Development and Evaluation of Personalized Explainable Machine Learning Models to Predict and Prevent Nocturnal Hypoglycemia in Type 1 Diabetes

开发和评估个性化可解释机器学习模型以预测和预防 1 型糖尿病夜间低血糖

• 批准号: 10373516
• 负责人: Clara Marcela Mosquera-Lopez
• 金额: $ 16.26万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-21 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10373516
• 关键词: Accounting; Adult; Affect; Algorithms; Beds; Big Data; Carbohydrates; Cellular Phone; Cessation of life; Clinic; Clinical Research; Consumption; Cross-Over Studies; Dangerousness; Data; Data Set; Detection; Development; Emergency Care; Engineering; Evaluation; Event; Exercise; Fiber; Fright; Glucose; Human; Hyperglycemia; Hypoglycemia; Individual; Infusion Pumps; Injection of therapeutic agent; Injections; Injury; Insulin; Insulin-Dependent Diabetes Mellitus; Intervention; Learning; Machine Learning; Macronutrients Nutrition; Measurement; Measures; Modeling; Nutrient; Outcome Measure; Participant; Phase; Physical activity; Psychological Transfer; Pump; Randomized; Registries; Risk; Running; Seizures; Sensitivity and Specificity; Sleep; Specificity; Symptoms; Syndrome; System; Testing; Time; Training; Unconscious State; Update; Wireless Technology; base; blood glucose regulation; cohort; comparison intervention; data management; design; diabetes management; diabetes mellitus therapy; experience; glycemic control; high risk; hypoglycemia unawareness; improved; intervention effect; large datasets; personalized decision; poor sleep; population based; prediction algorithm; predictive modeling; prevent; primary outcome; recruit; secondary outcome; sensor; sensor technology; side effect; simulation; sleep quality; subcutaneous; support tools

Development and Evaluation of Personalized Explainable Machine Learning Models to Predict and Prevent Nocturnal Hypoglycemia in Type 1 Diabetes Project Summary Hypoglycemia (glucose < 70 mg/dL) remains the limiting factor for achieving optimal glycemic control in type 1 diabetes (T1D), with nocturnal hypoglycemia being particularly dangerous. Nocturnal hypoglycemia may result in physical injury, poor sleep quality, fear of hypoglycemia, and hypoglycemia unawareness. Severe episodes can cause seizures and unconsciousness requiring emergency care, and even death (dead in bed syndrome). While automated insulin delivery (AID) systems have shown benefits in glucose control during the night, nighttime hypoglycemia still occurs. Moreover, many people with T1D manage their glucose with continuous subcutaneous infusion pump (CSII) therapy or multiple daily insulin injections (MDI) therapy. Data updated between 2013 and 2014 from 16,061 individuals with T1D participating in the T1D Exchange clinic registry showed that approximately 40% participants managed their glucose with MDI. In this project, we propose to develop and evaluate a personalized decision support tool that collects and analyzes glucose measurements, insulin, meals, and physical activity data to predict at bedtime the likelihood of overnight hypoglycemia and recommend a proactive carbohydrate intervention to substantially reduce nocturnal hypoglycemia. In the engineering development phase of the project, we will use unique datasets of time-matched glucose management data (i.e., continuous glucose measurements, insulin, meals, and exercise) from pump, closed- loop and MDI users to extract information about the major contributors to nocturnal hypoglycemia risk and train a population-based prediction model that will be personalized over time to better capture inter-subject variability. We will design a bedtime intervention consisting of a bedtime smart snack with variable nutrient content that can prevent nighttime hypoglycemia. Snacks will vary by macronutrient content and size to optimize time to peak post-prandial glycemia that will match the timing to predicted episode of hypoglycemia. We will conduct a randomized cross-over study to evaluate our smartphone-based decision support tool on a cohort of 20 people with T1D who are MDI users and are at higher risk of experiencing hypoglycemia. Participants will be randomly assigned to either first use CGM only (control period) followed by a smartphone- based decision support tool + nocturnal hypoglycemia intervention (intervention period), or vice-versa. The control and intervention periods will have a duration of three weeks each. We will measure the effect of the intervention by comparing the percent time in nocturnal hypoglycemia during the control period vs. the intervention period. We will also retrospectively measure the accuracy of the prediction model in predicting nocturnal hypoglycemia using data from the control period. We expect that the proposed bedtime intervention will lead to a significant reduction in time spent in hypoglycemia overnight of at least 50% reduction relative to baseline.

开发和评估个性化可解释的机器学习模型来预测和评估 预防 1 型糖尿病夜间低血糖 项目概要 低血糖（血糖 < 70 mg/dL）仍然是 1 型患者实现最佳血糖控制的限制因素 糖尿病（T1D），其中夜间低血糖尤其危险。可能会导致夜间低血糖 身体损伤、睡眠质量差、害怕低血糖、低血糖不自觉等。严重发作 可能导致癫痫发作和意识不清，需要紧急护理，甚至死亡（死在床上综合症）。 虽然自动胰岛素输送 (AID) 系统已显示出在夜间血糖控制方面的优势， 夜间低血糖仍会发生。此外，许多 T1D 患者通过持续控制血糖来控制血糖。 皮下输液泵（CSII）疗法或每日多次胰岛素注射（MDI）疗法。数据已更新 2013 年至 2014 年间，参与 T1D Exchange 诊所登记的 16,061 名 T1D 患者 研究表明，大约 40% 的参与者通过 MDI 控制血糖。在这个项目中，我们建议 开发和评估个性化决策支持工具，用于收集和分析血糖测量值， 胰岛素、膳食和体力活动数据可在睡前预测夜间低血糖的可能性， 建议采取积极的碳水化合物干预措施，以大幅减少夜间低血糖。在 在该项目的工程开发阶段，我们将使用时间匹配的葡萄糖的独特数据集 来自泵的管理数据（即连续血糖测量、胰岛素、膳食和运动） 循环和 MDI 用户提取有关夜间低血糖风险的主要因素的信息并进行培训 基于人群的预测模型，将随着时间的推移进行个性化，以更好地捕捉主体间的关系 可变性。我们将设计一种睡前干预措施，包括含有多种营养成分的睡前智能零食 可以预防夜间低血糖的内容。零食会根据大量营养素的含量和大小而有所不同 优化餐后血糖达到峰值的时间，使其与预测的低血糖发作时间相匹配。 我们将进行一项随机交叉研究，以评估我们基于智能手机的决策支持工具 20 名 T1D 患者组成的队列，他们是 MDI 用户，发生低血糖的风险较高。 参与者将被随机分配到首先仅使用 CGM（控制期），然后使用智能手机- 基于决策支持工具+夜间低血糖干预（干预期），或反之亦然。这 控制期和干预期各持续三周。我们将衡量效果 通过比较对照组和对照组期间夜间低血糖的时间百分比来进行干预 干预期。我们还将回顾性地衡量预测模型在预测方面的准确性 使用对照期的数据进行夜间低血糖。我们预计拟议的睡前干预 将导致夜间发生低血糖的时间显着减少，相对于 基线。