Examine the Effectiveness of Cancer Screening Among Patients with Alzheimer’s Disease and Related Dementias Using Real-World Data

使用真实世界数据检查阿尔茨海默病和相关痴呆症患者癌症筛查的有效性

基本信息

  • 批准号:
    10284993
  • 负责人:
  • 金额:
    $ 38.13万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-01-01 至 2023-12-31
  • 项目状态:
    已结题

项目摘要

ABSTRACT Detecting cancer early through screening substantially increases the chance of successful treatment, and thus provides the best opportunity for reducing cancer mortality and alleviating cancer burden in the population. National professional associations such as the US Preventive Services Task Force (USPSTF) and the American Cancer Society have developed screening guidelines for various types of cancers based on extensive reviews of scientific evidence. These guidelines identify cancer screening-eligible populations and provide recommendations on the type and frequency of screening tests. However, even with the evidence- based screening guidelines, a decision to undergo cancer screening is not always straightforward, especially for patients with Alzheimer's disease and related dementias (ADRD). ADRD patients suffer from poor functional status, comorbid illnesses, and shortened life expectancy. Screening ADRD patients for cancer may lead to over-diagnosis and treatment of cancers that otherwise would not have caused symptoms during a patient’s lifetime. On the other hand, the key evidence on the effectiveness of a cancer screening test is the reduction in late-stage cancer incidence and mortality, which is often obtained in randomized clinical trials (RCTs). However, RCTs of cancer screening require a long follow-up. Conducting these RCTs in ADRD patients, a population of compromised physical and cognitive abilities, can be logistically, financially, and ethically challenging. To date, it remains unclear whether cancer screening can actually reduce mortality in this population. In our parent award R01 CA246418, we have developed research-grade computable phenotypes (CP) for identifying individuals who are eligible or ineligible for lung cancer screening (LCS) using both structured and unstructured EHR data in the OneFlorida network. We will soon begin to examine the utilization patterns and predictors of appropriate and inappropriate LCS, and estimate the benefits and cost- effectiveness of LCS. OneFlorida is one of the 9 PCORI-funded research networks contributing to the national PCORnet. It contains ~15 million patient EHRs linked with data from various other sources, including Medicaid and Medicare claims, cancer registries, and vital statistics. In this administrative supplement, we propose to identify a retrospective cohort of ADRD patients in OneFlorida to: (1) examine the utilization patterns and predictors of lung, breast, and colorectal cancer screening among ADRD patients and (2) test whether cancer screening reduces late-stage cancer incidence and mortality among ADRD patients. This study will be the first to examine the effectiveness of cancer screening in reducing late-stage cancer diagnosis and mortality in ADRD patients. Given the infeasibility of RCTs, our study will provide critical evidence that can help clinicians, caregivers, and families understand the effectiveness of cancer screening in quantitative terms and decide whether cancer screening is useful in ADRD patients.
摘要 通过筛查早期发现癌症大大增加了成功治疗的机会, 为降低癌症死亡率和减轻人口癌症负担提供了最佳机会。 国家专业协会,如美国预防服务工作队(USPSTF)和 美国癌症协会已经制定了各种类型癌症的筛查指南, 对科学证据的广泛审查。这些指南确定了符合癌症筛查条件的人群, 提供有关筛选测试的类型和频率的建议。但是,即使有证据- 根据筛查指南,进行癌症筛查的决定并不总是直截了当的,特别是 阿尔茨海默病和相关痴呆症(ADRD)患者。ADRD患者患有不良 功能状态、共病和预期寿命缩短。对ADRD患者进行癌症筛查可能 导致过度诊断和治疗癌症,否则不会引起症状, 病人的一生另一方面,癌症筛查测试有效性的关键证据是 降低晚期癌症的发病率和死亡率,这通常在随机临床试验中获得 (RCT)。然而,癌症筛查的RCT需要长期随访。在ADRD中进行这些RCT 病人,一个身体和认知能力受损的群体,可以在后勤上,经济上, 道德挑战。到目前为止,癌症筛查是否真的能降低癌症患者的死亡率仍不清楚。 这个人口。在我们的父奖R 01 CA 246418中,我们开发了研究级可计算 表型(CP)用于鉴定有资格或无资格进行肺癌筛查(LCS)的个体, OneFlorida网络中的结构化和非结构化EHR数据。我们将很快开始研究 适当和不适当LCS的使用模式和预测因素,并估计收益和成本- LCS的有效性。OneFlorida是PCORI资助的9个研究网络之一, PCORnet。它包含约1500万患者EHR,与来自各种其他来源的数据相关联,包括Medicaid 和医疗保险索赔,癌症登记,和生命统计。在这份行政补充文件中,我们建议 在OneFlorida确定ADRD患者的回顾性队列,以:(1)检查使用模式, ADRD患者中肺癌、乳腺癌和结直肠癌筛查的预测因素,以及(2)检测癌症是否 筛查可降低ADRD患者的晚期癌症发病率和死亡率。本研究将首先 研究癌症筛查在降低晚期癌症诊断和死亡率方面的有效性, ADRD患者。鉴于随机对照试验的不可行性,我们的研究将提供关键证据,可以帮助临床医生, 护理人员和家庭了解癌症筛查的有效性,并决定 癌症筛查对ADRD患者是否有用。

项目成果

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Jiang Bian其他文献

Jiang Bian的其他文献

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{{ truncateString('Jiang Bian', 18)}}的其他基金

ACTS (AD Clinical Trial Simulation): Developing Advanced Informatics Approaches for an Alzheimer's Disease Clinical Trial Simulation System
ACTS(AD 临床试验模拟):为阿尔茨海默病临床试验模拟系统开发先进的信息学方法
  • 批准号:
    10753675
  • 财政年份:
    2023
  • 资助金额:
    $ 38.13万
  • 项目类别:
Disparities of Alzheimer's disease progression in sexual and gender minorities
性少数群体中阿尔茨海默病进展的差异
  • 批准号:
    10590413
  • 财政年份:
    2023
  • 资助金额:
    $ 38.13万
  • 项目类别:
Post-Acute Sequelae of SARS-CoV-2 Infection and Subsequent Disease Progression in Individuals with AD/ADRD: Influence of the Social and Environmental Determinants of Health
AD/ADRD 患者 SARS-CoV-2 感染的急性后遗症和随后的疾病进展:健康的社会和环境决定因素的影响
  • 批准号:
    10751275
  • 财政年份:
    2023
  • 资助金额:
    $ 38.13万
  • 项目类别:
Artificial Intelligence and Counterfactually Actionable Responses to End HIV (AI-CARE-HIV)
人工智能和反事实可行的终结艾滋病毒应对措施 (AI-CARE-HIV)
  • 批准号:
    10699171
  • 财政年份:
    2023
  • 资助金额:
    $ 38.13万
  • 项目类别:
An end-to-end informatics framework to study Multiple Chronic Conditions (MCC)'s impact on Alzheimer's disease using harmonized electronic health records
使用统一的电子健康记录研究多种慢性病 (MCC) 对阿尔茨海默病的影响的端到端信息学框架
  • 批准号:
    10728800
  • 财政年份:
    2023
  • 资助金额:
    $ 38.13万
  • 项目类别:
AI-ADRD: Accelerating interventions of AD/ADRD via Machine learning methods
AI-ADRD:通过机器学习方法加速 AD/ADRD 干预
  • 批准号:
    10682237
  • 财政年份:
    2023
  • 资助金额:
    $ 38.13万
  • 项目类别:
Advancing Precision Lung Cancer Surveillance and Outcomes in Diverse Populations (PLuS2)
推进不同人群的精准肺癌监测和结果 (PLuS2)
  • 批准号:
    10752848
  • 财政年份:
    2023
  • 资助金额:
    $ 38.13万
  • 项目类别:
Eligibility criteria design for Alzheimer's trials with real-world data and explainable AI
利用真实数据和可解释的人工智能设计阿尔茨海默病试验的资格标准
  • 批准号:
    10608470
  • 财政年份:
    2023
  • 资助金额:
    $ 38.13万
  • 项目类别:
Computational Drug Repurposing for AD/ADRD with Integrative Analysis of Real World Data and Biomedical Knowledge
通过对真实世界数据和生物医学知识的综合分析,计算药物再利用用于 AD/ADRD
  • 批准号:
    10576853
  • 财政年份:
    2022
  • 资助金额:
    $ 38.13万
  • 项目类别:
Computational Drug Repurposing for AD/ADRD with Integrative Analysis of Real World Data and Biomedical Knowledge
通过对真实世界数据和生物医学知识的综合分析,计算药物再利用用于 AD/ADRD
  • 批准号:
    10392169
  • 财政年份:
    2022
  • 资助金额:
    $ 38.13万
  • 项目类别:

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