Genentech, Inc., South San Francisco, California, USA.
Yale University School of Public Health, New Haven, Connecticut, USA.
Pharmacoepidemiol Drug Saf. 2021 Sep;30(9):1233-1241. doi: 10.1002/pds.5309. Epub 2021 Jun 24.
Eastern Cooperative Oncology Group performance status (ECOG PS) is an important predictor for receipt of treatment and overall survival (OS) but is often unreported in routine care. We developed a proxy for baseline ECOG PS using electronic health records (EHRs).
We analyzed patients who were diagnosed with advanced non-small cell lung cancer (aNSCLC), advanced bladder cancer (aBCa), and advanced melanoma (aMEL) between 2011 and 2018 and had a baseline (reported between diagnosis and treatment) ECOG PS in a real-world database. We used stepwise multivariable logistic regression to model associations between baseline ECOG PS good (<2) versus poor (≥2) and sociodemographic, clinical, and laboratory measures in each cancer type. Predictive accuracy of classifying ECOG PS was assessed. We tested the association between OS and observed and predicted ECOG PS.
In total, 20 697 aNSCLC patients, 2627 aBCa patients, and 2558 aMEL patients constituted the study population. Percentage of patients with poor ECOG PS ranged from 15.3% (aMEL) to 28.5% (aNSCLC). Poor ECOG PS was associated with more comorbid conditions, older age, lower body mass index, metastases, and abnormal laboratory indicators. Overall prediction accuracy using a 0.50 cutpoint was 73.3% for NSCLC, 73.8% for aBCa, and 85.4% for aMEL. The association of OS with ECOG PS was consistent between the observed and proxy measures.
In the EHR-derived data, clinical, sociodemographic, and laboratory information were used to assign ECOG PS and enhance the use of real-world data in outcome studies.
东部肿瘤协作组体力状况（ECOG PS）是接受治疗和总生存（OS）的重要预测因素，但在常规治疗中往往未报告。我们使用电子健康记录（EHR）开发了一种基线 ECOG PS 的替代指标。
我们分析了 2011 年至 2018 年间在真实世界数据库中被诊断为晚期非小细胞肺癌（aNSCLC）、晚期膀胱癌（aBCa）和晚期黑色素瘤（aMEL）的患者，这些患者在基线（报告时间在诊断和治疗之间）有 ECOG PS。我们使用逐步多变量逻辑回归分析来建立 ECOG PS 良好（<2）与不良（≥2）与每个癌症类型的社会人口统计学、临床和实验室指标之间的关联。评估了分类 ECOG PS 的准确性。我们测试了 OS 与观察到的和预测的 ECOG PS 之间的关联。
总共 20697 例 aNSCLC 患者、2627 例 aBCa 患者和 2558 例 aMEL 患者构成了研究人群。ECOG PS 不良（aMEL）的患者比例为 15.3%，ECOG PS 不良（aNSCLC）的患者比例为 28.5%。ECOG PS 不良与更多的合并症、年龄较大、较低的体重指数、转移和异常的实验室指标有关。使用 0.50 切点的总体预测准确性为 NSCLC 为 73.3%，aBCa 为 73.8%，aMEL 为 85.4%。OS 与 ECOG PS 的关联在观察和代理测量之间是一致的。
在 EHR 衍生的数据中，使用临床、社会人口统计学和实验室信息来分配 ECOG PS，并增强了真实世界数据在结局研究中的应用。
