Improving the prediction of life expectancy among older adults with advanced cancer using geriatric assessment

摘要 预测晚期（IV期或无法治愈的III期）癌症老年人的预期寿命具有挑战性 由于慢性疾病和老年综合征的异质性， 增加死亡的风险。对预期寿命的准确预测和沟通是 为患有晚期癌症的老年人及其提供者共同决策。然而，证据显示， 肿瘤学家倾向于高估老年晚期癌症患者的预期寿命。标准工具 目前用于预测预期寿命的方法在这一人群中的表现很差。老年评估是一个 一组经验证的、患者报告的客观指标，用于评估合并症、功能状态、身体状况 性能、认知状态、心理状态、营养状态、多种药物和社会支持，以及 有可能改善对晚期癌症老年人预期寿命的预测。然而，没有研究 明确寻求利用从老年评估中获取的信息来开发临床 老年晚期癌症患者的预期寿命预测模型。这项研究将利用详细的 来自老年评估和先进统计方法的信息，以开发和验证新生活 预期预测模型的老年人（70岁及以上）与先进的癌症。我们将评估 这种新的遗传算法模型的预测性能的改善程度相比，肿瘤学家的自我， 报告的预期寿命估计值和基于常规收集的临床数据（年龄， 合并症和性能状态）。为了实现这些目标，我们将把最新的生命状态 来自医疗保险计划的数据，对>800名70岁及以上患者进行了详细的老年病评估 晚期癌症患者及其肿瘤学家参与了两项基于社区的随机分组试验。 我们的多学科研究团队在数据链接，预测建模，老年医学， 肿瘤学和姑息治疗进行这项研究，并将我们的发现转化为有意义的进展， 这个弱势群体。我们的工作将导致开发一种临床预测工具，可用于 未来的实验工作，以测试是否预期寿命预测模型的基础上，老年评估 可以改善患者与提供者关于预后的沟通，并提供高质量的、以患者为中心的 癌症护理

ABSTRACT Predicting life expectancy in older adults with advanced (stage IV or incurable stage III) cancers is challenging due to heterogeneity in the presence of chronic conditions and geriatric syndromes, which independently increase the risk of death. Accurate prediction and communication of life expectancy is a key component of shared decision-making for older adults with advanced cancers and their providers. However, evidence shows that oncologists tend to overestimate life expectancy in older adults with advanced cancer. Standard tools currently available for predicting life expectancy perform poorly in this population. The geriatric assessment is a set of validated, patient-reported and objective measures to assess comorbidity, functional status, physical performance, cognitive status, psychological status, nutritional status, polypharmacy, and social support, and has potential to improve prediction of life expectancy for older adults with advanced cancer. Yet, no studies have explicitly sought to harness information captured from the geriatric assessment to develop a clinical prediction model for life expectancy in older adults with advanced cancers. This study will leverage detailed information from geriatric assessment and advanced statistical methods to develop and validate a new life expectancy prediction model for older adults (age 70 and over) with advanced cancer. We will then evaluate the extent of improvement in predictive performance of this new GA model compared with oncologists’ self- reported estimates of life expectancy and a model based on routinely collected clinical data (age, comorbidities, and performance status). To achieve these aims, we will link the most up-to-date vital status data from the Medicare program with detailed geriatric assessments for >800 patients aged 70 years and older with advanced cancer and their oncologists participating in two community-based, cluster-randomized trials. Our multidisciplinary study team has the requisite expertise in data linkage, predictive modeling, geriatric oncology, and palliative care to conduct this research and translate our findings into meaningful advances for this vulnerable population. Our work will lead to the develop of a clinical prediction tool that can be used in future experimental work to test whether the life expectancy prediction model based on geriatric assessment can improve patient-provider communication about prognosis and the delivery of high-quality, patient-centered cancer care.