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）：虽然某些癌症死亡归因于原发性疾病的进展，但许多（即使不是多数）是由于在成功的早期阶段疾病的成功疗法后发展的复发转移性癌。大多数肿瘤登记处，包括先知，都不会捕获复发。因此，对于晚期复发性癌症的基于人群的发病率，模式和护理结果知之甚少。用于识别行政数据中此类复发的有效且可靠的算法将使对这种常见，昂贵和致命状况的比较有效性研究的字面爆炸。特别是，可以用来识别行政数据中复发的算法将使（1）以（1）使用无病生存作为结果进行研究，（2）将能够鉴定建立同类群体，以研究其研究模式和护理结果，以确定患有晚期疾病。通过Dana-Farber癌症研究所与癌症研究网络研究者之间的现有多学科合作，我们在复发算法的开发方面取得了长足的进步，在两个独特的数据集中运行，其中包含与复发金标准数据有关的完整索赔。迄今为止，我们已经表明，在我们最近的基于人群的数据集中，已发表的复发识别策略具有不可接受的低灵敏度和特异性，并开发了一种高度有希望的两相概率模型，该模型首先确定复发的可能性，然后估计发生的日期。现在，我们建议以这项工作为基础，对算法进行进一步的开发和验证，然后将其应用于与经常性晚期癌症施加的公共卫生负担有关的与政策相关的数据。 具体而言，我们将：（1）完成对非中性肺，结直肠癌，乳腺癌和前列腺癌进行确定治疗后检测复发性的候选算法的开发，该算法通过使用交叉验证估计并进行了严格评估算法性能〜（2）直接和间接验证Algorith的新方法，以实现交叉验证估计，并在数量上进行了数量〜（2） 与出现诊断后患有晚期疾病的患者相比，经过验证的算法以估计归因于复发性的全因死亡率和复发性疾病患者的年度护理成本的比例。随着越来越多的癌症患者的生存和生存更长的时间，重复出现的风险增加。我们的算法将对癌症护理的有效性，质量和结果进行新一代研究，这些研究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费的影响来利用这一机会。