Pond Gregory R, Siu Lillian L, Moore Malcolm, Oza Amit, Hirte Hal W, Winquist Eric, Goss Glenwood, Hudes Gary, Townsley Carol A
Department of Medical Oncology and Hematology, Princess Margaret Hospital, University Health Network, 610 University Ave, Ste 5-718, Toronto, Ontario, M5G 2M9, Canada.
J Clin Oncol. 2008 Mar 10;26(8):1324-30. doi: 10.1200/JCO.2007.14.0673.
A tool that quantifies the risk of treatment-related toxicity based on individual patient characteristics can augment the informed consent process and safety monitoring in the setting of phase II cancer treatment trials of molecularly targeted agents (MTAs).
A regression model was constructed to predict the risk of a serious adverse event (SAE) with an MTA and presented as a nomogram. Estimation of risk can be performed by integrating risk estimates from the nomogram and from a reference or average patient. Internal validation was performed using bootstrapping techniques.
A total of 578 patients were treated with one of 14 MTAs given alone or in combination on one of 27 clinical trials performed by the Princess Margaret Hospital Drug Development Program between 2001 and 2006. Approximately 50% and 24% of patients experienced an SAE and an attributable SAE (SAEatt) during cycle 1, respectively. Albumin, lactate dehydrogenase (LDH), number of target lesions, prior radiotherapy, Charlson score, age, and performance status were included in the optimal model as predictors of a cycle 1 SAE, whereas the number of prior chemotherapy regimens, baseline creatinine, LDH, prior radiotherapy, Charlson score, body-surface area, and performance status were included as predictors of an SAEatt. Moderate-good internal validity was demonstrated, with area under the curve estimates ranging from 56.7% to 86.1% for all SAEs and 63.0% to 89.7% for SAEatts.
A regression model was constructed that predicts the SAE and SAEatt risk for an individual patient during cycle 1 of phase II trial treatment with moderate to good internal validity. External validation is still required.
一种基于个体患者特征对治疗相关毒性风险进行量化的工具，可在分子靶向药物（MTA）的II期癌症治疗试验中加强知情同意过程和安全监测。
构建回归模型以预测使用MTA发生严重不良事件（SAE）的风险，并以列线图形式呈现。通过整合列线图和参考或平均患者的风险估计值来进行风险估计。使用自抽样技术进行内部验证。
2001年至2006年期间，玛格丽特公主医院药物开发项目进行的27项临床试验中，共有578例患者接受了14种MTA中的一种单独或联合治疗。在第1周期中，分别约有50%和24%的患者发生了SAE和可归因的SAE（SAEatt）。白蛋白、乳酸脱氢酶（LDH）、靶病灶数量、既往放疗史、查尔森评分、年龄和体能状态被纳入最佳模型，作为第1周期SAE的预测指标，而既往化疗方案数量、基线肌酐、LDH、既往放疗史、查尔森评分、体表面积和体能状态被纳入SAEatt的预测指标。结果显示出中度至良好的内部效度，所有SAE的曲线下面积估计范围为56.7%至86.1%，SAEatt的曲线下面积估计范围为63.0%至89.7%。
构建了一个回归模型，该模型在II期试验治疗的第1周期中对个体患者的SAE和SAEatt风险具有中度至良好的内部效度。仍需进行外部验证。
