Cancer Recurrence: Detection in Administrative Data, Incidence, and Costs

癌症复发：管理数据、发病率和成本的检测

• 批准号: 8815117
• 负责人: Michael James Hassett
• 金额: $ 47.4万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2016-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8815117
• 关键词: Accounting; Advanced Malignant Neoplasm; Age; Algorithms; Area Under Curve; Cancer Care Outcomes Research and Surveillance Consortium; Cancer Etiology; Cancer Patient; Cancer Research Network; Caring; Cessation of life; Code; Collaborations; Colorectal Cancer; Comorbidity; Confidence Intervals; Consumption; Dana-Farber Cancer Institute; Data; Data Set; Data Sources; Detection; Development; Diagnosis; Disease; Disease-Free Survival; Disseminated Malignant Neoplasm; Early treatment; Effectiveness; Event; Explosion; Funding; Generations; Gold; Grant; Health; Healthcare; Incidence; Informatics; Insurance; Interdisciplinary Study; Link; Location; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of prostate; Maps; Measures; Medical Care Costs; Medicare; Medicare claim; Methods; Outcome; Patient Care; Patients; Pattern; Performance; Phase; Policies; Populations at Risk; Predictive Value; Probability; Process; Public Health; Publishing; Recurrence; Recurrent Malignant Neoplasm; Recurrent disease; Registries; Research; Research Infrastructure; Research Personnel; Resources; Second Primary Cancers; Sensitivity and Specificity; Sentinel; Solid Neoplasm; Staging; Statistical Models; System; Update; Validation; Work; advanced disease; attributable mortality; base; cancer care; cancer recurrence; cohort; comparative effectiveness; cost; effectiveness research; experience; malignant breast neoplasm; mortality; novel; patient population; population based; tumor registry; virtual

DESCRIPTION (provided by applicant): While some cancer deaths are attributable to progression of the primary disease, many, if not the majority, are due to recurrent metastatic cancer that develops after successful definitive therapy for earlier stage disease. Most tumor registries, including SEER, do not capture recurrence. Therefore, remarkably little is known about the population-based incidence or patterns and outcomes of care for advanced recurrent cancer. A valid and reliable algorithm for identifying such recurrences in administrative data would enable a literal explosion of comparative effectiveness research on this common, costly, and lethal condition. In particular, an algorithm that could be used to identify recurrence in administrative data would make it possible to (1) conduct studies using disease-free survival as an outcome, and (2) would enable the identification of inception cohorts in whom to study patterns and outcomes of care for advanced recurrent disease. Through an existing multidisciplinary collaboration between Dana-Farber Cancer Institute and Cancer Research Network investigators, we have made considerable progress on the development of a recurrence algorithm, working in two unique data sets that contain complete claims linked to gold standard data on recurrence. To date, we have shown that published recurrence identification strategies have unacceptably low sensitivity and specificity in our recent, population-based data sets, and have developed a highly promising two-phase probabilistic model that first determines the probability of recurrence and then estimates the date on which it occurred. We now propose to build on this work, conducting further development and validation of the algorithm, and then applying it to generate policy-relevant data on the public health burden imposed by recurrent advanced cancer. Specifically, we will: (1) complete the development of a candidate algorithm for detecting recurrence after definitive therapy of non-metastatic lung, colorectal, breast, and prostate cancer by incorporating use of cross-validation estimates and rigorously assessing algorithm performance~ (2) employ novel methods to directly and indirectly validate the algorithm in several entirely new data sets~ and (3) apply the validated algorithm to estimate the proportion of all-cause mortality attributable to recurrence and the total annualized costs of care for patients with recurrent disease, compared to patients presenting with advanced disease at diagnosis. As more cancer patients survive and survive longer, the population at risk for recurrence increases. Our algorithm will enable a new generation of research on the effectiveness, quality, and outcomes of cancer care that takes into account this sentinel event in the cancer trajectory. In our applied studies, we will begin to capitalize on this opportunity by measuring the impact of this condition on the public health and the consumption of societal resources.

描述（由申请人提供）：虽然一些癌症死亡可归因于原发性疾病的进展，但许多（如果不是大多数）是由于早期疾病成功确定治疗后出现的复发性转移性癌症所致。大多数肿瘤登记处（包括 SEER）不记录复发情况。因此，人们对晚期复发癌症的人群发病率、模式和治疗结果知之甚少。用于识别管理数据中此类重复发生的有效且可靠的算法将使针对这种常见、昂贵且致命的情况的比较有效性研究真正爆发式增长。特别是，一种可用于识别管理数据中复发的算法将使得（1）使用无病生存作为结果进行研究成为可能，（2）将能够识别初始队列，以研究晚期复发性疾病的护理模式和结果。通过丹娜—法伯癌症研究所和癌症研究网络研究人员之间现有的多学科合作，我们在复发算法的开发方面取得了相当大的进展，研究了两个独特的数据集，其中包含与复发金标准数据相关的完整声明。迄今为止，我们已经表明，已发表的复发识别策略在我们最近的基于人群的数据集中具有令人无法接受的低敏感性和特异性，并且开发了一种非常有前途的两阶段概率模型，该模型首先确定复发的概率，然后估计其发生的日期。我们现在建议在这项工作的基础上，对算法进行进一步的开发和验证，然后应用它来生成有关复发性晚期癌症造成的公共卫生负担的政策相关数据。 具体来说，我们将：（1）通过结合交叉验证估计和严格评估算法性能，完成用于检测非转移性肺癌、结直肠癌、乳腺癌和前列腺癌根治性治疗后复发的候选算法的开发~（2）采用新方法在几个全新的数据集中直接和间接验证算法~以及（3）应用 经验证的算法可估计复发性疾病引起的全因死亡率的比例，以及与诊断时患有晚期疾病的患者相比，复发性疾病患者的年化总护理费用。随着越来越多的癌症患者存活下来并且存活时间越来越长，复发风险的人群也在增加。我们的算法将能够对癌症护理的有效性、质量和结果进行新一代研究，并考虑到癌症轨迹中的这一哨兵事件。在我们的应用研究中，我们将开始 通过衡量这种情况对公共卫生和社会资源消耗的影响来利用这一机会。