通过多种数据源识别具有遗传性癌症风险的个体:使用 GARDE 平台和犹他州人口数据库的人口方法。

IF 3.3 Q2 ONCOLOGY JCO Clinical Cancer Informatics Pub Date : 2024-11-01 Epub Date: 2024-11-21 DOI:10.1200/CCI-24-00142
Guilherme Del Fiol, Michael J Madsen, Richard L Bradshaw, Michael G Newman, Kimberly A Kaphingst, Sean V Tavtigian, Nicola J Camp
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摘要

目的:GARDE 平台利用电子病历 (EHR) 中报告的家族史系统地识别符合遗传性癌症综合征基因检测条件的患者。本研究的目的是评估当提供更全面的家族史数据时,GARDE 识别合格个体的有效性的变化,从而量化记录不足的影响:方法: 使用 GARDE 分析了犹他大学健康中心的 133764 名患者队列,比较了使用电子病历数据和电子病历加上来自全州人口数据库(犹他人口数据库,UPDB)的数据的识别率:结果:与仅使用电子病历相比,电子病历+UPDB 使符合基因检测条件的患者比例从 4.1% 提高到 9.2%。在具有最全面家族史的 44,692 人中,符合基因检测条件的人数增加了四倍多,从 4.6%(仅 EHR)增至 19.3%(EHR + UPDB)。在所有人口统计数据中,这一比例都有显著提高,但历史上被边缘化的少数族裔仍存在差距(非白人种族为 9.2%-13.9%,而白人种族为 19.7%):结论:用UPDB的家族史数据增强电子病历数据大大提高了对所有亚群中符合遗传性癌症综合征基因检测条件的个体的检测率。这凸显了改进获取家族史方法的重要性,无论是亲自获取还是在硅学中获取。然而,这些增加并没有改善差异。持续的差异不太可能仅由不完整的家族史来解释,也可能是因为易感基因、风险变异和筛查指南主要是在白人种族中发现和制定的。要解决差异问题,就需要有意识地收集历史上被边缘化的少数民族的家族史数据,并在更多不同的人群中推广遗传和风险评估研究,以确保公平和医疗保健。
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Identification of Individuals With Hereditary Cancer Risk Through Multiple Data Sources: A Population-Based Method Using the GARDE Platform and The Utah Population Database.

Purpose: The GARDE platform uses family history reported in the electronic health record (EHR) to systematically identify eligible patients for genetic testing for hereditary cancer syndromes. The goal of this study was to evaluate the change in effectiveness of GARDE to identify eligible individuals when more comprehensive family history data are provided, thus quantifying the impact of underdocumentation.

Methods: A cohort of 133,764 patients at the University of Utah Health was analyzed with GARDE comparing identification rates using EHR data versus EHR plus data from a statewide population database, the Utah Population Database (UPDB).

Results: Compared with EHR alone, EHR + UPDB increased the rate of individuals eligible for genetic testing from 4.1% to 9.2%. In the 44,692 individuals with the most comprehensive family history, eligibility more than quadrupled from 4.6% (EHR alone) to 19.3% (EHR + UPDB). The increase was significant across all demographics, but disparities still remained for historically marginalized minorities (9.2%-13.9% in non-White races compared with 19.7% in White races).

Conclusion: Augmenting EHR data with family history data from the UPDB substantially improved the detection of individuals eligible for genetic testing of hereditary cancer syndromes in all subgroups. This underscores the importance of improving methods for acquiring family history, in person or in silico. However, these increases did not ameliorate disparities. Continuous disparities are unlikely to be explained by incomplete family history alone and may also be because susceptibility genes, risk variants, and screening guidelines were discovered and developed largely in White races. Addressing disparities will require intentional data collection of family history in historically marginalized minorities and the promotion of genetic and risk assessment studies in more diverse populations to ensure equity and health care.

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