PERSEVERE-PEF: optimizing medical therapy saves lives in heart failure with preserved ejection fraction

PERSEVERE-PEF：优化药物治疗可挽救射血分数保留的心力衰竭患者的生命

基本信息

批准号：
10381898
负责人：
Gabriela Voskerician
金额：
$ 103.49万
依托单位：
OPTIMA INTEGRATED HEALTH, INC.
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-15 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10381898
关键词：
Address Affect Algorithms American Area Atrial Fibrillation Automobile Driving California Cardiology Caring Cessation of life Clinical Complex Confidential Information Consumption Coronary Arteriosclerosis Data Element Development Diagnosis Disease Disease Management Dissemination and Implementation EFRAC Electronic Health Record Environment Evidence based intervention Expert Systems Guidelines Health Personnel Health system Heart failure Home Hospitalization Hospitals Hypertension Incidence Individual Intelligence Judgment Left Liquid substance Logic Medical Methodist Church Monitor Patients Pharmacology Population Process Protocols documentation Randomized Recommendation Records Sampling San Francisco Services Site Software Validation Standardization Syndrome Testing Time Treatment Efficacy Universities Visualization Voting Work adjudicate care coordination clinical care clinical research site cohort college community clinic comorbidity cost data cleaning digital experience health data hospitalization rates hypertension control improved improved outcome interoperability meetings mortality personalized care post-COVID-19 preservation regression algorithm response system architecture treatment optimization volume hypertension

项目摘要

Need. In the US, heart failure (HF) is the contributing cause of 1 in 8 deaths. HF with preserved ejection fraction (HFpEF) affects close to 50% of all HF patients. The 5-years mortality is 35%. HFpEF multi-organ syndrome clinical care management is complex and time consuming. In addressing HFpEF, the American College of Cardiology guidelines directed medical therapy (GDMT) references the medical therapy decision to the individual disease guidelines [hypertension (HTN), coronary artery disease (CAD), atrial fibrillation (AFib)]. Providing concerted multi-disease HFpEF management is a major unmet clinical need. Solution. In response to this need, we (OPTIMA) developed and demonstrated the feasibility of a clinical analytic intelligence (AI) for the management of HFpEF multi-organ syndrome, optima4PEF AI. The solution adds significant value to OPTIMA’s HF Management Service currently addressing the GDMT management of HTN (optima4BP AI) and HFrEF (optima4heart AI). optima4PEF deconstructs a complex set of disease- specific clinical guidelines and re-assembles them into a concerted multi-disease GDMT that is patient- personalized, explainable, and actionable. Objectives. PERSEVERE-PEF [optimizing medical therapy saves lives in heart failure with preserved ejection fraction] proposes to complete the AI development of optima4PEF product concept, and to validate its efficacy using contemporary, diverse, retrospective patient cohorts. Aim 1. Build optima4PEF AI to address the GDMT management of HFpEF multi-organ syndrome. Hypothesis. optima4PEF deconstructs a complex set of disease-specific clinical guidelines and re-assembles them into a concerted multi-disease GDMT that is patient-personalized, explainable, and actionable. The product concept work built the optima4PEF AI system architecture and developed the decision logic to address GDMT management for patients experiencing HFpEF + volume overload + HTN. optima4PEF product concept will be extended to include GDMT management of AFib and of CAD. An end-to-end algorithm regression test will be performed to verify that each decision logic step performs its intended function. Aim 2. Validate optima4PEF AI in recommending the most relevant GDMT. Hypothesis. In ≥ 90% of patient cases, optima4PEF case-specific treatment recommendation is ACCEPTED as the appropriate next step in the process of multi-disease GDMT treatment optimization of patients diagnosed with HFpEF. Unidentified patient records will be collected from 4 clinical partner sites. A randomization algorithm will select n=840 patient records. optima4PEF will generate a Treatment Action (TA) for each patient record. A simple majority rule of pharmacology and cardiology experts (n=5) will adjudicate the optima4PEF TA. optima4PEF averts loss of lives by assisting in the delivery of HFpEF multi-disease management. optima4PEF surveillance & personalized care support the emerging digital-first clinical care practices.

在美国，心力衰竭 (HF) 是导致射血不全的心力衰竭死亡的原因之一。 HFpEF 影响了近 50% 的心力衰竭患者，HFpEF 多器官的 5 年死亡率为 35%。美国人在处理 HFpEF 时，临床护理管理既复杂又耗时。心脏病学院指导医学治疗指南 (GDMT) 参考了医学治疗决定个体疾病指南[高血压（HTN）、冠状动脉疾病（CAD）、心房颤动（AFib）]。提供协调一致的多疾病 HFpEF 管理是一个未满足的主要临床需求。针对这一需求，我们（OPTIMA）开发并论证了临床的可行性。用于管理 HFpEF 多器官综合征的分析智能 (AI)，optima4PEF AI 解决方案。为目前解决 GDMT 管理问题的 OPTIMA HF 管理服务增添了重要价值 HTN (optima4BP AI) 和 HFrEF (optima4heart AI) 解构了一组复杂的疾病 - 具体的临床指南并将其重新组装成协调一致的多疾病 GDMT，该指南针对患者个性化、可解释且可操作。目标 PERSEVERE-PEF [优化药物治疗可挽救射血性心力衰竭患者的生命。 fraction]提出完成optima4PEF产品概念的AI开发，并验证其功效使用当代、多样化、回顾性的患者队列。目标 1. 构建 optima4PEF AI 以解决 HFpEF 多器官综合征的 GDMT 管理问题。 optima4PEF 解构了一套复杂的特定疾病临床指南并重新组装。将它们纳入协调一致的多疾病 GDMT，该 GDMT 是针对患者的个性化、可解释且可操作的。产品概念工作构建了 optima4PEF AI 系统架构并开发了决策逻辑解决 HFpEF + 容量超负荷 + optima4PEF 产品患者的 GDMT 管理问题。概念将扩展到包括 AFib 和 CAD 的 GDMT 管理端到端算法。将执行回归测试以验证每个决策逻辑步骤是否执行其预期功能。目标 2. 验证 optima4PEF AI 推荐的 GDMT ≥ 90%。患者病例，optima4PEF 病例特定治疗建议被接受为适当的下一步对诊断为 HFpEF 的患者进行多疾病 GDMT 治疗优化过程中的步骤。将从 4 个临床合作伙伴站点收集身份不明的患者记录，并采用随机算法进行选择。 n=840 个患者记录 optima4PEF 将为每个患者记录生成一个简单的治疗行动 (TA)。药理学和心脏病学专家（n=5）的多数规则将裁定最佳4PEF TA。 optima4PEF 通过协助进行 HFpEF 多疾病管理来避免生命损失。 optima4PEF 监测和个性化护理支持新兴的数字优先临床护理实践。