Literature DB >> 34161608

Individual prediction of nonadherence to oral mercaptopurine in children with acute lymphoblastic leukemia: Results from COG AALL03N1.

Anna L Hoppmann1, Yanjun Chen1, Wendy Landier1, Lindsey Hageman1, William E Evans2, F Lennie Wong3, Mary V Relling2, Smita Bhatia1.   

Abstract

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.
© 2021 American Cancer Society.

Entities:  

Keywords:  acute lymphoblastic leukemia; adolescent; child; medication adherence; mercaptopurine

Mesh:

Substances:

Year:  2021        PMID: 34161608      PMCID: PMC8478829          DOI: 10.1002/cncr.33760

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.921


  19 in total

1.  Comparison of self-report and electronic monitoring of 6MP intake in childhood ALL: a Children's Oncology Group study.

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

2.  HPLC determination of thiopurine nucleosides and nucleotides in vivo in lymphoblasts following mercaptopurine therapy.

Authors:  Thierry Dervieux; Yaqin Chu; Yi Su; Ching-Hon Pui; William E Evans; Mary V Relling
Journal:  Clin Chem       Date:  2002-01       Impact factor: 8.327

3.  Inherited NUDT15 variant is a genetic determinant of mercaptopurine intolerance in children with acute lymphoblastic leukemia.

Authors:  Jun J Yang; Wendy Landier; Wenjian Yang; Chengcheng Liu; Lindsey Hageman; Cheng Cheng; Deqing Pei; Yanjun Chen; Kristine R Crews; Nancy Kornegay; F Lennie Wong; William E Evans; Ching-Hon Pui; Smita Bhatia; Mary V Relling
Journal:  J Clin Oncol       Date:  2015-01-26       Impact factor: 44.544

4.  Six months of maintenance chemotherapy after intensified treatment for acute lymphoblastic leukemia of childhood.

Authors:  Y Toyoda; A Manabe; M Tsuchida; R Hanada; K Ikuta; Y Okimoto; A Ohara; Y Ohkawa; T Mori; K Ishimoto; T Sato; T Kaneko; M Maeda; K i Koike; T Shitara; Y Hoshi; R Hosoya; Y Tsunematsu; F Bessho; S Nakazawa; T Saito
Journal:  J Clin Oncol       Date:  2000-04       Impact factor: 44.544

Review 5.  Where do we stand in the treatment of relapsed acute lymphoblastic leukemia?

Authors:  Elizabeth A Raetz; Teena Bhatla
Journal:  Hematology Am Soc Hematol Educ Program       Date:  2012

6.  Mercaptopurine Ingestion Habits, Red Cell Thioguanine Nucleotide Levels, and Relapse Risk in Children With Acute Lymphoblastic Leukemia: A Report From the Children's Oncology Group Study AALL03N1.

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

7.  Assay of 6-mercaptopurine and its metabolites in patient plasma by high-performance liquid chromatography with diode-array detection.

Authors:  Y Su; Y Y Hon; Y Chu; M E Van de Poll; M V Relling
Journal:  J Chromatogr B Biomed Sci Appl       Date:  1999-09-24

8.  Systemic exposure to mercaptopurine as a prognostic factor in acute lymphocytic leukemia in children.

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

Review 9.  Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.

Authors:  Gary S Collins; Johannes B Reitsma; Douglas G Altman; Karel G M Moons
Journal:  BMJ       Date:  2015-01-07

10.  Effect of a Daily Text Messaging and Directly Supervised Therapy Intervention on Oral Mercaptopurine Adherence in Children With Acute Lymphoblastic Leukemia: A Randomized Clinical Trial.

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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  3 in total

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Review 3.  Social determinants of health and pediatric cancer survival: A systematic review.

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  3 in total

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