Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Medical Oncology Department, The Christie NHS Foundation Trust, Manchester, United Kingdom.
JCO Clin Cancer Inform. 2024 Dec;8:e2400035. doi: 10.1200/CCI.24.00035. Epub 2024 Nov 26.
Electronic patient-reported outcome measures (ePROMs) are increasingly collected routinely in clinical practice and may be prognostic for survival in adults with advanced non-small cell lung cancer (NSCLC) in addition to clinical data. This study developed ePROM-enhanced models for predicting 1-year overall survival in patients with advanced NSCLC at the start of immunotherapy.
This is a single-center study using consecutive patients from a tertiary cancer hospital in England. Using Cox proportional hazards models, we developed one clinical factor-only model and three ePROM-enhanced models, each including one of the following factors: quality of life (as measured by EuroQoL five-dimension five-level utility score) and overall symptom burden and number of moderate-to-severe symptoms (as measured by patient-reported version of Common Terminology Criteria for Adverse Events). Predictive performance was evaluated and compared through bootstrapping internal validation, and clinical utility was determined via decision curve analysis.
The clinical factor-only model contained age, histology, performance status, and neutrophile-to-lymphocyte ratio. While calibration was similar between the clinical factor-only and ePROM-enhanced models, the latter showed improved discrimination by 0.020 (95% CI, 0.011 to 0.024), 0.024 (95% CI, 0.016 to 0.031), and 0.024 (95% CI, 0.014 to 0.029) when enhanced with ePROMs on quality of life, overall symptom burden, and number of moderate-to-severe symptoms, respectively. If care decisions are to be made at risk thresholds between 25% and 75%, the ePROM-enhanced models led to higher net benefit than the clinical factor-only model and the default strategies of intervention for all and intervention for none.
The ePROM-enhanced models outperformed the clinical factor-only model in predicting 1-year overall survival for patients with advanced NSCLC receiving immunotherapy and showed potential clinical utility for informing decisions in this population. Future studies should focus on validating the models in external data sets.
电子患者报告结局测量（ePROM）在临床实践中越来越多地被常规收集，并且除了临床数据外，对于接受免疫治疗的晚期非小细胞肺癌（NSCLC）患者的生存也可能具有预后意义。本研究开发了 ePROM 增强模型，以预测接受免疫治疗的晚期 NSCLC 患者开始治疗后 1 年的总体生存率。
这是一项单中心研究，使用了英国一家三级癌症医院的连续患者。我们使用 Cox 比例风险模型，开发了一个临床因素模型和三个 ePROM 增强模型，每个模型都包含以下一个因素：生活质量（通过欧洲五维健康量表五维得分来衡量）和整体症状负担以及中重度症状的数量（通过患者报告的通用不良事件术语标准衡量）。通过 bootstrapping 内部验证评估和比较预测性能，并通过决策曲线分析确定临床实用性。
临床因素模型仅包含年龄、组织学、体能状态和中性粒细胞与淋巴细胞比值。虽然临床因素模型和 ePROM 增强模型的校准结果相似，但后者在增强了生活质量、整体症状负担和中重度症状数量的 ePROM 后，分别提高了 0.020（95%CI，0.011 至 0.024）、0.024（95%CI，0.016 至 0.031）和 0.024（95%CI，0.014 至 0.029）的区分度。如果决策风险阈值在 25%至 75%之间，ePROM 增强模型比临床因素模型和干预所有患者和不干预任何患者的默认策略产生更高的净收益。
ePROM 增强模型在预测接受免疫治疗的晚期 NSCLC 患者 1 年总体生存率方面优于临床因素模型，并显示出在该人群中为决策提供信息的潜在临床实用性。未来的研究应集中在验证这些模型在外部数据集上的有效性。
