692-P: Development and Adoption of CDISC Clinical Data Standards for Type 1 Diabetes (T1D)

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 692-P
Author(s):  
JOHN M. OWEN
Keyword(s):  
Type 1 Diabetes ◽  
Clinical Data ◽  
Data Standards

Bariatric surgery in the treatment of patients with obesity and type 1 diabetes: A retrospective study of clinical data

2021 ◽  
Author(s):  
Nagi Mohammed ◽  
Adam Buckley ◽  
Mohgah Elsheikh ◽  
Matthew Allum ◽  
Sara Suliman ◽  
...  
Keyword(s):  
Bariatric Surgery ◽  
Type 1 Diabetes ◽  
Retrospective Study ◽  
Clinical Data

scholarly journals A Multiple Hypothesis Approach to Estimating Meal Times in Individuals With Type 1 Diabetes

2019 ◽  
Vol 15 (1) ◽  
pp. 141-146
Author(s):  
John P. Corbett ◽  
Marc D. Breton ◽  
Stephen D. Patek
Keyword(s):  
Type 1 Diabetes ◽  
Clinical Data ◽  
Estimation Error ◽  
Average Error ◽  
Accurate Information ◽  
Data Set ◽  
Multiple Hypothesis ◽  
Meal Time ◽  
Normally Distributed

Introduction: It is important to have accurate information regarding when individuals with type 1 diabetes have eaten and taken insulin to reconcile those events with their blood glucose levels throughout the day. Insulin pumps and connected insulin pens provide records of when the user injected insulin and how many carbohydrates were recorded, but it is often unclear when meals occurred. This project demonstrates a method to estimate meal times using a multiple hypothesis approach. Methods: When an insulin dose is recorded, multiple hypotheses were generated describing variations of when the meal in question occurred. As postprandial glucose values informed the model, the posterior probability of the truth of each hypothesis was evaluated, and from these posterior probabilities, an expected meal time was found. This method was tested using simulation and a clinical data set ( n = 11) and with either uniform or normally distributed ( μ = 0, σ = 10 or 20 minutes) prior probabilities for the hypothesis set. Results: For the simulation data set, meals were estimated with an average error of −0.77 (±7.94) minutes when uniform priors were used and −0.99 (±8.55) and −0.88 (±7.84) for normally distributed priors ( σ = 10 and 20 minutes). For the clinical data set, the average estimation error was 0.02 (±30.87), 1.38 (±21.58), and 0.04 (±27.52) for the uniform priors and normal priors ( σ = 10 and 20 minutes). Conclusion: This technique could be used to help advise physicians about the meal time insulin dosing behaviors of their patients and potentially influence changes in their treatment strategy.

scholarly journals A Reinforcement Learning–Based Method for Management of Type 1 Diabetes: Exploratory Study

JMIR Diabetes ◽  
10.2196/12905 ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. e12905 ◽  
Author(s):  
Mahsa Oroojeni Mohammad Javad ◽  
Stephen Olusegun Agboola ◽  
Kamal Jethwani ◽  
Abe Zeid ◽  
Sagar Kamarthi
Keyword(s):  
Type 1 Diabetes ◽  
Reinforcement Learning ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Glucose Level ◽  
Clinical Data ◽  
Exploratory Study ◽  
Insulin Dosage

Background Type 1 diabetes mellitus (T1DM) is characterized by chronic insulin deficiency and consequent hyperglycemia. Patients with T1DM require long-term exogenous insulin therapy to regulate blood glucose levels and prevent the long-term complications of the disease. Currently, there are no effective algorithms that consider the unique characteristics of T1DM patients to automatically recommend personalized insulin dosage levels. Objective The objective of this study was to develop and validate a general reinforcement learning (RL) framework for the personalized treatment of T1DM using clinical data. Methods This research presents a model-free data-driven RL algorithm, namely Q-learning, that recommends insulin doses to regulate the blood glucose level of a T1DM patient, considering his or her state defined by glycated hemoglobin (HbA1c) levels, body mass index, engagement in physical activity, and alcohol usage. In this approach, the RL agent identifies the different states of the patient by exploring the patient’s responses when he or she is subjected to varying insulin doses. On the basis of the result of a treatment action at time step t, the RL agent receives a numeric reward, positive or negative. The reward is calculated as a function of the difference between the actual blood glucose level achieved in response to the insulin dose and the targeted HbA1c level. The RL agent was trained on 10 years of clinical data of patients treated at the Mass General Hospital. Results A total of 87 patients were included in the training set. The mean age of these patients was 53 years, 59% (51/87) were male, 86% (75/87) were white, and 47% (41/87) were married. The performance of the RL agent was evaluated on 60 test cases. RL agent–recommended insulin dosage interval includes the actual dose prescribed by the physician in 53 out of 60 cases (53/60, 88%). Conclusions This exploratory study demonstrates that an RL algorithm can be used to recommend personalized insulin doses to achieve adequate glycemic control in patients with T1DM. However, further investigation in a larger sample of patients is needed to confirm these findings.

scholarly journals Generation of Virtual Patient Populations That Represent Real Type 1 Diabetes Cohorts

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1200
Author(s):  
Sayyar Ahmad ◽  
Charrise M. Ramkissoon ◽  
Aleix Beneyto ◽  
Ignacio Conget ◽  
Marga Giménez ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Clinical Data ◽  
Insulin Pump ◽  
Real Life ◽  
Model Parameters ◽  
Preclinical Testing ◽  
Time In Range ◽  
Therapy Strategies ◽  
Hospital Clinic

Preclinical testing and validation of therapeutic strategies developed for patients with type 1 diabetes (T1D) require a cohort of virtual patients (VPs). However, current simulators provide a limited number of VPs, lack real-life scenarios, and inadequately represent intra- and inter-day variability in insulin sensitivity and blood glucose (BG) profile. The generation of a realistic scenario was achieved by using the meal patterns, insulin profiles (basal and bolus), and exercise sessions estimated as disturbances using clinical data from a cohort of 14 T1D patients using the Medtronic 640G insulin pump provided by the Hospital Clínic de Barcelona. The UVa/Padova’s cohort of adult patients was used for the generation of a new cohort of VPs. Insulin model parameters were optimized and adjusted in a day-by-day fashion to replicate the clinical data to create a cohort of 75 VPs. All primary and secondary outcomes reflecting the BG profile of a T1D patient were analyzed and compared to the clinical data. The mean BG 166.3 versus 162.2 mg/dL ( = 0.19), coefficient of variation 32% versus 33% ( = 0.54), and percent of time in range (70 to 180 mg/dL) 59.6% versus 66.8% ( = 0.35) were achieved. The proposed methodology for generating a cohort of VPs is capable of mimicking the BG metrics of a real cohort of T1D patients from the Hospital Clínic de Barcelona. It can adopt the inter-day variations in the BG profile, similar to the observed clinical data, and thus provide a benchmark for preclinical testing of control techniques and therapy strategies for T1D patients.

Insulin Pumps in Type 1 Diabetes with Mental Disorders: Real-Life Clinical Data Indicate Discrepancies to Recommendations

2016 ◽  
Vol 18 (1) ◽  
pp. 34-38 ◽  
Author(s):  
Nicole Prinz ◽  
Christina Bächle ◽  
Marianne Becker ◽  
Gabriele Berger ◽  
Angela Galler ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Mental Disorders ◽  
Clinical Data ◽  
Real Life ◽  
Insulin Pumps

scholarly journals Insulin Resistance in Type 1 Diabetes Mellitus and Its Association with Patient’s Micro- and Macrovascular Complications, Sex Hormones, and Other Clinical Data

2019 ◽  
Vol 11 (1) ◽  
pp. 161-174 ◽  
Author(s):  
Diana Šimonienė ◽  
Aksana Platūkiene ◽  
Edita Prakapienė ◽  
Lina Radzevičienė ◽  
Džilda Veličkiene
Keyword(s):  
Diabetes Mellitus ◽  
Insulin Resistance ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Sex Hormones ◽  
Clinical Data ◽  
Macrovascular Complications
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