Using predictive analytics to tailor care for patients with newly diagnosed type 2 diabetes

使用预测分析为新诊断的 2 型糖尿病患者量身定制护理

基本信息

批准号：
10286549
负责人：
Anjali Gopalan
金额：
$ 24.9万
依托单位：
KAISER FOUNDATION RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10286549
关键词：
Adopted Adult Adverse event Age Alcohol consumption Appointment Artificial Intelligence Behavioral Body Weight decreased Body mass index California Caring Characteristics Clinical Clinical Data Complex Complications of Diabetes Mellitus Cost Sharing Data Diabetes Mellitus Diagnosis Disease Drug Prescriptions Electronic Health Record Ethnic Origin Exercise Foundations Functional disorder Future Gender Glucose Glycosylated hemoglobin A Goals Health Health Benefit Health Care Visit Health behavior Healthcare Individual Integrated Delivery of Health Care Intervention Kidney Diseases Life Style Link Machine Learning Measures Medical Memory Metabolic Methods Modeling Myocardial Infarction National Institute of Diabetes and Digestive and Kidney Diseases Newly Diagnosed Non-Insulin-Dependent Diabetes Mellitus Online Systems Outcome Patient Care Patient risk Patients Pattern Pharmaceutical Preparations Pharmacological Treatment Pharmacy facility Physiology Pragmatic clinical trial Predictive Analytics Preventive screening Race Research Risk Self Care Self Management Smoking Source Stroke System Telemedicine Time Visit Work advanced analytics adverse outcome analytical method base clinical predictors cohort comorbidity data repository diabetes control diabetes management disorder control electronic data follow-up glycemic control improved individual patient innovation macrovascular disease medication compliance member predictive modeling random forest response risk prediction risk stratification sociodemographics standard care tool treatment response treatment strategy

项目摘要

PROJECT SUMMARY Over 1.5 million US adults are diagnosed with type 2 diabetes (T2D) each year. These newly diagnosed individuals are at increased risk of developing debilitating complications, including renal disease, strokes, and myocardial infarctions. However, individuals differ widely in their likelihood of experiencing these adverse outcomes. Individual risk varies based on a complex interplay between pathophysiology, responsiveness to treatment, and patient capacity for self-management and making sustained lifestyle changes. Early T2D glycemic control provides a lasting benefit ("metabolic memory"); therefore, strategies that enable the effective targeting and tailoring of T2D care starting in the initial period after diagnosis may result in better long-term health outcomes. Implementing individually-tailored care strategies requires substantially more effective risk prediction tools than are currently available. This R21 proposal seeks to apply advanced analytic prediction modeling methods to a rich source of electronic health record (EHR)-derived clinical data (assessed at the time of initial diagnosis and after a year of standard management) to define individual patient risk profiles. These patient risk profiles will incorporate differences in disease physiology (e.g., reflected in factors such as age, BMI, hemoglobin A1c at diagnosis), treatment responsiveness, and early self- management results (e.g., medication adherence, weekly exercise levels, weight loss). This R21 leverages an established, well-characterized cohort of adults with incident, newly-diagnosed T2D (n=67,575) within Kaiser Permanente Northern California. We will apply advanced machine learning-based modeling methods (e.g., random forests, LASSO, extreme gradient boosting) to complete the following Aims: 1) Develop and validate a predictive model using EHR-derived patient data available at T2D diagnosis to identify patients at increased risk of suboptimal glycemic control over the five years following diagnosis and 2) Modify the Aim 1 model by incorporating clinical predictors captured during the first year following T2D diagnosis. We hypothesize that the unique information available at these two time points (i.e., initial diagnosis and after one year of standard treatment) can be used to individualize both initial and subsequent early care for patients with newly diagnosed T2D. If successful, this project's results can be applied to support targeted T2D care strategies tailored to each individual's risk of suboptimal five-year glycemic control (and later micro and macrovascular complications) based on differences in disease physiology, treatment response, and early self-care. This work will form the foundation for innovative, pragmatic clinical trials that advance our ultimate goal of providing proactive and effectively tailored early care that results in better long-term health outcomes for adults with T2D.

项目概要每年有超过 150 万美国成年人被诊断患有 2 型糖尿病 (T2D)。这些新确诊的个体出现使人衰弱的并发症的风险增加，包括肾病、中风和心肌梗塞。然而，个体经历这些不利影响的可能性差异很大结果。个体风险因病理生理学、对疾病的反应性之间复杂的相互作用而异。治疗、患者自我管理和持续改变生活方式的能力。早期 T2D 血糖控制提供持久的益处（“代谢记忆”）；因此，能够实现的策略在诊断后的初始阶段开始有效的针对性和定制 T2D 护理可能会导致更好的长期健康结果。实施个性化定制的护理策略需要大量比目前可用的更有效的风险预测工具。此 R21 提案旨在应用先进的对电子健康记录 (EHR) 衍生的临床数据的丰富来源进行分析预测建模方法（在初次诊断时和一年标准管理后进行评估）以确定个体患者风险概况。这些患者风险概况将包含疾病生理学的差异（例如，反映在因素（例如年龄、BMI、诊断时的血红蛋白 A1c）、治疗反应和早期自我治疗管理结果（例如，药物依从性、每周运动水平、体重减轻）。该 R21 利用 Kaiser 内已建立的、特征明确的新诊断 T2D 成人队列 (n=67,575) 北加州永久医院。我们将应用先进的基于机器学习的建模方法（例如，随机森林、LASSO、极限梯度提升）来完成以下目标：1）开发并验证预测模型使用 EHR 衍生的 T2D 诊断患者数据来识别患者诊断后五年内血糖控制不佳的风险，以及 2) 通过以下方式修改目标 1 模型纳入 T2D 诊断后第一年捕获的临床预测因素。我们假设在这两个时间点（即初步诊断和一年标准治疗后）可获得的独特信息治疗）可用于对新诊断的患者进行个体化的初始和后续早期护理 T2D。如果成功，该项目的结果可用于支持针对每个患者量身定制的有针对性的 T2D 护理策略个人五年血糖控制不佳的风险（以及后来的微血管和大血管并发症）基于疾病生理学、治疗反应和早期自我护理的差异。这项工作将形成创新、务实的临床试验的基础，推动我们的最终目标，即提供主动和有效定制的早期护理可为患有 T2D 的成人带来更好的长期健康结果。