Integrated Model of Palliative and Primary Care in Seriously Ill Older Adults

重病老年人的姑息治疗和初级保健综合模式

基本信息

批准号：
9565691
负责人：
Ziad Obermeyer
金额：
$ 38.26万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-30 至 2018-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9565691
关键词：
Acute Adoption Advance Care Planning Area Under Curve Awareness Boston Caring Cessation of life Chronic Chronic Disease Clinic Clinic Visits Clinical Trials Complex Comprehensive Health Care Data Data Analytics Diagnosis Disease Elderly Electronic Health Record Emergency Department patient Emergency Situation Enrollment Event Face Geriatrics Home environment Hospitalization Hospitals Intervention Length of Stay Machine Learning Malignant Neoplasms Measurable Measures Medical Methods Modeling Older Population Outcome Palliative Care Patient Care Patient Outcomes Assessments Patient risk Patient-Focused Outcomes Patients Performance Pharmaceutical Preparations Population Population Heterogeneity Predictive Value Primary Health Care Procedures Quality of life Randomized Controlled Trials Records Reporting Research Design Resources Risk Role SF-12 Sampling Series Specialist Statistical Models Symptoms Techniques Testing Text Thinness Time Validation Visit Work base clinical infrastructure cost design end of life end of life care evidence base falls health care service utilization high risk hospice environment improved improved outcome interest intervention effect mortality multiple chronic conditions novel older patient oncology outcome forecast palliative patient population patient subsets predictive modeling primary care setting primary outcome prognostic programs randomized trial trial comparing

项目摘要

Project Summary Background Palliative care is known to improve patient outcomes and reduce health care utiliza- tion in patients with cancer. But we know little on how to deliver palliative care to the large and growing population of older patients with multiple chronic conditions. Palliative care clinicians are a scarce resource, so care must be targeted to the subset of patients who would benefit most: those at highest risk of near-term death. This is a major challenge outside of specific diseases with known trajectories. Clinicians struggle with prognosis, and current statistical models perform poorly. Aims We will use novel predictive modeling methods (`machine learning') to identify complex old- er patients at high risk of one-year mortality, drawing on our team's prior work in data analytics and machine learning. We will apply these methods to a diverse population of older patients with multi- ple chronic conditions, in a large academic primary care network. Building on our team's track rec- ord of successful clinical trials, we will conduct a randomized controlled trial of palliative care inte- grated with primary care, targeting older patients at the highest predicted risk of death. We will as- sess impact on a range of measurable patient-reported outcomes and health care utilization. Study design We will develop a model to predict one-year mortality in primary care patients over 65, using a rich set of variables from electronic health records. Our preliminary data indicate that machine learning models are highly accurate for predicting mortality out-of-sample, i.e., in patients the model has never seen. We will identify patients at the highest risk of death—who would benefit most from scarce palliative care resources—and approach them to participate in a randomized trial, comparing usual primary care to primary care integrated with palliative care. The intervention, a series of home-based visits by palliative care clinicians, will build a longitudinal relationship with the patient and primary care team. This strategy is designed specifically to meet the needs of older pa- tients, as well as busy primary care clinicians. We will power the study to detect changes in two primary outcomes: quality of life and care intensity, measured by hospital and emergency visits. Other outcomes include symptom burden, advanced care planning, hospice use, and mortality. Implications This project will generate the first evidence on a new model of palliative care for older adults with multiple chronic illnesses, delivered `upstream' in the disease trajectory. We will build the technical and clinical infrastructure needed to target palliative care interventions for older adults outside of specific disease-based programs. A successful trial would facilitate broader adop- tion of similar interventions for older adults, and fundamentally transform the scale and scope of palliative care efforts in this population.

项目摘要众所周知，背景姑息治疗可改善患者的结果并减少医疗保健利用率 - 癌症患者的影响。但是我们对如何提供姑息治疗不了解患有多种慢性病的老年患者的人口不断增长。姑息治疗临床医生是稀缺的资源，因此必须关心将受益最大的患者子集作为目标：近期死亡的最高风险。这是在特定疾病之外的主要挑战已知轨迹。临床医生在预后挣扎，当前的统计模型的表现不佳。目的我们将使用新颖的预测建模方法（“机器学习”）来识别复杂的旧 - ER患者一年死亡的高风险，借鉴了我们团队先前在数据分析方面的工作机器学习。我们将把这些方法应用于多个老年患者的潜水员在大型学术初级保健网络中，PLE慢性病。建立在我们团队的轨道上 - 成功的临床试验，我们将进行姑息治疗的随机对照试验带有初级保健的门控，以最高预测的死亡风险为目标。我们会 - 对一系列可测量的患者报告结果和医疗保健利用的影响。研究设计我们将开发一个模型，以预测初级保健患者的一年死亡率 65，使用来自电子健康记录的丰富变量。我们的初步数据表明机器学习模型非常准确地预测样本外死亡率，即患者该模型从未见过。我们将确定死亡风险最高的患者 - 谁会受益大多数来自稀缺的姑息治疗资源，并与他们一起参加随机试验，将通常的初级保健与姑息治疗融合的初级保健进行比较。干预，姑息治疗临床医生的一系列家庭访问将与病人和初级保健团队。该策略专门旨在满足较旧的PA-的需求 Timent以及繁忙的初级保健临床医生。我们将为研究提供动力，以检测两个的变化主要结果：通过医院和紧急就诊来衡量的生活质量和护理强度。其他结果包括症状伯恩，高级护理计划，临终关怀和死亡率。该项目的含义将在姑息治疗的新模型上产生第一个证据患有多种慢性疾病的老年人在疾病轨迹中输送了“上游”。我们将建立针对老年人姑息治疗干预措施所需的技术和临床基础设施在基于特定疾病的计划之外的成年人。成功的审判将促进更广泛的采用 - 老年人相似的干预措施，并从根本上改变了规模和范围在这一人群中的姑息治疗工作。