Department of Practice and Policy, School of Pharmacy, University College London, London, UK; The Centre for Medicines Optimisation Research and Education, University College London NHS Foundation Trust, London, UK.
University College London Cancer Institute, London, UK.
ESMO Open. 2023 Feb;8(1):100743. doi: 10.1016/j.esmoop.2022.100743. Epub 2022 Dec 19.
The risk of toxicity-related dose delays, with cancer treatment, should be included as part of pretreatment education and be considered by clinicians upon prescribing chemotherapy. An objective measure of individual risk could influence clinical decisions, such as escalation of standard supportive care and stratification of some patients, to receive proactive toxicity monitoring.
We developed a logistic regression prediction model (Delay-7) to assess the overall risk of a chemotherapy dose delay of 7 days for patients receiving first-line treatments for breast, colorectal and diffuse large B-cell lymphoma. Delay-7 included hospital treated, age at the start of chemotherapy, gender, ethnicity, body mass index, cancer diagnosis, chemotherapy regimen, colony stimulating factor use, first cycle dose modifications and baseline blood values. Baseline blood values included neutrophils, platelets, haemoglobin, creatinine and bilirubin. Shrinkage was used to adjust for overoptimism of predictor effects. For internal validation (of the full models in the development data) we computed the ability of the models to discriminate between those with and without poor outcomes (c-statistic), and the agreement between predicted and observed risk (calibration slope). Net benefit was used to understand the risk thresholds where the model would perform better than the 'treat all' or 'treat none' strategies.
A total of 4604 patients were included in our study of whom 628 (13.6%) incurred a 7-day delay to the second cycle of chemotherapy. Delay-7 showed good discrimination and calibration, with c-statistic of 0.68 (95% confidence interval 0.66-0.7), following internal validation and calibration-in-the-large of -0.006.
Delay-7 predicts a patient's individualised risk of a treatment-related delay at cycle two of treatment. The score can be used to stratify interventions to reduce the occurrence of treatment-related toxicity.
在进行癌症治疗时，应将与毒性相关的剂量延迟风险纳入预处理教育的一部分，并由临床医生在开具化疗药物时进行考虑。个体风险的客观衡量标准可能会影响临床决策，例如加强标准支持性护理和对某些患者进行分层，以进行主动毒性监测。
我们开发了一个逻辑回归预测模型（Delay-7），用于评估接受乳腺癌、结直肠癌和弥漫性大 B 细胞淋巴瘤一线治疗的患者发生化疗剂量延迟 7 天的总体风险。Delay-7 包括医院治疗、化疗开始时的年龄、性别、种族、体重指数、癌症诊断、化疗方案、集落刺激因子使用、第一周期剂量调整和基线血液值。基线血液值包括中性粒细胞、血小板、血红蛋白、肌酐和胆红素。收缩用于调整预测因素效果的过度乐观。为了内部验证（开发数据中的完整模型），我们计算了模型区分不良结局（c 统计量）和预测风险与观察风险之间的一致性（校准斜率）的能力。净效益用于了解模型在哪些风险阈值下的表现优于“治疗所有”或“治疗无”策略。
共有 4604 名患者纳入我们的研究，其中 628 名（13.6%）在第二次化疗周期中出现了 7 天的延迟。Delay-7 显示出良好的区分度和校准度，内部验证和大规模校准后的 c 统计量为 0.68（95%置信区间为 0.66-0.7），校准斜率为-0.006。
Delay-7 预测了患者在治疗第二周期时与治疗相关的延迟的个体化风险。该评分可用于分层干预措施，以减少与治疗相关的毒性发生。
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