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

项目摘要 背景姑息治疗是众所周知的，以改善病人的结果，减少卫生保健利用率。 对癌症患者的影响。但我们对如何为广大民众提供姑息治疗知之甚少， 患有多种慢性病的老年患者人数不断增加。姑息治疗临床医生是一个 稀缺的资源，所以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特定疾病以外的一个重大挑战， 已知的轨迹。临床医生与预后斗争，目前的统计模型表现不佳。 我们将使用新的预测建模方法（“机器学习”）来识别复杂的旧的， 一年死亡率高风险的患者，利用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不同人群的老年患者， 在一个大型的学术初级保健网络中。基于我们队的记录- 为了成功的临床试验，我们将进行一项姑息治疗的随机对照试验， 与初级保健相结合，针对死亡风险最高的老年患者。我们会- sess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集。我们的初步数据显示， 机器学习模型对于预测样本外死亡率是高度准确的，即，患者 模特从未见过。我们将确定死亡风险最高的患者-谁将受益 大多数来自稀缺的姑息治疗资源-并接近他们参加一项随机试验， 将普通初级保健与初级保健与姑息治疗相结合进行比较。干预，a 姑息治疗临床医生的一系列家庭访问，将建立一个纵向关系， 患者和初级护理团队。这一战略是专门为满足老年人的需要而设计的。 tients以及忙碌的初级保健临床医生。我们将为这项研究提供动力， 主要结果：生活质量和护理强度，通过医院和急诊来衡量。 其他结果包括症状负担，先进的护理计划，临终关怀的使用和死亡率。 该项目将产生关于姑息治疗新模式的第一个证据， 患有多种慢性病的老年人，在疾病轨迹的“上游”传播。我们将 建立针对老年人姑息治疗干预所需的技术和临床基础设施 成人以外的具体疾病为基础的方案。一个成功的试验将促进更广泛的adop- 为老年人提供类似的干预措施，并从根本上改变老年人的规模和范围。 在这一人群中进行姑息治疗。

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.