Background: The PRO-CTCAE provides patient-reported data on symptomatic AEs. A summary metric—the ACS—reflecting total AE burden can be calculated by averaging AE-level composite scores at a given timepoint for each participant. This study investigated the psychometric properties and interpretability of this PRO-CTCAE ACS in patients with breast, lung, or head/neck cancers. Methods: We conducted a secondary analysis of a PRO-CTCAE validation dataset comprising 940 adults undergoing chemotherapy or radiation therapy (clinicaltrials.gov: NCT02158637). We focused on empirically recommended symptom terms for three cancer sites. Analyses included Spearman’s correlations, coefficient alpha, and eigenvalues from the correlation matrices, confirmatory factor analysis (CFA), and principal component analysis (PCA). Latent profile analysis (LPA) was used to assess ACS interpretability in the lung cohort. Results: Mean composite score inter-correlations were moderate (0.30–0.35), and coefficient alphas were high (0.81–0.91). Eigenvalue ratios and CFA supported retention of a single factor/component, with suitable model fit indices. ACS correlated highly with factor scores and the first principal component from the PCA. Reduced sets of terms produced reliable scores that closely approximated the full set scores and aligned with external criteria. LPA in the lung subgroup identified four latent classes; ACS differentiated high vs. low symptom burden groups but did not distinguish the two groups expressing distinct symptom profiles. Conclusion: The ACS demonstrated structural validity through adequately fitting linear factor models and effectively summarized symptomatic AE burden. However, similar ACS values may mask clinically distinct symptomatic AE profiles, underscoring the value of both summary metrics and profile-based approaches.
背景：PRO-CTCAE（患者报告的不良事件通用术语标准）提供了患者报告的症状性不良事件数据。通过计算每位参与者在特定时间点各不良事件层面综合评分的平均值，可得出反映总体不良事件负担的汇总指标——不良事件综合评分。本研究探讨了该PRO-CTCAE不良事件综合评分在乳腺癌、肺癌或头颈癌患者中的心理测量学特性及临床可解释性。方法：我们对包含940名接受化疗或放疗的成人患者的PRO-CTCAE验证数据集进行了二次分析（临床试验注册号：NCT02158637）。研究聚焦于三个癌症部位经实证推荐的核心症状条目。分析方法包括斯皮尔曼相关性分析、系数α检验、相关矩阵特征值分析、验证性因子分析及主成分分析。在肺癌亚组中运用潜在类别分析评估不良事件综合评分的可解释性。结果：各症状综合评分间的平均相关性为中等水平（0.30-0.35），系数α值较高（0.81-0.91）。特征值比率与验证性因子分析支持保留单因子/成分结构，模型拟合指数良好。不良事件综合评分与因子得分及主成分分析的首个主成分高度相关。精简条目集产生的评分具有良好信度，与完整条目集评分高度吻合且符合外部标准。肺癌亚组的潜在类别分析识别出四个潜在类别；不良事件综合评分能有效区分高症状负担组与低症状负担组，但无法辨别具有不同症状特征的两个亚组。结论：不良事件综合评分通过良好拟合的线性因子模型证明了结构效度，能有效汇总症状性不良事件负担。然而，相似的不良事件综合评分可能掩盖具有临床差异的症状特征，这凸显了综合评分与特征分析并用的价值。
肿瘤(癌症)患者之家