Department of Urology, Yale University School of Medicine, New Haven, Connecticut.
Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut.
JAMA Netw Open. 2024 Jun 3;7(6):e2417274. doi: 10.1001/jamanetworkopen.2024.17274.
Although tissue-based gene expression testing has become widely used for prostate cancer risk stratification, its prognostic performance in the setting of clinical care is not well understood.
To develop a linkage between a prostate genomic classifier (GC) and clinical data across payers and sites of care in the US.
DESIGN, SETTING, AND PARTICIPANTS: In this cohort study, clinical and transcriptomic data from clinical use of a prostate GC between 2016 and 2022 were linked with data aggregated from insurance claims, pharmacy records, and electronic health record (EHR) data. Participants were anonymously linked between datasets by deterministic methods through a deidentification engine using encrypted tokens. Algorithms were developed and refined for identifying prostate cancer diagnoses, treatment timing, and clinical outcomes using diagnosis codes, Common Procedural Terminology codes, pharmacy codes, Systematized Medical Nomenclature for Medicine clinical terms, and unstructured text in the EHR. Data analysis was performed from January 2023 to January 2024.
Diagnosis of prostate cancer.
The primary outcomes were biochemical recurrence and development of prostate cancer metastases after diagnosis or radical prostatectomy (RP). The sensitivity of the linkage and identification algorithms for clinical and administrative data were calculated relative to clinical and pathological information obtained during the GC testing process as the reference standard.
A total of 92 976 of 95 578 (97.2%) participants who underwent prostate GC testing were successfully linked to administrative and clinical data, including 53 871 who underwent biopsy testing and 39 105 who underwent RP testing. The median (IQR) age at GC testing was 66.4 (61.0-71.0) years. The sensitivity of the EHR linkage data for prostate cancer diagnoses was 85.0% (95% CI, 84.7%-85.2%), including 80.8% (95% CI, 80.4%-81.1%) for biopsy-tested participants and 90.8% (95% CI, 90.5%-91.0%) for RP-tested participants. Year of treatment was concordant in 97.9% (95% CI, 97.7%-98.1%) of those undergoing GC testing at RP, and 86.0% (95% CI, 85.6%-86.4%) among participants undergoing biopsy testing. The sensitivity of the linkage was 48.6% (95% CI, 48.1%-49.1%) for identifying RP and 50.1% (95% CI, 49.7%-50.5%) for identifying prostate biopsy.
This study established a national-scale linkage of transcriptomic and longitudinal clinical data yielding high accuracy for identifying key clinical junctures, including diagnosis, treatment, and early cancer outcome. This resource can be leveraged to enhance understandings of disease biology, patterns of care, and treatment effectiveness.
尽管基于组织的基因表达测试已广泛用于前列腺癌风险分层,但它在临床护理环境中的预后性能尚不清楚。
在美国的多个支付方和医疗场所建立与前列腺基因组分类器(GC)相关的联系。
设计、设置和参与者:在这项队列研究中,临床和转录组数据来自 2016 年至 2022 年期间临床使用前列腺 GC 的情况,并与从保险索赔、药房记录和电子健康记录(EHR)数据中汇总的数据进行了关联。通过使用加密令牌的去识别引擎,通过确定性方法在数据集之间匿名链接参与者。使用诊断代码、通用程序术语代码、药房代码、医学术语系统命名法临床术语和 EHR 中的非结构化文本,开发和完善了用于识别前列腺癌诊断、治疗时机和临床结果的算法。数据分析于 2023 年 1 月至 2024 年 1 月进行。
前列腺癌的诊断。
主要结局是生化复发和诊断或根治性前列腺切除术后(RP)前列腺癌转移的发展。将临床和管理数据的链接和识别算法的灵敏度相对于在 GC 测试过程中获得的临床和病理信息计算为参考标准。
共对 95578 名接受前列腺 GC 测试的患者中的 92976 名(97.2%)成功链接到行政和临床数据,其中 53871 名接受了活检测试,39105 名接受了 RP 测试。GC 测试时的中位(IQR)年龄为 66.4(61.0-71.0)岁。EHR 链接数据对前列腺癌诊断的灵敏度为 85.0%(95%CI,84.7%-85.2%),包括活检测试参与者的 80.8%(95%CI,80.4%-81.1%)和 RP 测试参与者的 90.8%(95%CI,90.5%-91.0%)。在接受 RP 治疗的 GC 检测中,97.9%(95%CI,97.7%-98.1%)的患者和接受活检检测的患者中,86.0%(95%CI,85.6%-86.4%)的患者治疗年份一致。链接的灵敏度为识别 RP 为 48.6%(95%CI,48.1%-49.1%),识别前列腺活检为 50.1%(95%CI,49.7%-50.5%)。
本研究建立了一个全国范围内的转录组和纵向临床数据的链接,对识别关键临床节点具有很高的准确性,包括诊断、治疗和早期癌症结果。该资源可用于增强对疾病生物学、护理模式和治疗效果的理解。