A validated novel continuous prognostic index to deliver stratified medicine in pediatric acute lymphoblastic leukemia

Autor: Rob Pieters, Christine J. Harrison, Hanne Vibeke Marquart, John Moppett, Ajay Vora, Martin A. Horstmann, Gabriele Escherich, Jack Bartram, Jeremy Hancock, Mats Heyman, Monique L. den Boer, Amir Enshaei, David O'Connor, Judith M. Boer, Claire Schwab, Ulrika Norén-Nyström, Hester A. de Groot-Kruseman, Rachael Hough, Kjeld Schmiegelow, Sujith Samarasinghe, Anthony V. Moorman
Rok vydání: 2020
Předmět:
Male
medicine.medical_specialty
Neoplasm
Residual
Adolescent
Immunology
Biochemistry
Biomarkers
Tumor/analysis
Precursor Cell Lymphoblastic Leukemia-Lymphoma/pathology
Risk Factors
Internal medicine
Outcome Assessment
Health Care
Biomarkers
Tumor
Humans
Medicine
Outcome Assessment
Health Care/statistics & numerical data
Child
Neoplasm Recurrence
Local/pathology
Survival rate
Childhood Acute Lymphoblastic Leukemia
Neoplasm
Residual/pathology
Retrospective Studies
business.industry
Surrogate endpoint
Proportional hazards model
Patient Selection
Infant
Retrospective cohort study
Cell Biology
Hematology
Precursor Cell Lymphoblastic Leukemia-Lymphoma
Prognosis
Combined Modality Therapy
Minimal residual disease
Confidence interval
Survival Rate
Child
Preschool
Cohort
Female
Neoplasm Recurrence
Local
business
Follow-Up Studies
Zdroj: Enshaei, A, O'Connor, D, Bartram, J, Hancock, J, Harrison, C J, Hough, R, Samarasinghe, S, den Boer, M L, Boer, J M, de Groot-Kruseman, H A, Marquart, H V, Noren-Nystrom, U, Schmiegelow, K, Schwab, C, Horstmann, M A, Escherich, G, Heyman, M, Pieters, R, Vora, A, Moppett, J & Moorman, A V 2020, ' A validated novel continuous prognostic index to deliver stratified medicine in pediatric acute lymphoblastic leukemia ', Blood, vol. 135, no. 17, pp. 1438-1446 . https://doi.org/10.1182/blood.2019003191
ISSN: 1528-0020
0006-4971
DOI: 10.1182/blood.2019003191
Popis: Risk stratification is essential for the delivery of optimal treatment in childhood acute lymphoblastic leukemia. However, current risk stratification algorithms dichotomize variables and apply risk factors independently, which may incorrectly assume identical associations across biologically heterogeneous subsets and reduce statistical power. Accordingly, we developed and validated a prognostic index (PIUKALL) that integrates multiple risk factors and uses continuous data. We created discovery (n = 2405) and validation (n = 2313) cohorts using data from 4 recent trials (UKALL2003, COALL-03, DCOG-ALL10, and NOPHO-ALL2008). Using the discovery cohort, multivariate Cox regression modeling defined a minimal model including white cell count at diagnosis, pretreatment cytogenetics, and end-of-induction minimal residual disease. Using this model, we defined PIUKALL as a continuous variable that assigns personalized risk scores. PIUKALL correlated with risk of relapse and was validated in an independent cohort. Using PIUKALL to risk stratify patients improved the concordance index for all end points compared with traditional algorithms. We used PIUKALL to define 4 clinically relevant risk groups that had differential relapse rates at 5 years and were similar between the 2 cohorts (discovery: low, 3% [95% confidence interval (CI), 2%-4%]; standard, 8% [95% CI, 6%-10%]; intermediate, 17% [95% CI, 14%-21%]; and high, 48% [95% CI, 36%-60%; validation: low, 4% [95% CI, 3%-6%]; standard, 9% [95% CI, 6%-12%]; intermediate, 17% [95% CI, 14%-21%]; and high, 35% [95% CI, 24%-48%]). Analysis of the area under the curve confirmed the PIUKALL groups were significantly better at predicting outcome than algorithms employed in each trial. PIUKALL provides an accurate method for predicting outcome and more flexible method for defining risk groups in future studies.
Databáze: OpenAIRE
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