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: 8628813
• 负责人: Michael James Hassett
• 金额: $ 40.17万
• 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 2017-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8628813
• 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; 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; public health relevance; 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(申请人提供)：虽然一些癌症死亡可归因于原发病的进展，但许多(如果不是大多数)是由于早期疾病的成功最终治疗后出现的复发转移性癌症。大多数肿瘤登记，包括SEER，都不包括复发。因此，人们对晚期复发癌症的基于人群的发病率或护理模式和结果知之甚少。一种有效和可靠的算法用于识别管理数据中的此类复发，将使这种常见、昂贵和致命的疾病的比较有效性研究实现字面上的爆炸性增长。特别是，可用于识别行政数据中的复发的算法将使以下情况成为可能：(1)使用无病生存作为结果进行研究，以及(2)能够确定研究晚期复发疾病的护理模式和结果的初始队列。通过Dana-Farber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，使用了两个独特的数据集，其中包含与复发黄金标准数据相关联的完整声明。到目前为止，我们已经表明，已发表的复发识别策略在我们最近的基于总体的数据集中具有不可接受的低敏感性和特异性，并开发了一个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，然后估计复发发生的日期。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，然后应用它来生成与政策相关的数据，这些数据与复发的晚期癌症造成的公共健康负担有关。具体地说，我们将：(1)通过结合使用交叉验证估计和严格评估算法性能来完成用于检测非转移性肺癌、结直肠癌、乳腺癌和前列腺癌最终治疗后复发的候选算法的开发~(2)使用新的方法在几个全新的数据集中直接和间接验证算法~和(3)应用 经过验证的算法，以估计复发患者与确诊时出现晚期疾病的患者相比，可归因于复发的全原因死亡率的比例和每年的总护理成本。随着更多的癌症患者存活和存活时间更长，复发风险的人口也会增加。我们的算法将使新一代关于癌症护理的有效性、质量和结果的研究能够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 通过衡量这一状况对公共卫生和社会资源消耗的影响，充分利用这一机会。