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.

项目概要众所周知，背景姑息治疗可以改善患者的治疗效果并减少医疗保健的利用率但我们对如何向广大癌症患者提供姑息治疗知之甚少。患有多种慢性病的老年患者数量不断增加。资源稀缺，因此护理必须针对最受益的患者子集：近期死亡的风险最高，这是除特定疾病之外的一项重大挑战。临床医生在预后方面遇到了困难，而当前的统计模型表现不佳。目标我们将使用新颖的预测建模方法（“机器学习”）来识别复杂的旧模型呃患者一年内死亡风险很高，利用我们团队之前在数据分析和我们将把这些方法应用于患有多种疾病的不同老年患者群体。在我们团队的跟踪记录的基础上，我们建立了一个大型学术初级保健网络。在一系列成功的临床试验之后，我们将进行一项关于姑息治疗的随机对照试验我们将针对预测死亡风险最高的老年患者进行初级护理评估。评估对一系列可衡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来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。机器学习模型对于预测样本外死亡率（即患者死亡率）非常准确我们将识别出死亡风险最高的患者——谁将从中受益。大部分来自稀缺的姑息治疗资源，并让他们参与随机试验，将常规初级保健与结合姑息治疗的初级保健进行比较。姑息治疗参议员的一系列家访，将与姑息治疗建立纵向关系该策略是专门为满足老年人的需求而设计的。我们将推动这项研究来检测两个方面的变化。主要结果：通过医院和急诊就诊衡量的生活质量和护理强度。其他结果包括症状负担、高级护理计划、临终关怀服务的使用和死亡率。影响该项目将为姑息治疗新模式提供第一个证据患有多种慢性疾病的老年人，将在疾病轨迹中“逆流而上”。建立针对老年人的姑息治疗干预所需的技术和临床基础设施成功的试验将促进更广泛的采用。对老年人采取类似的干预措施，并从根本上改变干预措施的规模和范围该人群的姑息治疗工作。