The choice of the insulin basal rate regimen at initiation of insulin pump therapy in routine clinical practice

Background: Numbers of patients with diabetes mellitus using insulin pumps have been increasing every year. Successful achievement of glycemic targets with continuous subcutaneous insulin infusion (CSII) is based on an adequate basal rate of infusion, carbohydrate coefficient and insulin sensitivity index. There are two approaches to basal insulin infusion rate, namely the flat one and the circadian; however, at present there is no convincing data on which one should be chosen at the start of insulin pump therapy.Aim: To compare two regimens of basal insulin infusion rate at initiation of insulin pump therapy in routine clinical practice.Materials and methods: We analyzed data from 120 patients with Type 1 diabetes mellitus, who were switched on insulin pump therapy in the Department of Endocrinology from 2017 to 2018. At initiation of CSII, 60 patients used the flat basal rate profile and the other 60 patients used the circadian basal rate, calculated with the Renner's scale. Safety of the two basal rate regimens was assessed based on glucose variability measured with continuous glucose monitoring during the first two days after the start of insulin pump therapy.Results: Mean (± SD) coefficients of variation in the groups with circadian and flat basal rate at Day 1 were 31.06±12.13 and 32.74±10.7, respectively (p=0.423); at Day 2, 26.78±11.27 and 28.83±10.7 (p=0.309). Median [Q1; Q3] areas under glucose curve (AUC) values above the glucose targets in the groups with circadian and flat basal rate at Day 1 were 0.37 [0.03; 0.89] and 0.48 [0.08; 1.75], respectively, at Day 2 0.44 [0.03; 1.57] and 0.31 [0.1; 1.5], respectively (p>0.05). Median glucose AUC values below the goal in groups with circadian basal rate and flat basal rate on the first day were 0.01 [0; 0.06] and 0.02 [0; 0.1], respectively (p=0.855), on the second day – 0.00 [0; 0.01] and 0.00 [0; 0.02], respectively (р=0.085). We also haven’t found any between-group differences in the prevalence of glucose deviations below and above the target, as well as in the time spent in normoglycaemia.Conclusion: The comparative analysis of two basal insulin rate regimens in Type 1 diabetic patients switched to insulin pump therapy has shown no significant differences between them. The use of Renner’s scale has no clinical advantages over the fixed basal insulin regimen at initiation of insulin pump therapy in adults.

Metaheuristic Optimization of Insulin Infusion Protocols Using Historical Data with Validation Using a Patient Simulator

Metaheuristic search algorithms are used to develop new protocols for optimal intravenous insulin infusion rate recommendations in scenarios involving hospital in-patients with Type 1 Diabetes. Two metaheuristic search algorithms are used, namely, Particle Swarm Optimization and Covariance Matrix Adaption Evolution Strategy. The Glucose Regulation for Intensive Care Patients (GRIP) serves as the starting point of the optimization process. We base our experiments on a methodology in the literature to evaluate the favorability of insulin protocols, with a dataset of blood glucose level/insulin infusion rate time series records from 16 patients obtained from the Waikato District Health Board. New and significantly better insulin infusion strategies than GRIP are discovered from the data through metaheuristic search. The newly discovered strategies are further validated and show good performance against various competitive benchmarks using a virtual patient simulator.

P1796FACTORS PREDICTING SUBCUTANEOUS INSULIN REQUIREMENT AFTER KIDNEY TRANSPLANT

Background and Aims Diabetes is the most common cause for end stage renal disease leading to kidney transplant. Post transplant glycemic management has significant impact on long term outcomes, but is challenging, especially while transitioning patients from intravenous to subcutaneous insulin dose. This study was therefore planned to assess factors which influence subcutaneous insulin dose after kidney transplant. Method Data was prospectively collected from 98 consecutive kidney transplant patients with type 2 diabetes at a tertiary care hospital in India, with regards to age, gender, height, weight, duration of diabetes, pre transplant insulin dose, pre transplant use of oral antidiabetics. First two days after transplant patients are nil by mouth and are on insulin infusion (column based method). On third day, patients are transitioned to multiple subcutaneous insulin. We assessed and recorded the subcutaneous insulin dose requirement by 4th and 5th day. Results Mean (SD) for patients’ age was 52.28 (6.32) years, height 167.83 (5.64) cm, weight 70.55 (14.32) kg, body mass index 25.39 (4.72) kg/m2 and duration of diabetes 13.3 (7.02) years. All 98 transplant recipients were male. Mean insulin requirement before transplant was 15.37 (20.24) units/day. Mean post transplant intravenous insulin infusion rate for 4 hours before transitioning to subcutaneous insulin was 2.07 (0.987) units/hour. Mean subcutaneous insulin requirement after transplant was 73.18 (25.45) units/day or 1.12 (0.61) units/kgbw. Mean basal insulin dose was 25.32 (10.91) units. Mean bolus dose before breakfast was 10.75 (4.37) units, before lunch was 20.12 (7.4)) units, before evening snack was 6.65 (3.43) units and before dinner was 10.75 (4.11) units. In terms of proportion of total daily dose (TDD), mean basal insulin was 0.34 (0.08) of TDD, bolus dose before breakfast was 0.15 (0.03) of TDD, before lunch was 0.28 (0.05) of TDD, before evening snack was 0.09 (0.04) of TDD and before dinner was 0.15 (0.04) of TDD. Subcutaneous insulin dose after transplant correlated with insulin dose of the recipient before transplant (Pearson’s coefficient 0.43; p value 0.003) and weight of the patient (Pearson’s coefficient 0.32; p value 0.001). It did not correlate with age of the recipient, duration of diabetes, intravenous insulin infusion rate or tac level. On multivariate linear regression analysis to assess the factors predicting subcutaneous insulin dose after transplant, only pre-transplant insulin dose was significant (p value 0.046). Age of the recipient, duration of diabetes, weight of the patient, intravenous insulin infusion rate or preoperative use of oral anti diabetic were not significant Conclusion In kidney transplant patients with type 2 diabetes, only pre transplant insulin dose predicted the subcutaneous insulin dose post transplant.

Reinforcement Learning Algorithm for Blood Glucose Control in Diabetic Patients

In this paper a reinforcement learning algorithm is applied to regulating the blood glucose level of Type I diabetic patients using insulin pump. In this approach the agent learns from its exploration and experiences to selects its actions. In the current reinforcement learning algorithm, body weight, A1C level, and physical activity define the state of a diabetic patient. For the agent, insulin dose levels constitute the actions. There are five alternative actions for the agent: (1) raising the insulin infusion rate during 24 hours, (2) keeping it the same, (3) decreasing insulin infusion rate, (4) adjusting basal rate two times during 24 hours, and (5) adjusting basal rate three times during 24 hours. As a result of a patient’s treatment, after each time step t, the reinforcement learning agent receives a numerical reward depending on the response of the patient’s health condition. At each stage the reward is calculated as a function of the deviation of the A1C from its target value. Since reinforcement learning algorithm can select actions that improve patient condition by taking into account delayed effects it has tremendous potential to control blood glucose level in diabetic patients. This research will utilize ten years of clinical data obtained from a hospital.

The effects of acute hyperinsulinemia on bone metabolism

Insulin signaling in bone-forming osteoblasts stimulates bone formation and promotes the release of osteocalcin (OC) in mice. Only a few studies have assessed the direct effect of insulin on bone metabolism in humans. Here, we studied markers of bone metabolism in response to acute hyperinsulinemia in men and women. Thirty-three subjects from three separate cohorts (n=8, n=12 and n=13) participated in a euglycaemic hyperinsulinemic clamp study. Blood samples were collected before and at the end of infusions to determine the markers of bone formation (PINP, total OC, uncarboxylated form of OC (ucOC)) and resorption (CTX, TRAcP5b). During 4 h insulin infusion (40 mU/m2 per min, low insulin), CTX level decreased by 11% (P<0.05). High insulin infusion rate (72 mU/m2 per min) for 4 h resulted in more pronounced decrease (−32%, P<0.01) whereas shorter insulin exposure (40 mU/m2 per min for 2 h) had no effect (P=0.61). Markers of osteoblast activity remained unchanged during 4 h insulin, but the ratio of uncarboxylated-to-total OC decreased in response to insulin (P<0.05 and P<0.01 for low and high insulin for 4 h respectively). During 2 h low insulin infusion, both total OC and ucOC decreased significantly (P<0.01 for both). In conclusion, insulin decreases bone resorption and circulating levels of total OC and ucOC. Insulin has direct effects on bone metabolism in humans and changes in the circulating levels of bone markers can be seen within a few hours after administration of insulin.

An in silico method to identify computer-based protocols worthy of clinical study: An insulin infusion protocol use case

Objective Develop an efficient non-clinical method for identifying promising computer-based protocols for clinical study. An in silico comparison can provide information that informs the decision to proceed to a clinical trial. The authors compared two existing computer-based insulin infusion protocols: eProtocol-insulin from Utah, USA, and Glucosafe from Denmark. Materials and Methods The authors used eProtocol-insulin to manage intensive care unit (ICU) hyperglycemia with intravenous (IV) insulin from 2004 to 2010. Recommendations accepted by the bedside clinicians directly link the subsequent blood glucose values to eProtocol-insulin recommendations and provide a unique clinical database. The authors retrospectively compared in silico 18 984 eProtocol-insulin continuous IV insulin infusion rate recommendations from 408 ICU patients with those of Glucosafe, the candidate computer-based protocol. The subsequent blood glucose measurement value (low, on target, high) was used to identify if the insulin recommendation was too high, on target, or too low. Results Glucosafe consistently provided more favorable continuous IV insulin infusion rate recommendations than eProtocol-insulin for on target (64% of comparisons), low (80% of comparisons), or high (70% of comparisons) blood glucose. Aggregated eProtocol-insulin and Glucosafe continuous IV insulin infusion rates were clinically similar though statistically significantly different (Wilcoxon signed rank test P = .01). In contrast, when stratified by low, on target, or high subsequent blood glucose measurement, insulin infusion rates from eProtocol-insulin and Glucosafe were statistically significantly different (Wilcoxon signed rank test, P &lt; .001), and clinically different. Discussion This in silico comparison appears to be an efficient nonclinical method for identifying promising computer-based protocols. Conclusion Preclinical in silico comparison analytical framework allows rapid and inexpensive identification of computer-based protocol care strategies that justify expensive and burdensome clinical trials.

Effects of Measurement Frequency on Analytical Quality Required for Glucose Measurements in Intensive Care Units: Assessments by Simulation Models

BACKGROUND Total error allowances have been proposed for glucose meters used in tight-glucose-control (TGC) protocols. It is unclear whether these proposed quality specifications are appropriate for continuous glucose monitoring (CGM). METHODS We performed Monte Carlo simulations of patients on TGC protocols. To simulate use of glucose meters, measurements were made hourly. To simulate CGM, glucose measurements were made every 5 min. Glucose was measured with defined bias (varied from −20% to 20%) and imprecision (0% to 20% CV). The measured glucose concentrations were used to alter insulin infusion rates according to established treatment protocols. Changes in true glucose were calculated hourly on the basis of the insulin infusion rate, the modeled patient's insulin sensitivity, and a model of glucose homeostasis. We modeled 18 000 patients, equally divided between the hourly and every-5-min measurement schemas and distributed among 45 combinations of bias and imprecision and 2 treatment protocols. RESULTS With both treatment protocols and both measurement frequencies, higher measurement imprecision increased the rates of hypoglycemia and hyperglycemia and increased glycemic variability (SD). These adverse effects of measurement imprecision were lower at the higher measurement frequency. The rate of hypoglycemia at an imprecision (CV) of 5% with hourly measurements was similar to the rate of hypoglycemia at 10% CV when measurements were made every 5 min. With measurements every 5 min, imprecision up to 10% had minimal effects on hyperglycemia or glycemic variability. Effects of simulated analytical bias on glycemia were unaffected by measurement frequency. CONCLUSIONS Quality specifications for imprecision of glucose meters are not transferable to CGM.