was self-managed and reduced 20% compared with SPRINT.

I. Two experiments were conducted to compare the effect on nitrogen retention of three dietary levels of N given either as groundnut or as urea to growing heifers. Additions of maize starch or dextrose wcrc made to the diets to equalize the inputs of digestible energy within and between experiments.2. In the first experiment, in which the maximum level of N supplementation was 69.0 g/d, the response to additional N was linear, and was identical for both sources of N. Differences in faecal N between treatments were small; differences in urinary N were large and were entirely attributable to level of N intake.3. In the second experiment, the maximum Ievel of N supplementation was raised to 103.3 g/d. The response to additional N was again linear and identical for both sources of N ; however, for a given level of N input, thc amount of N retained was 4 6 g less than in Expt I. This reduction in N retention may have been due to the changc in the proportion of digestible energy derived from the fibrous components of the ration. There is still much doubt about the response to be expected when non-protein nitrogen is substituted €or protein in diets for ruminants. In comparison with conventional proteins, the efficiency of utilization of non-protein N has varied greatly between experiments (see review by Reid, 1953). In some instances, and especially with experimental diets rich in starch, the utilization of urea N appears to have been as high as the utilization of protein N (Loosli & McDonald, 1968). With many diets, more typical of those used on farms, the utilization of urea N appears to have been somewhat lower. Thus, the problem which remains is that of predicting the efficiency of utilization of non-protein N under particular circumstances.T o predict responses to particular inputs of N, consideration must be given to the interaction between inputs of both energy and N (Broster, Tuck & Balch, 1963;Elliott, Reed & Topps, 1964). The relationship between energy supply and protein requirement is complicated by the fact that, in addition to acting as a source of amino acids, protein can be utilized as a source of energy. If increasing amounts of protein are added to diets in which energy content is fixed and limiting to growth, the rate of gain and the N balance of growing animals receiving the diets continue to increase. The rate of gain is, however, lower than with diets in which energy is not limiting (Balch, 1967). There is little direct information about whether the rcsponse to increments of non-protein N declines in the same way as the response to protein when energy becomes limiting or whether thc rcsponse is greater or less than that to protein. The present work was undertaken to calibrate and compare the responses to additional N as protein and as urea when energy is limiting and fixed. With a small number of https://www.cambridge.org/core/terms. https://doi

The interface is designed to minimize real and perceived clinical effort, and ongoing pilot trials have reported high levels of acceptance. The overall design principles, approach, and testing methods are based on fundamental human factors principles designed to reduce user effort and error and are readily generalizable.

The data entry method selected significantly reduced data entry errors in the limited design tests presented, and is in use on a clinical pilot TGC study. The overall approach and testing methods are easily performed and generalizable to other applications and protocols.