Optimal Insulin Dose Research Articles

An SMS-Guided Basal Insulin Titration in People with Type 2 Diabetes New on Insulin.

The shortage of general practitioners (GPs) and the increasing prevalence of type 2 diabetes create significant pressure on primary healthcare services. To enable that medical services are available to allthat need it, innovative solutions are needed. One of those, a Short Message Service (SMS)-supported basal insulin titration service is investigated in this study. The primary objective was to determine the percentage of subjects who achieved stable fasting blood glucose (FBG) within their individual target range with this service after week 16. This single-arm, 16-week study aimed to enroll 111 adults diagnosed with type 2 diabetes that needed insulin. The study subjects measured their FBG 4 consecutive days to establish a baseline, then received SMS prompts for daily FBG measurements and evening insulin injections until their FBG stabilized within the target range. Adjusting the insulin based on the FBG. Once stabilization was achieved, subjects continued with their optimal insulin dose for the remainder of the study. Sixteen weeks after the baseline, subjects measured FBG for 4days before visit 4, where these values were read by the healthcare provider. Out of the planned 111 subjects, only 30 were enrolled, with one withdrawal prior to service activation. Challenges in subject recruitment were attributed to the COVID-19 outbreak, limited eligibility, competing studies, and new medications delaying insulin initiation. Subjects were on average 59.97years old, had an HbA1c of 9.29% a FBG of 205.64mg/dl, and had diabetes for 10years. Among the 29 subjects who started the service, 72% achieved successful titration at visit 4, with a median time of 49days. Notable improvements were observed in HbA1c levels (decreased by 1.58%) and FBG levels (decreased by 64mg/dl) over the 16-week study period. No adverse events or device-related issues were reported. Despite recruitment challenges, guided basal insulin titration holds promise for insulin therapy initiation in individuals. The findings emphasize the potential of tele-medical approaches, specifically through remote messaging, in managing diabetes and improving therapy adherence.

Smart Algorithms for Efficient Insulin Therapy Initiation in Individuals With Type 2 Diabetes: An in Silico Study.

Insulin-naive subjects with type 2 diabetes (T2D) start basal insulin titration from a low initial insulin dose (IID), which is adjusted weekly or twice per week based on fasting plasma glucose (FPG) measurement as recommended by the American Diabetes Association (ADA). The procedure to reach the optimal insulin dose (OID) is time-consuming, especially in subjects with high insulin needs (HIN). The aim of this study is to provide a fast and effective, but still safe, insulin titration algorithm in insulin-naive T2D subjects with HIN. To do that, we in silico cloned 300 subjects, matching a real population of insulin-naive T2D and used a logistic regression model to classify them as subjects with HIN or subjects with low insulin needs (LIN). Then, we applied to the subjects with HIN both a more aggressive insulin dose initiation (SMART-IID) and two newly developed titration algorithms (continuous glucose monitoring [CGM]-BASED and SMART-CGM-BASED) in which CGM was used to guide the decision-making process. The new titration algorithm applied to HIN-classified individuals guaranteed a faster reaching of OID, with significant improvements in time in range (TIR) and reduction in time above range (TAR) in the first months of the trial, without any clinically significant increase in the risk of hypoglycemia. Smart basal insulin titration algorithms enable insulin-naive T2D individuals to achieve OID and improve their glycemic control faster than standard guidelines, without jeopardizing patient safety.

G2P2C — A modular reinforcement learning algorithm for glucose control by glucose prediction and planning in Type 1 Diabetes

Developing diagnostic and treatment solutions for medical applications is often challenging due to the complex dynamics, partial observability, high inter- and intra-population variability, and the presence of unknown delays and disturbances. A characteristic case is the control of glucose concentration in people with Type 1 Diabetes (T1D) through the administration of exogenous insulin. The above complexities, enhanced by the significant cognitive burden associated with the estimation of optimal insulin dosing related to daily activities such as food intake and exercise, call for advanced insulin administration solutions towards a fully automated Artificial Pancreas System (APS). Reinforcement Learning (RL) is currently being explored in the development of APSs thanks to its demonstrated potential in problems characterized by complex dynamics and uncertainties. Despite the progress, RL algorithms in T1D still require manual estimation and announcement of meal carbohydrate (CHO) content or rely on small meal scenarios. In this study, we proposed G2P2C, a modular deep RL algorithm, which aims to fully automate glucose control in T1D, eliminating the need for CHO estimation and announcement. G2P2C was designed based on the state-of-the-art Proximal Policy Optimization (PPO) algorithm, augmented by two novel optimization phases: (i) model learning and (ii) planning. The former integrated an auxiliary learning task to learn a glucose dynamics model. The latter fine-tuned the learned control strategy to a short-time horizon by simulating glucose trajectories into the future. We evaluated the performance of G2P2C in-silico on a challenging meal protocol (180 g of CHO per day) for 20 subjects (10 adults and 10 adolescents) using an open-source version of a T1D simulator approved by the United States Food and Drug Administration (FDA). G2P2C was compared with state-of-the-art RL algorithms and two basal-bolus (BB) clinical treatment strategies, which involve manual meal announcement and CHO estimation with automated correction insulin boli for elevated glucose. G2P2C obtained statistically significant (P<0.05) reward improvements compared to PPO in 18 out of 20 subjects, while maintaining a lower failure rate. In addition, G2P2C achieved a time in range of 73% and 64% for the adult and adolescent cohorts, respectively, outperforming BB strategies in the adult cohort although no meal announcement was performed. The control performance and algorithmic characteristics of G2P2C show promise as a candidate algorithm for glucose control in APSs. We released the codebase of G2P2C (https://github.com/chirathyh/G2P2C) and an online demonstration tool (https://capsml.com/), where users can perform custom simulations to compare G2P2C with BB strategies, under the MIT license.

Use of Voice-Based Conversational Artificial Intelligence for Basal Insulin Prescription Management Among Patients With Type 2 Diabetes

Optimizing insulin therapy for patients with type 2 diabetes can be challenging given the need for frequent dose adjustments. Most patients receive suboptimal doses and do not achieve glycemic control. To examine whether a voice-based conversational artificial intelligence (AI) application can help patients with type 2 diabetes titrate basal insulin at home to achieve rapid glycemic control. In this randomized clinical trial conducted at 4 primary care clinics at an academic medical center from March 1, 2021, to December 31, 2022, 32 adults with type 2 diabetes requiring initiation or adjustment of once-daily basal insulin were followed up for 8 weeks. Statistical analysis was performed from January to February 2023. Participants were randomized in a 1:1 ratio to receive basal insulin management with a voice-based conversational AI application or standard of care. Primary outcomes were time to optimal insulin dose (number of days needed to achieve glycemic control), insulin adherence, and change in composite survey scores measuring diabetes-related emotional distress and attitudes toward health technology and medication adherence. Secondary outcomes were glycemic control and glycemic improvement. Analysis was performed on an intent-to-treat basis. The study population included 32 patients (mean [SD] age, 55.1 [12.7] years; 19 women [59.4%]). Participants in the voice-based conversational AI group more quickly achieved optimal insulin dosing compared with the standard of care group (median, 15 days [IQR, 6-27 days] vs >56 days [IQR, >29.5 to >56 days]; a significant difference in time-to-event curves; P = .006) and had better insulin adherence (mean [SD], 82.9% [20.6%] vs 50.2% [43.0%]; difference, 32.7% [95% CI, 8.0%-57.4%]; P = .01). Participants in the voice-based conversational AI group were also more likely than those in the standard of care group to achieve glycemic control (13 of 16 [81.3%; 95% CI, 53.7%-95.0%] vs 4 of 16 [25.0%; 95% CI, 8.3%-52.6%]; difference, 56.3% [95% CI, 21.4%-91.1%]; P = .005) and glycemic improvement, as measured by change in mean (SD) fasting blood glucose level (-45.9 [45.9] mg/dL [95% CI, -70.4 to -21.5 mg/dL] vs 23.0 [54.7] mg/dL [95% CI, -8.6 to 54.6 mg/dL]; difference, -68.9 mg/dL [95% CI, -107.1 to -30.7 mg/dL]; P = .001). There was a significant difference between the voice-based conversational AI group and the standard of care group in change in composite survey scores measuring diabetes-related emotional distress (-1.9 points vs 1.7 points; difference, -3.6 points [95% CI, -6.8 to -0.4 points]; P = .03). In this randomized clinical trial of a voice-based conversational AI application that provided autonomous basal insulin management for adults with type 2 diabetes, participants in the AI group had significantly improved time to optimal insulin dose, insulin adherence, glycemic control, and diabetes-related emotional distress compared with those in the standard of care group. These findings suggest that voice-based digital health solutions can be useful for medication titration. ClinicalTrials.gov Identifier: NCT05081011.

Personalized Prediction of Change in Fasting Blood Glucose Following Basal Insulin Adjustment in People With Type 2 Diabetes: A Proof-of-Concept Study.

For people with type 2 diabetes treated with basal insulin, suboptimal glycemic control due to clinical inertia is a common issue. Determining the optimal basal insulin dose can be difficult, as it varies between individuals. Thus, insulin titration can be slow and cautious which may lead to treatment fatigue and non-adherence. A model that predicts changes in fasting blood glucose (FBG) after adjusting basal insulin dose may lead to more optimal titration, reducing some of these challenges. To predict the change in FBG following adjustment of basal insulin in people with type 2 diabetes using a machine learning framework. A multiple linear regression model was developed based on 786 adults with type 2 diabetes. Data were divided into training (80%) and testing (20%) sets using a ranking approach. Forward feature selection and fivefold cross-validation were used to select features. Participants had a mean age of approximately 59 years, a mean duration of diabetes of 12 years, and a mean HbA1c at screening of 65 mmol/mol (8.1%). Chosen features were FBG at week 2, basal insulin dose adjustment from week 2 to 7, trial site, hemoglobin level, and alkaline phosphatase level. The model achieved a relative absolute error of 0.67, a Pearson correlation coefficient of 0.74, and a coefficient of determination of 0.55. A model using FBG, insulin doses, and blood samples can predict a five-week change in FBG after adjusting the basal insulin dose in people with type 2 diabetes. Implementation of such a model can potentially help optimize titration and improve glycemic control.

A practical approach based on learning-based model predictive control with minimal prior knowledge of patients for artificial pancreas

Background and objectivesComplete identification of the glucose dynamics for a patient generally requires prior clinical procedures and several measurements for the patient. However, these steps may not be always feasible. To address this limitation, we propose a practical approach integrating learning-based model predictive control (MPC), adaptive basal and bolus injections, and suspension with minimal requirements of prior knowledge of the patient. MethodsThe glucose dynamic system matrices were periodically updated using only input values, without any pretrained models. The optimal insulin dose was calculated based on a learning-based MPC algorithm. Meal detection and estimation modules were also introduced. The basal and bolus insulin injections were fine-tuned using the performance of glucose control from the previous day. To validate the proposed method, evaluations with 20 virtual patients from a type 1 diabetes metabolic simulator were employed. ResultsTime-in-range (TIR) and time-below-range (TBR) were 90.8% (84.1% – 95.6%) and 0.3% (0% – 0.8%), as represented by the median, first (Q1), and third quartiles (Q3), respectively, when meal intakes were fully announced. When one out of three meal intake announcements was missing, TIR and TBR were 85.2% (75.0% – 88.9%) and 0.9% (0.4% – 1.1%), respectively. ConclusionsThe proposed approach obviates the need for prior tests from patients and shows effective regulation of blood glucose levels. From the perspective of practical implementation in clinical environments, to deal with minimal prior information of the patient, our study demonstrates how essential clinical knowledge and learning-based modules can be integrated into a control framework for an artificial pancreas.

Insulin detection in diabetes mellitus: challenges and new prospects.

Tremendous progress has been made towards achieving tight glycaemic control in individuals with diabetes mellitus through the use of frequent or continuous glucose measurements. However, in patients who require insulin, accurate dosing must consider multiple factors that affect insulin sensitivity and modulate insulin bolus needs. Accordingly, an urgent need exists for frequent and real-time insulin measurements to closely track the dynamic blood concentration of insulin during insulin therapy and guide optimal insulin dosing. Nevertheless, traditional centralized insulin testing cannot offer timely measurements, which are essential to achieving this goal. This Perspective discusses the advances and challenges in moving insulin assays from traditional laboratory-based assays to frequent and continuous measurements in decentralized (point-of-care and home) settings. Technologies that hold promise for insulin testing using disposable test strips, mobile systems and wearable real-time insulin-sensing devices are discussed. We also consider future prospects for continuous insulin monitoring and for fully integrated multisensor-guided closed-loop artificial pancreas systems.

Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes

The widespread adoption of effective hybrid closed loop systems would represent an important milestone of care for people living with type 1 diabetes (T1D). These devices typically utilise simple control algorithms to select the optimal insulin dose for maintaining blood glucose levels within a healthy range. Online reinforcement learning (RL) has been utilised as a method for further enhancing glucose control in these devices. Previous approaches have been shown to reduce patient risk and improve time spent in the target range when compared to classical control algorithms, but are prone to instability in the learning process, often resulting in the selection of unsafe actions. This work presents an evaluation of offline RL for developing effective dosing policies without the need for potentially dangerous patient interaction during training. This paper examines the utility of BCQ, CQL and TD3-BC in managing the blood glucose of the 30 virtual patients available within the FDA-approved UVA/Padova glucose dynamics simulator. When trained on less than a tenth of the total training samples required by online RL to achieve stable performance, this work shows that offline RL can significantly increase time in the healthy blood glucose range from 61.6±0.3% to 65.3±0.5% when compared to the strongest state-of-art baseline (p<0.001). This is achieved without any associated increase in low blood glucose events. Offline RL is also shown to be able to correct for common and challenging control scenarios such as incorrect bolus dosing, irregular meal timings and compression errors. The code for this work is available at: https://github.com/hemerson1/offline-glucose.

Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments

People with type 1 diabetes (T1D) struggle to calculate the optimal insulin dose at mealtime, especially when under multiple daily injections (MDI) therapy. Effectively, they will not always perform rigorous and precise calculations; but occasionally, they might rely on intuition and previous experience. Reinforcement learning (RL) has shown outstanding results in outperforming humans on tasks requiring intuition and learning from experience. In this work, we propose an RL agent that recommends the optimal meal-accompanying insulin dose corresponding to a qualitative meal (QM) strategy that does not require precise carbohydrate counting (CC) (e.g., a usual meal at noon.). The agent is trained using the soft actor-critic approach and comprises long short-term memory (LSTM) neurons. For training, eighty virtual subjects (VS) of the FDA-accepted UVA/Padova T1D adult population were simulated using MDI therapy and QM strategy. For validation, the remaining twenty VS were examined in 26-week scenarios, including intra- and inter-day variabilities in glucose. In-silico results showed that the proposed RL approach outperforms a baseline run-to-run approach and can replace the standard CC approach. Specifically, after 26 weeks, the time-in-range (70 - 180mg/dL) and time-in-hypoglycemia (< 70mg/dL) were 73.1 ± 11.6% and 2.0 ± 1.8% using the RL-optimized QM strategy compared to 70.6± 14.8% and 1.5± 1.5% using CC. Such an approach can simplify diabetes treatment resulting in improved quality of life and glycemic outcomes.

928-P: Dysglycemia in Patients without Diabetes after Renal Transplantation

Background: Patients without a prior history of diabetes often experience post-operative hyperglycemia after undergoing renal transplantation. Hyperglycemia in the immediate post-transplant period has been associated with increased risk for infection, graft failure and hospital readmission. Methods: We performed a retrospective review of all adult patients undergoing renal transplantation at a single tertiary referral center from January 1 - December 31, 2019. Results: Of the 292 patients, 191 (65.2%) did not have a history of diabetes. Of these, 83 (43.5%) were female (mean ±SD age 46.3 ±14.7 years) while 1 (56.5%) were male (mean ±SD age 47.9 ±15.5 years) . The percentage of post-operative days (POD) while hospitalized with any hyperglycemia (≥180 mg/dL) was 55.5% (female 51.8%, male 58.3%) with the highest average blood glucose on POD1 among all patients. The mean±SD point of care (POC) glucose on POD0, 1, 2 and 3 was 123.4±31.5, 151.1±28.6, 139.8±29.7, and 117.4±28.9 mg/dL, respectively (n = 181, 145, 141, and 126 for respective days) . As compared with those without hyperglycemia, patients with any detected hyperglycemia up to POD5 had numerically higher percentage of hospital acquired infection (2.1% vs. 1%) , admission to the intensive care unit (7.9% vs. 3.7%) , and 30-day readmission (20.4% vs. 10.5%) . The percentage of post-operative days with POC glucose &amp;lt;70 mg/dl was 7.9% (female 8.4%, male 7.4%) . Conclusions: Patients without a history of diabetes who undergo renal transplantation experience hyperglycemia during the majority of days while hospitalized peri-transplant. Further analysis is needed to determine optimal insulin dosing strategies to improve glycemic control in this population. Disclosure S.A.Seggelke: None. M.Griff: None. R.Hawkins: None. S.Crawley: None. C.C.Low wang: Employee; CPC Clinical Research, Research Support; CellResearch Corporation, Virta Health Corp.

A Comparison of the Blood Glucose, Growth Hormone, and Cortisol Responses to Two Doses of Insulin (0.15 U/kg vs. 0.10 U/kg) in the Insulin Tolerance Test: A Single-Centre Audit of 174 Cases.

Objective The insulin tolerance test (ITT) is the gold standard endocrine test used to assess the integrity of the growth hormone (GH) and cortisol axes. The ITT has potential risks, and severe hypoglycaemia may necessitate intravenous glucose rescue. There is no clear consensus as to the optimal insulin dose for the ITT. Therefore, we sought to compare the standard dose (0.15 U/kg) and a low-dose ITT (0.1 U/kg). Design Single-centre audit of ITT data (2012–2021). Patients and Measurements. Patients who underwent an ITT to assess possible GH deficiency/adrenal insufficiency were included. Glucose, GH, and cortisol were measured at baseline and 30, 45, 60, 90, and 120 minutes following I.V. insulin bolus (0.15 U/kg or 0.10 U/kg). Results Of the ITTs performed, only 3/177 (1.7%) did not achieve adequate hypoglycaemia (≤2.2 mmol/L) with a single insulin dose. In total, 174 patients (43.5 ± 12.1 yrs, mean ± standard deviation) were included for analysis (0.15 U/kg: n = 113, 0.10 U/kg: n = 61). All 174 subjects had adequate hypoglycaemia regardless of baseline fasting blood glucose level or insulin dose. Neither nadir glucose nor glucose delta (i.e., baseline minus nadir) differed between insulin doses. Trends in both cortisol and GH responses over time were similar between groups, and a greater proportion of patients receiving the standard dose had an adequate cortisol response (77/106 (72.6%) vs. 32/60 (53.3%), p=0.01). The rates of glucose rescue did not differ in a subset of 79 patients, with on-demand glucose rescue in 4/35 (11%) for the standard dose and 2/44 (5%) for the low dose (p=0.25). Conclusions Our results suggest that the low-dose ITT produces comparable glucose, cortisol, and GH responses to the higher dose. Given the risks associated with hypoglycaemia, the low dose appears to be preferable to the standard dose ITT in most circumstances.

Titration of Long-Acting Insulin Using Continuous Glucose Monitoring and Smart Insulin Pens in Type 1 Diabetes: A Model-Based Carbohydrate-Free Approach.

ObjectiveMultiple daily injections (MDI) therapy is the most common treatment for type 1 diabetes (T1D), consisting of long-acting insulin to cover fasting conditions and rapid-acting insulin to cover meals. Titration of long-acting insulin is needed to achieve satisfactory glycemia but is challenging due to inter-and intra-individual metabolic variability. In this work, a novel titration algorithm for long-acting insulin leveraging continuous glucose monitoring (CGM) and smart insulin pens (SIP) data is proposed.MethodsThe algorithm is based on a glucoregulatory model that describes insulin and meal effects on blood glucose fluctuations. The model is individualized on patient’s data and used to extract the theoretical glucose curve in fasting conditions; the individualization step does not require any carbohydrate records. A cost function is employed to search for the optimal long-acting insulin dose to achieve the desired glycemic target in the fasting state. The algorithm was tested in two virtual studies performed within a validated T1D simulation platform, deploying different levels of metabolic variability (nominal and variance). The performance of the method was compared to that achieved with two published titration algorithms based on self-measured blood glucose (SMBG) records. The sensitivity of the algorithm to carbohydrate records was also analyzed.ResultsThe proposed method outperformed SMBG-based methods in terms of reduction of exposure to hypoglycemia, especially during the night period (0 am–6 am). In the variance scenario, during the night, an improvement in the time in the target glycemic range (70–180 mg/dL) from 69.0% to 86.4% and a decrease in the time in hypoglycemia (<70 mg/dL) from 10.7% to 2.6% was observed. Robustness analysis showed that the method performance is non-sensitive to carbohydrate records.ConclusionThe use of CGM and SIP in people with T1D using MDI therapy has the potential to inform smart insulin titration algorithms that improve glycemic control. Clinical studies in real-world settings are warranted to further test the proposed titration algorithm.SignificanceThis algorithm is a step towards a decision support system that improves glycemic control and potentially the quality of life, in a population of individuals with T1D who cannot benefit from the artificial pancreas system.

A Correction Insulin Bolus Delivery Strategy for Decision Support Systems in Type 1 Diabetes.

Management of type 1 diabetes (T1D) requires affected individuals to perform multiple daily actions to keep their blood glucose levels within the safe rage and avoid adverse hypo-/hyperglycemic episodes. Decision support systems (DSS) for T1D are composite tools that implement multiple software modules aiming to ease such a burden and to improve glucose control. At the University of Padova, we are developing a new DSS that currently integrate a smart insulin bolus calculator for optimal insulin dosing and a rescue carbohydrate intake advisor to tackle hypoglycemia. However, a module specifically targeting hyperglycemia, that suggests the administration of corrective insulin boluses (CIB), is still missing. For such a scope, this work aims to assess a recent literature methodology, proposed by Aleppo et al., which provides a simple strategy for dealing with hyperglycemia. The methodology is tested retrospectively on clinical data of individuals with T1D. In particular, here we leveraged a novel in silico tool that first identifies a non-linear model of glucose-insulin dynamics on data, then uses such model to simulate and compare the glucose trace obtained by "replaying" the recorded scenario and the glucose trace obtained using the CIB delivery strategy under evaluation. Results show that the CIB delivery strategy significantly reduce the percentage of time spent in hyperglycemia (-15.63%) without inducing any hypoglycemic episode, demonstrating both safety and efficacy of its use. These preliminary results suggest that the CIB delivery strategy proposed by Aleppo et al. is a promising candidate to be included in our system to counteract hyperglycemia. Future work will extensively evaluate the methodology and will compare it against other competing approaches.

Electronic health records based reinforcement learning for treatment optimizing

Electronic Health Records (EHRs) have become one of the main sources of evidence to evaluate clinical actions, improve medical quality, predict disease-risk, and optimize treatment effects. However, EHRs present several modeling challenges, including heterogeneous data types and dynamic characteristics. Reinforcement learning provides an efficient way for sequential decision-making. Powered by model-based reinforcement learning approach, we propose an EHRs-based reinforcement learning algorithm to optimize sequential treatment strategies for diseases, such as sepsis, diabetes, and their complications. We conduct our experiments with this algorithm to optimize physicians’ historical treatment strategies and achieve better glucose control for diabetic ketoacidosis (DKA) patients, which is one serious complication of diabetes. The research includes the modeling process and reinforcement learning process. During the EHRs modeling process, besides considering the necessary physiological variables, we also consider the major disease factors to enhance the interpretability of the model. In the reinforcement learning process, a deep Q network is employed to explore the optimal insulin dose for patients. Moreover, inspired by the real medical scenes, we extend the algorithm to cooperative learning environment. We use the joint policy of the two agents to simulate doctor consultations, and achieve better treatment performances in terms of policy and blood glucose control than single agent and clinicians.

825-P: Glycemic Control in Hospitalized Patients Receiving Enteral Nutrition

Objective: The incidence of hyperglycemia in patients receiving enteral nutrition ranges from 34.5-50% and is associated with increased morbidity and mortality. This study examined glycemic control in hospitalized patients receiving enteral nutrition on insulin therapy. Methods: Admission level data for all adults (18 years or older) admitted to UC Health Central from Jan 1-Dec 31, 2019 treated with insulin while receiving continuous enteral nutrition were included. Patients were stratified by any pre-existing diagnosis of diabetes mellitus (DM) determined from diagnosis codes prior to the admission date. Blood glucose values from point of care testing performed up to 10 days while receiving enteral nutrition and insulin were included. Results: A total of 662 patients accounted for 789 admissions in 2019 during which enteral nutrition and insulin were received. For each admission, the longest period of continuous tube feeding up to 10 days was selected, for a total of 5443 days for which data are available. Of the 662 patients, 348 (52.5%) had pre-existing DM (mean±SD 61.5±13.1 years) and 314 (47.4%) did not (mean±SD age 58.2 ±15.2 years); 260 (39.3%) were female and 402 (60.7%) were male. The glucose on days 2, 6, and 10 of enteral nutrition and insulin were 168.3±51.9, 160.1±41.1, and 153.4±38.6 mg/dL, respectively (n=621, 426, and 284 for respective days).. The percentage of inpatient days with a blood glucose value &amp;lt; 70 mg/dL in patients without DM was 2.8% vs. 5.3% in patients with DM (p&amp;lt;0.0001). Conclusion: Mean glucose improved and variability decreased over time in hospitalized patients with or without diabetes on enteral nutrition and insulin therapy, but the mean glucose was within the goal range of 140-180 mg/dL. Patients without a prior diagnosis of diabetes had a lower percentage of inpatient days with a BG &amp;lt; 70 mg/dL. Further analysis is needed to determine optimal insulin dosing strategies and to improve glycemic control in this population. Disclosure R. Hawkins: None. S. A. Seggelke: None. J. Gibbs: None. M. C. Lindsay: None. C. C. Low wang: None.

Addressing Hospital-Acquired Hypoglycemia.

Hospitalized patients who have diabetes often experience hospital-acquired hypoglycemia, a potentially serious adverse event; as a result, management of this condition has become an important quality of care indicator in the inpatient environment. A growing body of research and evidence-based clinical guidelines support proper timing of point of care (POC) blood glucose (BG) measurements, mealtime insulin administration, and meal delivery to reduce the incidence of both hypoglycemic and hyperglycemic events. Monitoring and improving the timing of these three patient care interventions are recognized as a crucial step in the safe and effective care of patients with diabetes. The objective of the QI project was to improve the timing of mealtime insulin administration related to bedside BG monitoring and meal delivery for patients with diabetes who receive mealtime insulin; a secondary goal was to decrease the number of episodes of recurrent hypoglycemia. The overall strategy was to change staff members' approach to mealtime insulin management from a series of individual tasks to a process-oriented collaborative approach. Nurses on the medical-surgical unit at one hospital within a large health system formed a QI team with staff members in information technology and food and nutrition services. The team implemented an eight-week QI pilot project (July 3 to August 26, 2017) using a multidisciplinary approach to coordinate between POC BG measurement, mealtime insulin administration, and meal delivery. More than two years after the hospital-wide rollout of the practice change, follow-up analysis has shown that, on both noncritical and critical care units, recurrent hypoglycemia has decreased. For example, comparing data obtained in a six-month period before the pilot project (November 2016 through April 2017) with the same six-month period in 2018 and 2019, more than a year after the pilot project, the percentage of patient stays (admissions) on noncritical care units in which there was a recurrence of hypoglycemia fell from 41.8% (of 1,162 total hospital admissions) to 35.1% (of 792 total hospital admissions); similarly, the percentage of patient stays on critical care units in which recurrent hypoglycemia occurred decreased from 36.8% to 22.8%. Findings suggest that ensuring a consistent 30-minute window between POC BG measurement and meal delivery enabled nursing staff to perform timely POC BG measurements and administer a more optimal mealtime insulin dose. Increasing interdisciplinary communication, collaboration, and awareness of best practice guidelines relating to proper mealtime insulin administration resulted in a sustained improvement in timing between POC BG measurements and mealtime insulin administration and between mealtime insulin administration and meal delivery.

Safety and Feasibility Evaluation of Step Count Informed Meal Boluses in Type 1 Diabetes: A Pilot Study.

Physical activity can cause glucose fluctuations both during and after it is performed, leading to hurdles in optimal insulin dosing in people with type 1 diabetes (T1D). We conducted a pilot clinical trial assessing the safety and feasibility of a physical activity-informed mealtime insulin bolus advisor that adjusts the meal bolus according to previous physical activity, based on step count data collected through an off-the-shelf physical activity tracker. Fifteen adults with T1D, each using a continuous glucose monitor (CGM) and an insulin pump with carbohydrate counting, completed two randomized crossover daily visits. Participants performed a 30 to 45-minute brisk walk before lunch and lunchtime insulin boluses were calculated based on either their standard therapy (ST) or the physical activity-informed bolus method. Post-lunch glycemic excursions were assessed using CGM readings. There was no significant difference between visits in the time spent in hypoglycemia in the post-lunch period (median [IQR] standard: 0 [0]% vs physical activity-informed: 0 [0]%, P = NS). Standard therapy bolus yielded a higher time spent in 70 to 180 mg/dL target range (mean ± standard: 77% ± 27% vs physical activity-informed: 59% ± 31%, P = .03) yet, it was associated with a steeper negative slope in the early postprandial phase (P = .032). Use of step count to adjust mealtime insulin following a walking bout has proved to be safe and feasible in a cohort of 15 T1D subjects. Physical activity-informed insulin dosing of meals eaten soon after a walking bout has a potential of mitigating physical activity related glucose reduction in the early postprandial phase.

A Model-Based Insulin Dose Optimization Algorithm for People With Type 1 Diabetes on Multiple Daily Injections Therapy.

Multiple daily injections (MDI) therapy is the most common treatment for type 1 diabetes (T1D) including basal insulin doses to keep glucose levels constant during fasting conditions and bolus insulin doses with meals. Optimal insulin dosing is critical to achieving satisfactory glycemia but is challenging due to inter- and intra-individual variability. Here, we present a novel model-based iterative algorithm that optimizes insulin doses using previous-day glucose, insulin, and meal data. Our algorithm employs a maximum-a-posteriori method to estimate parameters of a model describing the effects of changes in basal-bolus insulin doses. Then, parameter estimates, their confidence intervals, and the goodness of fit, are combined to generate new recommendations. We assessed our algorithm in three ways. First, a clinical data set of 150 days (15 participants) were used to evaluate the proposed model and the estimation method. Second, 60-day simulations were performed to demonstrate the efficacy of the algorithm. Third, a sample 6-day clinical experiment is presented and discussed. The model fitted the clinical data well with a root-mean-square-error of 1.75 mmol/L. Simulation results showed an improvement in the time in target (3.9-10 mmol/L) from 64% to 77% and a decrease in the time in hypoglycemia (< 3.9 mmol/L) from 8.1% to 3.8%. The clinical experiment demonstrated the feasibility of the algorithm. Our algorithm has the potential to improve glycemic control in people with T1D using MDI. This work is a step forward towards a decision support system that improves their quality of life.

Automatically accounting for physical activity in insulin dosing for type 1 diabetes

Background and ObjectiveType 1 diabetes is a disease characterized by lifelong insulin administration to compensate for the autoimmune destruction of insulin-producing pancreatic beta-cells. Optimal insulin dosing presents a challenge for individuals with type 1 diabetes, as the amount of insulin needed for optimal blood glucose control depends on each subject's varying needs. In this context, physical activity represents one of the main factors altering insulin requirements and complicating treatment decisions. This work aims to develop and test in simulation a data-driven method to automatically incorporate physical activity into daily treatment decisions to optimize mealtime glycemic control in individuals with type 1 diabetes. MethodsWe leveraged glucose, insulin, meal and physical activity data collected from twenty-three individuals to develop a method that (i) tracks and quantifies the accumulated glycemic impact from daily physical activity in real-time, (ii) extracts an individualized routine physical activity profile, and (iii) adjusts insulin doses according to the prolonged changes in insulin needs due to deviations in daily physical activity in a personalized manner. We used the data replay simulation framework developed at the University of Virginia to “re-simulate” the clinical data and estimate the performances of the new decision support system for physical activity informed insulin dosing against standard insulin dosing. The paired t-test is used to compare the performances of dosing methods with p < 0.05 as the significance threshold. ResultsSimulation results show that, compared with standard dosing, the proposed physical-activity informed insulin dosing could result in significantly less time spent in hypoglycemia (15.3± 8% vs. 11.1± 4%, p = 0.007) and higher time spent in the target glycemic range (66.1± 11.7% vs. 69.6± 12.2%, p < 0.01) and no significant difference in the time spent above the target range(26.6± 1.4 vs. 27.4± 0.1, p = 0.5). ConclusionsIntegrating daily physical activity, as measured by the step count, into insulin dose calculations has the potential to improve blood glucose control in daily life with type 1 diabetes.

717-P: Effect of Telephone Coaching Support on Glycemic Control in Type 2 Diabetes Treated with Glargine U300 Insulin

Background and Aims: “T-Coach® program” is a telephone support platform for type 2 diabetes (T2D) on glargine U300 insulin. This study aimed to evaluate whether this program was effective in helping patients reach their optimal insulin dose. Materials and Methods: Both T2D patients who were initiating glargine U300 or requiring dose titration, enrolled in T-Coach® program, were included in the study. Telephone intervention was delivered by diabetes nurse educators and consisted of regular telephone sessions, aimed at achievement of target fasting blood glucose (FBG) and reinforce diabetes education. The optimal insulin dose (OID) was defined as the dose of Glargine U300 required to achieve target FBG. The primary outcome was whether a patient reached the OID dose within 6 months. Results: A total of 589 patients consented and were included in TCoach program®. Median age was 65.4±11.2 years; 51.8% men; BMI 30.3±6 kg/m2. Of them, 65.9% were already on basal insulin (17.7% on Glargine U300). Mean glargine U300 dose was 27.4±15.9 UI. Mean baseline HbA1c was 9.4±1.8%, and basal FBG was 215.2±66.2 mg/dL. Mean FBG target was 88.7-132.3 mg/dl. Of them, 77.6% reached their OID at 6 months (75.7% at 3 months, and 41.2% at 1 month), and they did so in an average of 2.2±1.8 months. Mean FBG levels fell from 215.2 mg/dL to 137.9 mg/dL, 117.9 mg/dL and 115.9 mg/dL at months 1,3 and 6, respectively. Mean glargine U300 dose increased from 27.4 UI to 30.4 UI at month 6. Knowledge and adherence questionnaires were given to the patients. Mean score in knowledge questionnaire increased from 4.8 to 7.6 after the program. Moreover, a satisfaction survey was completed by 208 patients. The program was very well graded, with an average score of 9.1 out of 10. Conclusion: The T-Coach® program is an effective way to help patients reach their optimal insulin glargine U300 dose, improves glycemic control and can be an effective support for diabetes education. Disclosure D. Bellido Guerrero: None. V. Bellido: None. C. Morales: Other Relationship; Self; AstraZeneca, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Merck Sharp &amp; Dohme Corp., Novo Nordisk A/S, Sanofi. J. García: None. A. Muñoz-Garach: Speaker's Bureau; Self; Lilly Diabetes, Novo Nordisk A/S, Sanofi-Aventis. J.L. Fernandez Morera: None. B. Gonzalez Aguilera: None. M. López de la Torre: None. P. Fuentealba: Employee; Self; Sanofi.