Optimizing Treatment Decision Making for Patients with Localized Renal Mass

优化局部肾脏肿块患者的治疗决策

基本信息

批准号：
10734606
负责人：
Stephen Hembree Culp
金额：
$ 39.61万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10734606
关键词：
Address Affect Age Algorithms Benign Biopsy California Cancer Control Cancer Intervention Characteristics Clinical Collaborations Communities Community Practice Complex Confusion Data Data Analyses Data Collection Data Set Databases Decision Making Detection Development Diagnosis Disease Progression Expert Opinion Face Feedback Future Gender Geography Goals Health Health Care Costs Health system Healthcare Heating Histology Hospitals Image Impairment Individual Institution Internet Kidney Learning Left Life Malignant - descriptor Malignant Neoplasms Medical Medical center Modality Modeling Needles Nephrectomy Online Systems Operative Surgical Procedures Outcome Pathologic Pathway interactions Patient Preferences Patient-Focused Outcomes Patients Persons Process Provider Quality of life Recommendation Renal Cell Carcinoma Renal Mass Renal function Resources Rest Risk San Francisco Secure Surveys System Thermal Ablation Therapy Translations Triage Uncertainty Universities Urologist Validation Variant Virginia Wisconsin Work cancer cell cohort community partnership comorbidity cost cost comparison expectation functional decline geographic difference health care service utilization individual patient intervention cost kidney cell markov model multidisciplinary novel therapeutics patient oriented patient variability personalized care personalized management preservation prospective prototype shared database shared decision making side effect tool treatment effect treatment optimization treatment planning tumor usability web based interface web-based tool willingness

项目摘要

Project Summary Kidney cancer, or renal cell carcinoma (RCC), is one of the 10 most common cancers in the US. In 2022, over 79,000 people will be diagnosed with RCC. Localized renal masses (LRM, tumors confined to the kidney) make up two-thirds of all RCC. Patients found to have a LRM face many choices on what to do, especially since it is not uncommon for these LRM to be benign. There are currently four ways to manage LRM – active surveillance (watching the mass but not treating it), thermal ablation (heating the mass by putting a needle in it from the outside), partial nephrectomy (surgery to cut the mass out but leave the rest of the kidney in place), and radical nephrectomy (surgery to remove the entire kidney). Patients and doctors are often confused as to which option to pursue since there have been few previous studies to guide them. It is currently recommended that the kidney be preserved in patients who may have future problems with their kidney function. However, it is hard to identify these patients, especially when they have other health issues. Current recommendations are based on “expert opinion” and it is typically left up to the doctor to decide what is best for the patient in terms of risk to the kidney and whether the cancer threatens the life of the patient over other medical conditions the patient may have. The primary goal of our study is to identify specific management plans that can be individualized for a patient with a LRM. This will make it easier for the doctor and patient to decide on the best way to manage the mass. By using a tool built with real-world information, the “trade- off” with each option (like kidney function) will be clearer to the patient, thereby helping them make the best decision. We will create an internet-based database shared between three different major academic hospitals. We will collect information about the health of the patient, how good their kidney function is, and the details of the LRM. We will assess how often biopsies are done prior to treatment of the LRM and what treatments patients tend to pursue. We will develop a set of rules for management of a LRM using a Markov decision process model and real-world patient information. We will then use this model to identify treatment decisions that are in line with patient- driven goals, including what is best for their quality of life, and approaches that minimize health care cost. Our work will enable patients and their doctors to participate in a shared-decision making process. We will create a web-based tool and assess the feasibility of community urologists helping with gathering patient information and collaboration to optimize patient management (i.e., community versus academic setting).

项目摘要肾癌或肾细胞癌（RCC）是10种最常见的癌症之一美国。 2022年，将有79,000多人被诊断出患有RCC。局部肾脏肿块（LRM，仅限于肾脏的肿瘤占所有RCC的三分之二。患者发现患有LRM脸关于做什么的许多选择，尤其是因为这些LRM的良性并不少见。当前有四种管理LRM的方法 - 主动监视（观看群众，但不是治疗它），热消融（通过从外部放一根针加热质量），部分肾切除术（将肿块切除但将其余的肾脏切除的手术）和自由基肾切除术（手术以切除整个肾脏）。病人和医生经常感到困惑由于以前几乎没有研究指导它们，因此可以购买哪种选择。目前是建议将肾脏保存在可能将来有问题的患者中肾功能。但是，很难识别这些患者，尤其是当他们还有其他健康问题。当前的建议是基于“专家意见”，通常是剩下的向医生决定哪种最适合患者的肾脏风险以及是否癌症威胁了患者可能患者可能患者的生命。我们研究的主要目标是确定可以是为患有LRM的患者个性化。这将使医生和病人更容易决定以管理质量的最佳方式。通过使用带有现实世界信息的工具，“交易 - 随着每个选项（例如肾功能），对患者的清晰最好的决定。我们将创建一个基于Internet的数据库，共享三个不同的主要专业学术医院。我们将收集有关患者健康的信息，他们的健康肾功能是LRM的细节。我们将评估活检之前的频率治疗LRM以及患者倾向于接受哪些治疗方法。我们将开发一组使用马尔可夫决策过程模型和现实世界患者管理LRM的规则信息。然后，我们将使用此模型来确定与患者一致的治疗决策 - 驱动的目标，包括最适合其生活质量的目标，以及使健康最小的方法护理费用。我们的工作将使患者及其医生能够参加共享决定制作过程。我们将创建一个基于网络的工具并评估社区的可行性泌尿科医生帮助收集患者信息和协作以优化患者管理（即社区与学术环境）。