Identifying Cancer Recurrence with Novel Data Linkages with a Cancer Registry

通过与癌症登记处的新数据关联来识别癌症复发

基本信息

批准号：
10522203
负责人：
MIA HASHIBE
金额：
$ 67.95万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10522203
关键词：
Accident and Emergency department Accreditation Algorithms Ambulatory Surgical Procedures American College of Surgeons Back Biochemical Breast Cancer Patient Cancer Survivor Caring Cause of Death Characteristics Clinical Trials Code Collection Computerized Medical Record Custom Data Data Collection Data Linkages Data Sources Databases Death Certificates Denmark Diagnosis Disease-Free Survival Ethnic Origin Evaluation Event Fright Generations Gold Health Health Maintenance Organizations Health care facility Hispanic Populations Hospitals Incidence Individual Inpatients Link Malignant Neoplasms Malignant neoplasm of prostate Medical Records Medicare Medicare claim Methods National Cancer Institute National Cancer Program Pathology Report Patients Performance Population Population Database Population Study Positioning Attribute Predictive Value Procedures Prostate Protocols documentation Race Recurrence Registries Reporting Research Research Personnel Risk Rural Rural Population SEER Program Source Stress Structure Training Treatment Effectiveness Treatment Efficacy Utah Validation Woman Work base breast cancer registry cancer recurrence cancer site cancer type clinical practice cost data registry data streams ethnic diversity health record improved learning algorithm malignant breast neoplasm men multiple data sources neoplasm registry novel patient registry population based prediction algorithm primary endpoint public database racial diversity sound survivorship

项目摘要

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

抽象的对于当今美国估计的1700万癌症存活，对复发的恐惧是一个重要的来源压力和驱动生存护理的问题。了解癌症复发的范围幸存者可以为临床实践提供信息，改善患者健康并允许对治疗的现实评估效力。关于癌症复发的人口级数据很难捕获，需要评估多个数据源可准确识别癌症的回报。犹他州癌症注册表（UCR），先知自1973年以来的注册表处于强烈的位置，可以在基于人群的环境中确定回报。注册表数据链接到犹他州人口数据库（UPDB），其中包括电子病历（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），Medicare）。我们建议评估使用数据的实用性所有州癌症注册表中常见的来源，并研究新型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，以识别其他癌症类型的复发，以估计人口级复发的伯恩。我们的具体目的如下：1）确定预测性使用当前可用于乳腺癌和乳腺癌注册表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，电子病理学的佣金报告和死亡证明。 2）估计预测性能的改进以识别通过纳入乳腺癌和前列腺癌的新型行政数据联系来复发。 3）评估复发性的可伸缩性和可运输性识别跨环境和研究人群。我们将通过估计阳性和来自西雅图的大致和种族多样性收集的癌症病例中的负预测值 Puget Sound Seer注册表，包括在种族/种族，年龄，舞台和粗糙/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犹他州乳腺癌的西雅图注册中心。目前没有算法来评估数据单独并合并来确定基于癌症注册表和行政的复发事件数据。我们的结果将为常规可用数据的预测性能提供信息，以及行政数据源，这些数据源可能是差异化的和/或昂贵的。我们的工作将为癌症复发和数据紧急优先级的人群水平跟踪建立前进的道路可以根据不同情况下可用的数据自定义的生成工作和算法。 1