Identifying Cancer Recurrence with Novel Data Linkages with a Cancer Registry

摘要 对于今天美国估计的1700万癌症幸存者来说，对复发的恐惧是他们死亡的主要原因。 压力和一个推动生存护理的问题。了解癌症复发的范围 幸存者可以告知临床实践，改善患者健康，并允许对治疗进行真实世界的评估 有效性关于癌症复发的人群水平数据难以获取，需要评估 多个数据源，以准确识别癌症复发。犹他州癌症登记处（UCR），SEER 登记处自1973年以来，是强有力的定位，以确定复发性在人口为基础的设置。书记官处 数据链接到犹他州人口数据库（EIB），其中包括电子医疗记录（EMR）， 全州医疗机构数据（SHFD;住院、门诊手术和急诊科），以及 索赔数据（所有付款人索赔数据库（APCD），医疗保险）。我们建议评估使用数据的效用 所有国家癌症登记处的共同来源，并调查新数据链接的附加值 可在犹他州癌症登记处获得。我们还建议扩展和验证最近开发的算法 确定个体水平的乳腺癌复发，确定其他癌症类型的复发， 人口水平的复发负担。我们的具体目标如下：1）确定预测 使用目前可用于乳腺癌登记的数据识别复发的性能， 前列腺癌这些将包括癌症复发变量委员会，电子病理学 报告和死亡证明2)估计预测性能的改进，以确定 通过纳入乳腺癌和前列腺癌的新管理数据关联来确定复发率。3)评价 跨设置的递归识别算法的可扩展性和可移植性， 人口研究。我们将通过估计正的和负的， 在来自西雅图的不同种族和人种的癌症病例中， Puget Sound SEER登记研究，包括不同人种/种族、年龄、分期和 农村/城市状况。此外，我们将验证最近开发的乳房复发识别算法 在西雅图登记的犹他州乳腺癌人群中。目前没有算法来评估数据 根据癌症登记和管理， 数据我们的研究结果将为常规可用数据的预测性能和 行政数据源，这些数据源可能不完全和/或采购成本高。我们的工作将 为癌症复发的人群水平跟踪建立前进的道路，并促进数据的优先排序 可以根据不同情况下可用的数据定制生成工作和算法。 1

ABSTRACT For the estimated 17 million cancer survivors in the US today, fear of recurrence is a substantial source of stress and an issue that drives survivorship care. Understanding the scope of recurrence among cancer survivors can inform clinical practice, improve patient health, and allow for real-world assessment of treatment effectiveness. Population-level data on cancer recurrence are difficult to capture, and require evaluation of multiple data sources to accurately identify cancer recurrences. The Utah Cancer Registry (UCR), a SEER registry since 1973, is strongly positioned to identify recurrences in a population-based setting. The registry data are linked to the Utah Population Database (UPDB), which includes electronic medical records (EMR), statewide healthcare facility data (SHFD; inpatient, ambulatory surgery and emergency department), and claims data (All Payer Claims Database (APCD), Medicare). We propose to assess the utility of using data sources common across all state cancer registries and to investigate the added value of novel data linkages available at the Utah Cancer Registry. We also propose to extend and validate a recently-developed algorithm to identify individual level breast cancer recurrence to identify recurrence for other cancer types to estimate the population-level burden of recurrence. Our specific aims are as follows: 1) Determine the predictive performance to identify recurrence using data currently available to cancer registries for breast and prostate cancer. These would include Commission on Cancer recurrence variables, electronic pathology reports, and death certificates. 2) Estimate the improvements in predictive performance to identify recurrence by inclusion of novel administrative data linkage for breast and prostate cancer. 3) Evaluate the scalability and transportability of recurrence identifying algorithms across settings and populations for research. We will validate the algorithms’ predictive performance by estimating positive and negative predictive values among a racially and ethnically diverse collection of cancer cases from the Seattle- Puget Sound SEER registry, including comparisons of performance across race/ethnicity, age, stage, and rural/urban status. In addition, we will validate the breast recurrence identification algorithm recently developed in the Seattle registry in the Utah breast cancer population. No algorithms currently exist to evaluate the data sources individually and combined to identify recurrence events based on cancer registry and administrative data. Our results will inform the predictive performance for routinely available data and the value added of administrative data sources, which may be differentially complete and/or costly to procure. Our work will establish a path forward for population-level tracking of cancer recurrence and facilitate prioritization of data generation efforts and algorithms that can be customized based on the data available in different situations. 1