BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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. LAY SUMMARY: 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.
BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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. LAY SUMMARY: 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.
Authors: Wendy Landier; Yanjun Chen; Lindsey Hageman; Heeyoung Kim; Bruce C Bostrom; Jacqueline N Casillas; David S Dickens; William E Evans; Kelly W Maloney; Leo Mascarenhas; A Kim Ritchey; Amanda M Termuhlen; William L Carroll; Mary V Relling; F Lennie Wong; Smita Bhatia Journal: Blood Date: 2017-02-02 Impact factor: 22.113
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Authors: Wendy Landier; Lindsey Hageman; Yanjun Chen; Nancy Kornegay; William E Evans; Bruce C Bostrom; Jacqueline Casillas; David S Dickens; Anne L Angiolillo; Glen Lew; Kelly W Maloney; Leo Mascarenhas; A Kim Ritchey; Amanda M Termuhlen; William L Carroll; Mary V Relling; F Lennie Wong; Smita Bhatia Journal: J Clin Oncol Date: 2017-03-24 Impact factor: 44.544
Authors: G Koren; G Ferrazini; H Sulh; A M Langevin; J Kapelushnik; J Klein; E Giesbrecht; S Soldin; M Greenberg Journal: N Engl J Med Date: 1990-07-05 Impact factor: 91.245
Authors: Smita Bhatia; Lindsey Hageman; Yanjun Chen; F Lennie Wong; Elizabeth L McQuaid; Christina Duncan; Leo Mascarenhas; David Freyer; Nkechi Mba; Paula Aristizabal; David Walterhouse; Glen Lew; Pamela Helen-Heilge Kempert; Thomas Bennett Russell; Rene Y McNall-Knapp; Shana Jacobs; Ha Dang; Elizabeth Raetz; Mary V Relling; Wendy Landier Journal: JAMA Netw Open Date: 2020-08-03
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