Institute for Cancer Outcomes and Survivorship, Division of Pediatric Hematology and Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
St. Jude Children's Research Hospital, Memphis, Tennessee.
Cancer. 2021 Oct 15;127(20):3832-3839. doi: 10.1002/cncr.33760. Epub 2021 Jun 23.
Poor mercaptopurine (6MP) adherence (mean adherence rate < 90%) increases the relapse risk among children with acute lymphoblastic leukemia (ALL). 6MP adherence remains difficult to measure in real time. Easily measured patient-level factors could identify patients at risk for poor adherence.
The authors measured 6MP adherence via electronic monitoring for 6 months per patient. Using data from month 3, they created a risk prediction model for 6MP nonadherence in 407 children with ALL (mean age, 7.7 ± 4.4 years); they used receiver operating characteristic analyses in the training set (n = 250) and replicated this in the test set (n = 157).
Age, race/ethnicity, 6MP dose intensity, absolute neutrophil count, 6MP ingestion patterns, and household structure were retained in the prediction model. The model yielded areas under the receiver operating characteristic curve (AUCs) of 0.79 (95% confidence interval [CI], 0.71-0.85) and 0.74 (95% CI, 0.63-0.85) in the training and test sets, respectively. The model performed better for those who were ≥12 years old (AUC, 0.79; 95% CI, 0.59-0.99) than those <12 years old (AUC, 0.70; 95% CI, 0.58-0.81). Using the predicted probability of nonadherence based on receiver operating characteristic analysis, the authors developed a binary risk classifier to classify patients with a high or low probability of nonadherence. The sensitivity and specificity of the binary risk classifier were 71% and 76%, respectively. Adjusted for clinical prognosticators, the risk of relapse was 2.2-fold higher (95% CI, 0.94-5.1; P = .07) among patients with a high probability of nonadherence in comparison with those with a low probability, as identified by the risk prediction model.
The risk prediction model identified patients with a high probability of nonadherence and could be used in real time to personalize recommendations and interventions in the clinic.
The vast majority of children with acute lymphoblastic leukemia, the most common childhood cancer, are cured. The treatment of acute lymphoblastic leukemia includes taking an oral chemotherapy medicine (mercaptopurine) for approximately 2 years. Children who miss doses of this medicine (specifically children who take the medicine less than 90% of the time that it is prescribed) are more likely to suffer leukemia relapse. The authors of this article have measured mercaptopurine adherence with electronic bottle caps to determine characteristics of patients that predict nonadherence, and they have created a prediction tool that could allow physicians to identify and intervene with patients at high risk of nonadherence.
急性淋巴细胞白血病(ALL)患儿的巯嘌呤(6MP)依从性差(平均依从率<90%)会增加复发风险。6MP 依从性的实时测量仍然很困难。易于测量的患者水平因素可以识别依从性差的高风险患者。
作者通过电子监测为每位患者测量 6 个月的 6MP 依从性。他们使用第 3 个月的数据,为 407 名 ALL 患儿(平均年龄 7.7±4.4 岁)创建了一个 6MP 不依从的风险预测模型;他们在训练集(n=250)中进行了受试者工作特征分析,并在测试集(n=157)中进行了复制。
年龄、种族/民族、6MP 剂量强度、绝对中性粒细胞计数、6MP 摄入模式和家庭结构保留在预测模型中。该模型在训练集和测试集中的受试者工作特征曲线下面积(AUC)分别为 0.79(95%置信区间[CI],0.71-0.85)和 0.74(95%CI,0.63-0.85)。对于年龄≥12 岁的患者(AUC,0.79;95%CI,0.59-0.99),该模型的表现优于年龄<12 岁的患者(AUC,0.70;95%CI,0.58-0.81)。根据受试者工作特征分析预测的不依从概率,作者开发了一个二元风险分类器,以分类具有高或低不依从概率的患者。该二元风险分类器的敏感性和特异性分别为 71%和 76%。在调整了临床预后因素后,与低风险患者相比,高风险患者的复发风险高 2.2 倍(95%CI,0.94-5.1;P=0.07),高风险患者是根据风险预测模型确定的。
该风险预测模型识别出了高不依从风险的患者,并可实时用于个性化临床建议和干预。
急性淋巴细胞白血病是最常见的儿童癌症,绝大多数患儿都能被治愈。急性淋巴细胞白血病的治疗包括口服化疗药物(巯嘌呤)约 2 年。服用这种药物(特别是儿童服用的药物少于规定时间的 90%)的患儿更有可能出现白血病复发。本文作者通过电子瓶盖测量巯嘌呤的依从性,以确定预测不依从的患者特征,并创建了一个预测工具,使医生能够识别和干预高风险不依从的患者。