Patient Level Prediction of Clinical Outcomes and Cost-Effectiveness in SPRINT (Optimize-SPRINT)

SPRINT 中临床结果和成本效益的患者水平预测 (Optimize-SPRINT)

• 批准号: 10083758
• 负责人: Adam P Bress
• 金额: $ 76.27万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-12-15 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10083758
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Adult; Ancillary Study; Antihypertensive Agents; Benefits and Risks; Biometry; Blood Pressure; Cessation of life; Characteristics; Chronic Disease; Clinical; Clinical Treatment; Clinical Trials; Cost of Illness; Costs and Benefits; Data; Data Analyses; Decision Analysis; Electrolytes; Eligibility Determination; Epidemiology; Equilibrium; Event; Goals; Gold; Government; Guidelines; Health Benefit; Health Care Costs; Health Personnel; Health system; Healthcare; Healthcare Systems; Hypertension; Hypotension; Incidence; Intervention Trial; Laboratories; Maps; Measures; Methods; Modeling; National Health and Nutrition Examination Survey; Office Visits; Outcome; Participant; Patients; Pharmaceutical Preparations; Population; Privatization; Public Health; Quality of life; Quality-Adjusted Life Years; Randomized; Randomized Clinical Trials; Reasons for Geographic And Racial Differences in Stroke; Reporting; Resources; Risk; Risk Reduction; Sampling; Serious Adverse Event; Societies; Syncope; Testing; Time; Titrations; Treatment Cost; Visit; adverse event risk; arm; base; blood pressure intervention; cardiovascular disorder risk; clinical practice; clinical predictors; cost; cost effective; cost effectiveness; design; economic evaluation; health care service utilization; health economics; high risk; hypertension treatment; implementation strategy; improved; individual patient; innovation; mortality; point of care; population health; predict clinical outcome; predictive modeling; preservation; prevent; preventable death; randomized trial; standard care; statistics; tool; treatment arm; treatment choice; treatment effect

Project Summary About 80 million U.S. adults have hypertension, and hypertension treatment costs the nation about $80 billion annually. Effective and inexpensive antihypertensive medications are available, but many patients remain inadequately treated. The Systolic Blood Pressure Intervention Trial (SPRINT) showed that in high CVD risk patients intensive treatment to a lower systolic blood pressure (SBP, <120 mm Hg) reduced CVD risk and all- cause mortality compared to standard treatment (SBP <140 mm Hg). We estimated that 16.8 million adults in the US meet the SPRINT eligibility criteria. Given this large number of eligible patients and the additional resources (e.g., health care provider time, laboratory visits) required to deliver intensive treatment, we need a method for selecting “high-value” patients most likely to benefit safely from intensive SBP treatment. Optimize-SPRINT is a SPRINT ancillary study. Optimize-SPRINT will apply predictive modeling methods to SPRINT to build a clinical decision tool that will be able to select optimal patients (e.g., high absolute benefit and cost-effectiveness) for intensive SBP treatment both at the point of care (for health care providers and patients) and in the population (for private health system and government payers). Our innovative predication modeling methods will identify the highest benefit patients underlying the average summary treatment effect reported from SPRINT, while preserving the advantages of SPRINT's randomized design. We aim to: • Develop and validate a predictor of clinical benefit with intensive SBP treatment (SPRINT Clinical Benefit Tool) using baseline characteristics. • Determine cost-effectiveness of intensive versus standard SBP treatment in SPRINT overall and by level of predicted SPRINT clinical benefit, and predict cost-effectiveness of intensive SBP treatment based on baseline characteristics. • Determine the population health impact of implementing intensive SBP treatment based on predicted SPRINT Clinical Benefit and cost-effectiveness. Relevance: The results of this study may change the way health care providers, payers, and clinical guideline makers treat hypertension in the US and abroad. This study will inform treatment choice decisions by health care providers and patients, and improve implementation strategies for private and government payers.

项目摘要 大约有8000万美国成年人患有高血压，高血压治疗使美国大约为800亿美元 可以使用一种抗高血压药物，但仍有许多患者 经过不足的治疗。收缩压干预试验（Sprint）表明，在高CVD风险下 患者对较低的收缩压（SBP，<120 mm Hg）进行密集治疗可降低CVD风险和全能 与标准治疗（SBP <140 mm Hg）相比导致死亡率。我们估计有1680万成年人 美国符合冲刺资格标准。鉴于这么大量的合格患者和额外的患者 提供密集治疗所需的资源（例如，医疗保健提供者时间，实验室访问），我们需要一个 选择“高价值”患者最有可能从密集的SBP治疗中安全受益的方法。 优化sprint是一项Sprint辅助研究。优化sprint将将预测建模方法应用于 短跑以构建一个临床决策工具，该工具将能够选择最佳患者（例如，高度益处 和成本效益）在护理点进行密集的SBP治疗（用于医疗保健提供者和 患者）和人口（用于私人卫生系统和政府付款人）。我们的创新预测 建模方法将确定平均摘要治疗效果的最高利益患者 从Sprint报道的同时保留了Sprint随机设计的优势。我们的目标是： •通过强化SBP治疗（Sprint Clinical）开发和验证临床益处的预测指标 利益工具）使用基线特征。 •确定Sprint中密集型与标准SBP治疗的成本效益总体以及通过 预测的Sprint临床益处的水平，并预测密集型SBP治疗的成本效益 基于基线特征。 •根据预测，确定实施强化SBP治疗的人口健康影响 冲刺临床益处和成本效益。 相关性：这项研究的结果可能会改变医疗保健提供者，付款人和临床指南的方式 制造商治疗美国和国外的高血压。这项研究将根据健康状况为治疗选择提供信息 护理提供者和患者，并改善私人和政府付款人的实施策略。

项目成果

An efficient approach for optimizing the cost-effective individualized treatment rule using conditional random forest.

使用条件随机森林优化具有成本效益的个体化治疗规则的有效方法。

• DOI: 10.1177/09622802221115876
• 发表时间: 2022
• 期刊: Statistical methods in medical research
• 影响因子: 2.3
• 作者: Xu,Yizhe;Greene,TomH;Bress,AdamP;Bellows,BrandonK;Zhang,Yue;Zhang,Zugui;Kolm,Paul;Weintraub,WilliamS;Moran,AndrewS;Shen,Jincheng
• 通讯作者: Shen,Jincheng

Home Blood Pressure Monitoring for Hypertension Diagnosis by Current Recommendations: A Long Way to Go.

• DOI: 10.1161/hypertensionaha.121.18463
• 发表时间: 2022-03
• 期刊: Hypertension (Dallas, Tex. : 1979)
• 作者: Bryant KB;Green MB;Shimbo D;Schwartz JE;Kronish IM;Zhang Y;Sheppard JP;McManus RJ;Moran AE;Bellows BK
• 通讯作者: Bellows BK

Patient Selection for Intensive Blood Pressure Management Based on Benefit and Adverse Events.

• DOI: 10.1016/j.jacc.2021.02.058
• 发表时间: 2021-04-27
• 期刊: Journal of the American College of Cardiology
• 影响因子: 24
• 作者: Bress AP;Greene T;Derington CG;Shen J;Xu Y;Zhang Y;Ying J;Bellows BK;Cushman WC;Whelton PK;Pajewski NM;Reboussin D;Beddu S;Hess R;Herrick JS;Zhang Z;Kolm P;Yeh RW;Basu S;Weintraub WS;Moran AE;SPRINT Research Group
• 通讯作者: SPRINT Research Group