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

项目摘要 背景：众所周知，姑息治疗可以改善患者的预后，减少医疗保健的利用率。 对癌症患者的治疗。但我们几乎不知道如何向广大民众提供姑息治疗 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 资源稀缺，因此护理必须针对受益最大的患者子集：那些 面临着最高的近期死亡风险。这是除特定疾病外的一个重大挑战 已知的轨迹。临床医生为预后而苦苦挣扎，目前的统计模型表现不佳。 目标我们将使用新的预测建模方法(机器学习)来识别复杂的老年人- 急诊室患者一年内死亡的风险很高，借鉴了我们团队之前在数据分析和 机器学习。我们将把这些方法应用于不同人群的老年患者，这些患者患有多- 许多慢性疾病，在一个大型的学术初级保健网络中。在我们队田径记录的基础上- 在一系列成功的临床试验中，我们将进行姑息治疗的随机对照试验。 配合初级保健，以预测死亡风险最高的老年患者为目标。我们会像- Sess对一系列可测量的患者报告的结果和卫生保健利用的影响。 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岁，使用来自电子健康记录的一组丰富的变量。我们的初步数据显示 机器学习模型对于样本外(即患者中)的死亡率预测非常准确 这位模特从未见过。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--并让他们参与一项随机试验， 将普通初级保健与初级保健与姑息治疗相结合。干预，一个 姑息治疗临床医生的一系列家访，将与 病人和初级保健团队。这一战略是专门为满足老年人的需求而设计的- 以及忙碌的初级保健临床医生。我们将推动这项研究，以检测两个方面的变化 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、高级护理计划、临终关怀使用和死亡率。 这一项目将为一种新的姑息治疗模式产生首个证据 患有多种慢性病的老年人在疾病发展轨迹中“逆流而上”。我们会 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 未参加特定疾病项目的成年人。一次成功的试验将促进更广泛的ADOP- 对老年人采取类似的干预措施，并从根本上改变 在这一人群中开展姑息治疗的努力。

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.