Predicting and Preventing Hospital Readmission in Patients with Diabetes and CVD
预测和预防糖尿病和心血管疾病患者再入院
基本信息
- 批准号:8891852
- 负责人:
- 金额:$ 17.16万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2015
- 资助国家:美国
- 起止时间:2015-04-01 至 2019-02-28
- 项目状态:已结题
- 来源:
- 关键词:AccountingAddressAdmission activityAreaBostonCalibrationCardiovascular DiseasesCaringClinicalClinical ResearchClinical TrialsCommunity Health Nurse SpecialistCommunity Health NursingDataDiabetes MellitusDiscriminationEducationElectronicsEvaluation ResearchEvidence based practiceGeneral HospitalsGlycosylated hemoglobin AGoalsHealth Care CostsHealth Care ReformHospital CostsHospitalizationHospitalsHypoglycemiaInformation TechnologyInpatientsInterventionKnowledgeLipidsMassachusettsMeasuresMedical centerMentorsMentorshipMethodsMinorityParticipantPatient CarePatient EducationPatientsPharmaceutical PreparationsPhasePilot ProjectsPopulationPreventionRandomized Controlled TrialsRecordsResearchResearch PersonnelResearch TrainingResourcesRiskRisk ReductionStructureTechniquesTestingTimeTrainingUnited StatesUnited States Dept. of Health and Human ServicesUniversity HospitalsUrban PopulationWomanWorkarmbasecareercareer developmentclinical practicecohortcostdiabetes mellitus therapydiabetic patienteconomic costglycemic controlhealth care qualityhigh riskhospital readmissionimprovedindexingnovelpatient orientedpilot trialpopulation healthpredictive modelingpreventpublic health relevanceresearch and developmentskillssocioeconomic disparitystatisticstherapy designtoolurban area
项目摘要
DESCRIPTION (provided by applicant): The overall goal of this K23 proposal is to train Daniel J. Rubin, MD, MSc for a career as an independent investigator in the prediction and prevention of hospital readmission among patients with diabetes. Readmissions within 30 days of discharge (30d readmissions) are a high-priority quality measure and target for cost reduction. Patients with diabetes and cardiovascular disease (CVD) are disproportionately over- represented in 30d readmissions, especially among racial minorities and urban populations. Currently there is no validated method to identify diabetic patients admitted for CVD at the highest risk of 30d readmission, which is a critical prerequisite for targeting limited resources fr reducing readmission risk to those most in need. Furthermore, there are no proven interventions to reduce the risk of 30d readmission specifically among patients with diabetes. Dr. Rubin plans to address these gaps in knowledge with 2 aims, 1) validating and refining a Diabetes Early Readmission Risk Index (DERRI-CVD) for predicting 30d readmission risk in diabetic patients admitted for CVD, and 2) assessing the feasibility and acceptability of a novel, multifactorial intervention designed to reduce 30d readmissions. The intervention adapts methods successfully used in other inpatient populations to diabetic patients hospitalized for CVD and will
target those identified as high risk for readmission based on the DERRI-CVD. There are 2 Phases to the proposed research that address these aims: Phase 1 uses a new retrospective cohort of diabetic patients to validate and refine the DERRI-CVD for prediction of 30d readmission risk; Phase 2 involves a single-arm pilot trial to qualitatively assess the feasibility
and acceptability of the intervention. In order to successfully complete the proposed research and build on skills to achieve the overall goal of independence, Dr. Rubin will receive additional training through formal coursework and structured mentorship in the following: validating and refining predictive models, clinical trials, and principles of hospital quality improvement with a focus on readmission prediction and prevention. A distinguished group of senior investigators will serve as mentors and advisors to supervise Dr. Rubin's research and career development. The proposed research and training will help Dr. Rubin acquire skills, establish a body of work in this area and generate preliminary data to support a competitive R01 application to test the readmission risk reduction intervention in a randomized controlled trial. Such work is highly relevant in the current era of soaring health care costs and national health care reform. This proposal will enable Dr. Rubin to develop into a successful independent investigator while improving patient care and reducing readmission disparities through clinical research.
描述(由申请人提供):本K23提案的总体目标是培训医学博士、理学硕士丹尼尔·J·鲁宾(Daniel J. Rubin),使其成为预测和预防糖尿病患者再入院的独立研究者。出院后30天内的再入院(30 d再入院)是一项高优先级的质量措施,也是降低成本的目标。糖尿病和心血管疾病(CVD)患者在30天再入院中的比例过高,特别是在少数民族和城市人群中。目前还没有经过验证的方法来确定因CVD入院的糖尿病患者在30天再入院的最高风险,这是将有限的资源用于降低最需要的患者再入院风险的关键先决条件。此外,没有经过证实的干预措施可以降低糖尿病患者30天再入院的风险。Rubin博士计划通过两个目标来解决这些知识差距,1)验证和改进糖尿病早期再入院风险指数(DERRI-CVD),用于预测因CVD入院的糖尿病患者的30天再入院风险,以及2)评估旨在减少30天再入院的新型多因素干预的可行性和可接受性。该干预措施将在其他住院人群中成功使用的方法应用于因CVD住院的糖尿病患者,
针对那些根据DERRI-CVD确定为再入院高风险的患者。提出的研究有2个阶段来解决这些目标:第1阶段使用一个新的糖尿病患者回顾性队列来验证和完善DERRI-CVD预测30天再入院风险;第2阶段涉及一项单臂试点试验,以定性评估可行性
干预的可接受性。为了成功完成拟议的研究,并建立技能,以实现独立的总体目标,鲁宾博士将通过正式的课程和结构化的指导在以下方面接受额外的培训:验证和完善预测模型,临床试验和医院质量改进的原则,重点是再入院预测和预防。一组杰出的高级研究人员将担任导师和顾问,监督鲁宾博士的研究和职业发展。拟议的研究和培训将帮助鲁宾博士获得技能,在这一领域建立一个工作机构,并生成初步数据,以支持竞争性R 01应用程序,以在随机对照试验中测试再入院风险降低干预措施。在当前医疗保健费用飙升和国家医疗保健改革的时代,这项工作具有高度相关性。这项提案将使鲁宾博士能够发展成为一名成功的独立研究者,同时通过临床研究改善患者护理并减少再入院差异。
项目成果
期刊论文数量(0)
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Daniel J Rubin其他文献
Daniel J Rubin的其他文献
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{{ truncateString('Daniel J Rubin', 18)}}的其他基金
Developing and validating EHR-integrated readmission risk prediction models for hospitalized patients with diabetes
开发和验证住院糖尿病患者的 EHR 集成再入院风险预测模型
- 批准号:
10414988 - 财政年份:2020
- 资助金额:
$ 17.16万 - 项目类别:
Developing and validating EHR-integrated readmission risk prediction models for hospitalized patients with diabetes
开发和验证住院糖尿病患者的 EHR 集成再入院风险预测模型
- 批准号:
10245208 - 财政年份:2020
- 资助金额:
$ 17.16万 - 项目类别:
Developing and validating EHR-integrated readmission risk prediction models for hospitalized patients with diabetes
开发和验证住院糖尿病患者的 EHR 集成再入院风险预测模型
- 批准号:
10629295 - 财政年份:2020
- 资助金额:
$ 17.16万 - 项目类别:
Predicting and Preventing Hospital Readmission in Patients with Diabetes and CVD
预测和预防糖尿病和心血管疾病患者再入院
- 批准号:
9206498 - 财政年份:2015
- 资助金额:
$ 17.16万 - 项目类别:
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