Prediction and Prevention of Hypoglycemia in Veterans with Diabetes

糖尿病退伍军人低血糖的预测和预防

• 批准号: 10194475
• 负责人: DONALD R MILLER
• 金额: --
• 依托单位: EDITH NOURSE ROGERS MEMORIAL VETERANS HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10194475
• 关键词: Address; Adverse drug event; Adverse event; Algorithms; Awareness; Blood Glucose; Blood Vessels; Cardiovascular system; Caring; Cessation of life; Clinical; Clinical Management; Code; Complication; Data; Decision Aid; Development; Diabetes Mellitus; Diagnosis; Documentation; Electronic Health Record; Evaluation; Event; Eye; Fostering; Glucose; Goals; Health; Hospitalization; Human; Hyperglycemia; Hypoglycemia; Incidence; Kidney; Laboratories; Measurable; Measures; Medical; Medical Records; Metabolic; Methodology; Methods; Modeling; Monitor; Natural Language Processing; Operations Research; Outcome; Patient Monitoring; Patient Outcomes Assessments; Patient Self-Report; Patient risk; Patients; Performance; Population; Prevention; Process; Provider; Public Health; Quality of Care; Recommendation; Research; Risk; Risk Adjustment; Risk Reduction; Safety; Sampling; Services; Severities; Source; Standardization; Structure; Subgroup; Surveys; System; Technology; Testing; Text; Time; Validation; Veterans; Work; adverse outcome; base; case finding; contextual factors; diabetes management; effective therapy; experience; glycemic control; high risk; improved; information model; innovation; method development; patient oriented; patient population; patient safety; personalized approach; point of care; population health; precision medicine; prediction algorithm; predictive modeling; prevent; programs; side effect; structured data; surveillance strategy; tool

Control of hyperglycemia to prevent or delay the onset of vascular complications is a fundamental goal of diabetes care. Intensive treatment is limited, however, by risk of hypoglycemia, a common and potentially hazardous metabolic complication of glucose-lowering treatment. Traditionally, this risk was considered unavoidable during treatment but new models of care focus on improving the safety of treatment while optimizing glycemic control. To monitor for safety and foster better care, work is needed to develop standardized methods and strategies for performance evaluation. The proposed research employs multiple methodologies to address the issue of hypoglycemia and improved safety of diabetes treatment. For identification of the condition in the Veteran patient population with diabetes, we propose to analyze national VA and non-VA structured data and claims, measure patient-reported experience through patient surveys collected from stratified random samples of patients, and develop accurate and efficient natural language processing (NLP) tools to search documentation in the medical records for evidence of hypoglycemia. This will include development of a valid case-finding algorithm. These measures will be combined and compared to obtain a unique and comprehensive evaluation of the condition in the patient population and to provide practical information on the accuracy and completeness of various methods. Patients with identified hypoglycemia will be followed forward to evaluate the risks of subsequent adverse outcomes associated with the condition, including repeat hypoglycemia, preventable hospitalizations, and death. We will combine all available and relevant information and model hypoglycemia to identify predictors in the sample of patients who completed the survey and in the whole VA diabetes population, limiting candidate predictors to factors available from structured medical data or from NLP extractions. We will identify those factors obtained from surveys or NLP extraction that add substantially to the predictive models. These models will inform the process of developing parsimonious predictive algorithms for the whole population and in relevant subgroups. Risk algorithms will include branching to classify patients by contextual factors that are useful in guiding clinical management. The best practical algorithms will be implemented in an integrated system for near real-time hypoglycemia case finding and assignment of diabetes patients by predicted hypoglycemia risks. This work will generate methods and tools for monitoring population health and safety among Veterans with diabetes and for improving care to reduce risks. The near real-time hypoglycemia case-finding and risk assignment system will be available for use by operations and research as it is implemented. This work could form the basis for new measures of care quality, providing technologies for risk adjustment, facility and provider profiling, and practice evaluations. It could be used in generating clinical alerts or as a point of care decision aid, suggesting approaches for tailored risk reduction. Ultimately, it would improve monitoring of population health and safety among Veterans and should facilitate precision medicine in diabetes care, with the emergence of optimal, tailored, and patient centered approaches for managing diabetes.

控制高血糖以预防或延缓血管并发症的发生是一个根本目标 糖尿病护理。然而，强化治疗受到低血糖风险的限制，低血糖是一种常见的和潜在的 降糖治疗的危险代谢并发症。传统上，这种风险被认为是 治疗过程中不可避免的，但新的护理模式侧重于提高治疗的安全性， 优化血糖控制。为了监测安全和促进更好的护理，需要开展工作， 绩效评估的标准化方法和战略。 拟议的研究采用多种方法来解决低血糖问题， 提高糖尿病治疗的安全性。用于识别退伍军人患者人群中的疾病， 糖尿病，我们建议分析国家VA和非VA结构化数据和索赔，测量患者报告的 通过对患者进行分层随机抽样收集的患者调查经验， 和高效的自然语言处理（NLP）工具，以搜索医疗记录中的文档， 低血糖的证据。这将包括制定有效的病例发现算法。这些措施 将被合并和比较，以获得一个独特的和全面的评估条件， 患者人群，并提供各种方法的准确性和完整性的实用信息。 将对已确定的低血糖患者进行随访，以评价后续不良事件的风险。 与病情相关的结局，包括反复低血糖、可预防的住院治疗，以及 死亡我们将结合联合收割机所有可用的相关信息，并对低血糖进行建模，以确定 完成调查的患者样本和整个VA糖尿病人群，限制候选人 从结构化的医学数据或从NLP提取的因素的预测。我们会找出那些 从调查或NLP提取中获得的因素，大大增加了预测模型。这些模型 将为整个人口开发节俭预测算法的过程提供信息， 相关子组。风险算法将包括分支，以根据上下文因素对患者进行分类， 有助于指导临床管理。最好的实用算法将在一个集成的 用于通过预测近实时低血糖病例发现和糖尿病患者分配的系统 低血糖风险。 这项工作将产生监测退伍军人人口健康和安全的方法和工具 以及改善护理以降低风险。近实时低血糖病例发现和风险 任务分配系统在实施后将可供业务和研究部门使用。这项工作可 形成新的护理质量措施的基础，提供风险调整技术，设施和 提供者分析和实践评估。它可用于生成临床警报或作为护理点 决策援助，建议有针对性的减少风险的方法。最终，它将改善对 退伍军人的人口健康和安全，并应促进糖尿病护理中的精准医学， 最佳的、量身定制的、以患者为中心的糖尿病管理方法的出现。