Geriatric Assessment - Driven Interventions for Older Cancer Patients

描述（由申请人提供）：超过60%的癌症发生在老年人中，随着人口老龄化，老年癌症患者的数量预计会增加。老年癌症患者化疗并发症的风险增加，并且没有标准的方法来识别风险和实施干预措施以减少毒性。包括癌症和衰老研究小组（CARG）在500例患者中进行的研究在内的几项研究表明，46%的晚期癌症老年患者在治疗开始后3个月内发生了严重的化疗毒性，综合老年评估（GA）是一种评估老年人健康状况的有效方法，可以预测严重的化疗毒性。然而，关于如何将来自大会的信息转化为有可能预防或减少不良后果的有针对性的干预措施（例如，毒性、功能下降和生活质量降低）。 该提案的总体目标是确定老年评估（GA）驱动的干预措施，可以改善老年癌症患者的结果，创建一个算法来实施这些干预措施，并在随机试点研究中测试该算法。这项研究的总体假设是，对老年患者实施GA指导的干预措施的算法可以降低化疗毒性的风险，并维持或改善生活质量。主要研究者，老年肿瘤学家，以及通过CARG和资助的U13会议系列资助“老年肿瘤学研究以改善临床护理”组建的老年医学家，肿瘤学家和老年肿瘤学家的研究团队处于有利地位，能够成功完成这项高影响力的研究。在具体目标1中，我们将采用修改后的德尔菲共识建立过程，与第二届U13老年肿瘤学会议上召集的专家一起修订并最终确定GA驱动的癌症患者干预算法。在具体目标2中，我们将进行一项随机试点研究（n = 80），以收集有关GA驱动干预的算法是否可以改善老年癌症患者结局的初步数据。正在接受新化疗方案的70岁及以上晚期癌症患者将被随机分配到常规护理或老年肿瘤学团队的访视，该团队将在算法指导下实施GA驱动的干预措施。主要结局将是比较化疗开始后3个月发生重度化疗毒性的患者比例。次要结果将包括两组中实施的干预措施数量的比较以及生活质量、功能能力和身体表现的比较。由PI领导的CARG研究团队将在罗切斯特大学社区临床肿瘤学项目（CCOP）的计划R01中纳入该提案的结果，该项目将测试GA驱动的干预措施的实施是否可以降低接受化疗的老年癌症患者的毒性并维持其生活质量。

DESCRIPTION (provided by applicant): Over 60% of cancers occur in older persons, and the number of older cancer patients is expected to grow as the population ages. Older cancer patients are at increased risk of chemotherapy treatment complications, and there is no standard approach to identifying risk and implementing interventions to reduce toxicity. Several studies, including a Cancer and Aging Research Group (CARG) study in 500 patients, have demonstrated that 46% of older patients with advanced cancer have severe toxicity from chemotherapy within 3 months of treatment initiation and that comprehensive geriatric assessment (GA), a validated approach to assessing health status in older persons, can predict severe chemotherapy toxicities. No consensus exists, however, on how to translate information from the GA into targeted interventions that have potential to prevent or reduce adverse outcomes (e.g., toxicity, functional decline, and lower quality of life) for older cancer patients. The overarching goal of this proposal is to identify geriatric assessment (GA)-driven interventions that could improve outcomes of older adults with cancer, to create an algorithm to implement these interventions, and to test the algorithm in a randomized pilot study. The overall hypothesis of this proposed research is that an algorithm that implements interventions guided by GA for older patients can reduce the risk of chemotherapy toxicity and maintain or improve quality of life. The principal investigator, a geriatric oncologist, and the research team of geriatricians, oncologists, and geriatric oncologists assembled through CARG and a funded U13 conference series grant, "Geriatric Oncology Research to Improve Clinical Care" are well positioned to successfully complete this high-impact research. In specific aim 1, we will employ a modified Delphi consensus-building process with experts convened at the second U13 geriatric oncology conference to revise and finalize an algorithm for GA-driven interventions for cancer patients. In specific aim 2, we will conduct a randomized pilot study (n=80) to gather preliminary data on whether this algorithm for GA-driven interventions can improve outcomes of older cancer patients. Patients aged 70 and over with advanced cancer who are embarking on a new chemotherapy regimen will be randomized to usual care or a visit with a geriatric oncology team who will implement GA-driven interventions guided by the algorithm. The primary outcome will be a comparison of the proportion of patients who have severe chemotherapy toxicity at 3 months after chemotherapy initiation. Secondary outcomes will include a comparison of the number of interventions implemented in both groups and comparisons of quality of life, functional abilities, and physical performance. The CARG research team, led by the PI, will incorporate the results of this proposal in a planned R01 within the University of Rochester's Community Clinical Oncology Program (CCOP) that will test whether implementation of GA-driven interventions can reduce toxicity and maintain quality of life of older cancer patients receiving chemotherapy.