Abramson Cancer Center Support Grant

艾布拉姆森癌症中心支持补助金

基本信息

批准号：
10408409
负责人：
ROBERT H VONDERHEIDE
金额：
$ 20万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10408409
关键词：
Address Administrative Supplement African American Age Algorithms Area Cancer Center Cancer Center Support Grant Cancer Etiology Cessation of life Clinic Clinical Code Colonoscopy Colorectal Adenoma Colorectal Cancer Communities Consumption County Data Detection Diagnosis Disease Effectiveness Electronic Health Record Family Practice Frequencies Goals Guidelines Hispanics Immunochemistry Immunohistochemistry Incidence Income Individual Instruction Internal Medicine Intervention Intestines Knowledge Machine Learning Malignant Neoplasms Medicine Methods Minority Mission Modeling Outcome Patient Care Patients Pennsylvania Performance Philadelphia Poverty Poverty Areas Preparation Primary Health Care Process Program Effectiveness Public Health Recording of previous events Relative Risks Research Research Personnel Research Proposals Resources Retrospective cohort Risk Risk Factors Screening for cancer Services Telephone Testing Time Training Transportation United States Universities adenoma adherence rate base cancer diagnosis cancer risk cohort colon cancer screening colorectal cancer prevention colorectal cancer risk colorectal cancer screening community living design effectiveness evaluation ethnic diversity ethnic minority population evidence base feasibility testing high risk high risk population improved machine learning algorithm minority communities mortality novel patient population programs racial diversity racial minority risk stratification screening screening guidelines social social factors social health determinants socioeconomics success transportation access underserved area usability

项目摘要

PROJECT SUMMARY/ABSTRACT Colorectal cancer is the 4th most common cancer diagnosed and the 2nd most common cause of cancer death in the United States. The age-adjusted incidence of CRC in Philadelphia County – a persistently high poverty area– is nearly 25% above the national average. Guideline-based screening for CRC via colonoscopy or fecal immunohistochemistry (FIT) reduces CRC-associated mortality. Despite the proven benefit of regular colonoscopy, CRC screening completion rates remain only around 50% in Philadelphia County and are consistently 10-15% lower for African-Americans than White individuals living in Philadelphia County. Adverse social determinants of health such as high poverty contribute to CRC screening nonadherence disproportionately for African-American and other minority communities. Recognizing this, in 2011, Penn Medicine created a navigation program to increase access to screening colonoscopies for patients in underserved areas of West, South, and Southwest Philadelphia by providing services that reduce barriers to cancer screening, including transportation assistance and detailed instructions on bowel prep. Despite initial success in increasing colonoscopies, a key challenge in scaling this navigation program is identifying patient populations at increased risk of CRC, who may benefit most from timely navigation. Automated machine learning (ML) algorithms based on routine electronic health record (EHR) data accurately estimate a patient’s relative risk of CRC. High-risk individuals may be particularly motivated to comply with disease screening recommendations and be targeted with an effective but resource-constrained navigator program. The overarching goals of this Administrative Supplement is to support the Abramson Cancer Center (ACC) mission to increase colorectal cancer (CRC) screening completion among high-risk individuals living in a persistent poverty county by designing, conducting, disseminating and evaluating an electronic health record- based automated identification program to target effective, culturally-sensitive CRC screening navigation to individuals who have not completed an ordered colonoscopy or fecal immunochemical test (FIT). Specifically, the goals of this supplement are to: 1) Adapt a previously validated EHR-based machine learning algorithm to predict CRC detection by retraining the model using data from patients seen in primary care clinics serving zip codes with a high proportion of racial and ethnic minorities living in Philadelphia County, a persistent poverty county; and 2) Implement and evaluate the feasibility and effectiveness of an algorithm-based CRC navigation program to increase colorectal cancer screening among 344 patients seen at one of 7 primary care practices within Philadelphia county who are at high risk of CRC, have uncompleted colonoscopies. Together, these projects aim to increase evidence-based screening in order to reduce the burden of CRC among high-risk individuals living in a persistent poverty county by utilizing evidence-based, targeted, culturally-sensitive CRC screening navigation that in part addresses social factors that prevent colorectal cancer screening.

项目概要/摘要结直肠癌是第四大常见癌症，也是第二大癌症死亡原因美国费城县的 CRC 发病率持续偏高。区域——比基于指南的结肠镜检查或粪便筛查 CRC 的全国平均水平高出近 25%。尽管免疫组织化学 (FIT) 已被证明具有一定的益处，但仍可降低 CRC 相关死亡率。费城县的结肠镜检查、结直肠癌筛查完成率仍仅为 50% 左右，并且居住在费城县的非裔美国人的死亡率始终低于白人 10-15%。高度贫困等健康问题社会决定因素导致不遵守CRC筛查 2011 年，宾夕法尼亚大学认识到这一点，对非裔美国人和其他少数族裔群体的影响不成比例。医学界创建了一个导航程序，以增加患者接受结肠镜检查的机会通过提供减少障碍的服务，为费城西部、南部和西南部服务欠缺的地区提供服务癌症筛查，包括交通援助和肠道准备的详细说明（尽管最初）。成功增加结肠镜检查，扩大该导航计划的一个关键挑战是识别患者 CRC 风险较高的人群可能会从及时的自动化导航中受益最多。基于常规电子健康记录 (EHR) 数据的学习 (ML) 算法可准确估计患者的健康状况 CRC 的相对风险可能特别有动力遵守疾病筛查。建议并以有效但资源有限的导航计划为目标。本行政补充文件的总体目标是支持艾布拉姆森癌症中心 (ACC) 使命是提高居住在高危人群中的结直肠癌 (CRC) 筛查完成率通过设计、实施、传播和评估电子健康记录来帮助持续贫困县基于自动识别程序的目标是有效性、文化敏感的 CRC 筛查导航尚未完成预约的结肠镜检查或粪便免疫化学测试 (FIT) 的个人。本补充的目标是： 1) 采用先前经过验证的基于 EHR 的机器学习算法来通过使用在提供 zip 服务的初级保健诊所就诊的患者的数据重新训练模型来预测 CRC 检测居住在费城县的少数族裔比例很高，长期贫困县；以及 2) 实施并评估基于算法的 CRC 导航的可行性和有效性一项旨在加强对 7 个初级保健机构之一就诊的 344 名患者进行结直肠癌筛查的计划费城县内患有 CRC 高风险的人未完成结肠镜检查。项目旨在加强循证筛查，以减轻高危人群的结直肠癌负担利用循证、有针对性、文化敏感的《儿童权利公约》帮助生活在持续贫困县的个人筛查导航部分解决了阻碍结直肠癌筛查的社会因素。