Background: Despite chemotherapy-related toxicities being more likely in older patients, no routine prediction tool has been validated for the UK population. Previous research within the TOASTIE (tolerance of anti-cancer systemic therapy in the elderly) study found a low predictive performance of the Cancer and Aging Research Group (CARG) score for severe chemotherapy-related toxicities. Building on this, the TOASTIE study dataset was used to assess the viability of developing a predictive model with baseline variables and frailty scores for severe chemotherapy-related toxicities in older patients. Methods: All patients from the TOASTIE dataset were included, with the inclusion/exclusion criteria detailed in the TOASTIE protocol. Demographic factors, self-assessment scores, Rockwood Clinical Frailty Score and researcher’s estimated risks of toxicity were assessed for their association with severe chemotherapy-related toxicities. After data partition into 70:15:15 train/validation/test, models were built on the training dataset using logistic regression (LR), LASSO and random forest (RF). Models were optimized with a validation set with LR and LASSO; cross-validation was used with RF. Model performance was assessed with balanced accuracy, NPV and AUC. Results: Of the 322 patients included, the incidence of severe toxicities was 22% (n = 71). Ten variables were statistically significant, albeit weakly associated with severe toxicities: primarily patient-reported factors, Performance Status and high baseline neutrophil count. LR models gave the best balanced accuracies of 0.6382 (AUC 0.6950, NPV 0.8696) and 0.6469 (AUC 0.6469, NPV 0.4286) with LASSO, and 0.6294 (AUC 0.6557, NPV 0.6557) with RF. Conclusions: Models lack sufficiently robust results for clinical utility. However, a high NPV in predicting no toxicity could help identify lower-risk patients who may not require dose reductions, potentially improving overall outcomes.
背景：尽管老年患者更易发生化疗相关毒性反应，但目前尚未有经过验证的常规预测工具适用于英国人群。先前在TOASTIE（老年患者抗癌系统治疗耐受性）研究中发现，癌症与衰老研究组（CARG）评分对严重化疗相关毒性反应的预测效能较低。基于此，本研究利用TOASTIE研究数据集，评估了结合基线变量与衰弱评分构建老年患者严重化疗相关毒性反应预测模型的可行性。方法：纳入TOASTIE数据集中的所有患者，具体纳入/排除标准详见TOASTIE研究方案。评估人口统计学因素、自我评估评分、Rockwood临床衰弱评分及研究者预估的毒性风险与严重化疗相关毒性反应之间的关联性。将数据按70:15:15的比例划分为训练集/验证集/测试集后，采用逻辑回归（LR）、LASSO和随机森林（RF）方法在训练集上构建模型。使用验证集对LR和LASSO模型进行优化，RF模型则采用交叉验证法。通过平衡准确率、阴性预测值和曲线下面积评估模型性能。结果：在纳入的322例患者中，严重毒性反应发生率为22%（n=71）。十项变量与严重毒性反应存在统计学显著关联（尽管相关性较弱），主要包括患者自评因素、体能状态评分及基线中性粒细胞计数升高。LR模型在LASSO方法下获得最佳平衡准确率0.6382（AUC 0.6950，NPV 0.8696）和0.6469（AUC 0.6469，NPV 0.4286），在RF方法下为0.6294（AUC 0.6557，NPV 0.6557）。结论：现有模型尚缺乏足够稳健的结果以支持临床应用。然而，在预测无毒性反应方面的高阴性预测值有助于识别可能无需减量的低风险患者，这有望改善总体治疗结局。
肿瘤(癌症)患者之家