糖尿病退伍军人低血糖的预测和预防-猫眼课题宝

糖尿病退伍军人低血糖的预测和预防

批准号：

9503930

负责人：

DONALD R MILLER

金额：

$0.0万

依托单位：

EDITH NOURSE ROGERS MEMORIAL VETERANS HOSPITAL

依托单位国家：

美国

项目类别：

财政年份：

2019

资助国家：

美国

项目状态：

已结题

起止时间：

2019-08-01 至 2023-07-31

来源：

https://reporter.nih.gov/project-details/9503930

关键词：

Address Address Adverse drug event Adverse drug event Adverse effects Adverse event Adverse event Algorithms Algorithms Awareness Awareness Blood Glucose Blood Glucose Blood Vessels Blood Vessels Cardiovascular system Cardiovascular system Caring Caring Cessation of life Cessation of life Clinical Clinical Clinical Management Clinical Management Code Code Complication Complication Data Data Decision Aid Decision Aid Development Development Diabetes Mellitus Diabetes Mellitus Diagnosis Diagnosis Documentation Documentation Electronic Health Record Electronic Health Record Evaluation Evaluation Event Event Eye Eye Fostering Fostering Glucose Glucose Goals Goals Health Health Hospitalization Hospitalization Human Human Hyperglycemia Hyperglycemia Hypoglycemia Hypoglycemia Incidence Incidence Kidney Kidney Laboratories Laboratories Measurable Measurable Measures Measures Medical Medical Medical Records Medical Records Metabolic Metabolic Methodology Methodology Methods Methods Modeling Modeling Monitor Monitor Natural Language Processing Natural Language Processing Operations Research Operations Research Outcome Outcome Patient Monitoring Patient Monitoring Patient Outcomes Assessments Patient Self-Report Patient Self-Report Patient risk Patient risk Patients Patients Performance Performance Population Population Prevention Prevention Process Process Provider Provider Public Health Public Health Quality of Care Quality of Care Recommendation Recommendation Reporting Research Research Risk Risk Risk Adjustment Risk Adjustment Risk Reduction Risk Reduction Safety Safety Sampling Sampling Services Services Severities Severities Source Source Standardization Standardization Structure Structure Subgroup Subgroup Surveys Surveys System System Technology Technology Testing Testing Text Text Time Time Validation Validation Veterans Veterans Work Work adverse outcome adverse outcome base base case finding case finding contextual factors contextual factors diabetes management diabetes management effective therapy effective therapy experience experience glycemic control glycemic control high risk high risk improved improved information model information model innovation innovation method development method development patient oriented patient oriented patient population patient population patient safety patient safety personalized approach personalized approach point of care point of care population health population health precision medicine precision medicine prediction algorithm prediction algorithm predictive modeling predictive modeling prevent prevent programs programs side effect structured data surveillance strategy surveillance strategy tool tool

项目摘要

项目成果

DONALD R MILLER的其他基金

相似基金

相关文献

中文摘要

翻译

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对糖尿病的护理。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潜在风险。降糖治疗的危险代谢并发症。传统上，这种风险被认为是在治疗过程中不可避免，但新的护理模式侧重于提高治疗的安全性，同时优化血糖控制。为了监测安全并促进更好的护理，需要开展工作标准化的绩效评估方法和战略。这项拟议的研究采用多种方法来解决低血糖和提高了糖尿病治疗的安全性。用于确定退伍军人患者群体中的病情糖尿病，我们建议分析国家VA和非VA结构化数据和索赔，衡量患者报告通过从分层随机抽样的患者中收集患者调查的经验，并制定准确的和高效的自然语言处理(NLP)工具来搜索医疗记录中的文档低血糖的证据。这将包括开发有效的病例查找算法。这些措施将进行组合和比较，以获得对并提供有关各种方法的准确性和完整性的实用信息。确诊为低血糖的患者将接受随访，以评估后续不良反应的风险。与这种情况相关的结果，包括反复低血糖，可预防的住院治疗，以及死亡。我们将结合所有可用的和相关的信息和模型低血糖来确定预测因素完成调查的患者样本，在整个退伍军人糖尿病人群中，限制候选从结构化医疗数据或从NLP提取中获得的因素的预测因子。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。这些型号将为开发适用于整个人口的简约预测算法以及相关亚组。风险算法将包括根据上下文因素对患者进行分类的分支，这些因素有助于指导临床管理。最实用的算法将在一个集成的糖尿病患者预测近乎实时低血糖病例发现和分配系统低血糖风险。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对于糖尿病和改善护理以降低风险。近实时低血糖病例发现及风险分析作业系统实施后，将可供业务和研究使用。这项工作可以形成新的护理质量衡量标准的基础，提供风险调整、设施和提供商概况分析和实践评估。它可用于生成临床警报或作为关注点决策辅助，建议量身定做的降低风险的方法。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。

英文摘要

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.

期刊论文(0)

专著(0)

科研奖励(0)

会议论文