Risk stratification is essential for the delivery of optimal treatment in childhood acute lymphoblastic leukemia. However, current risk stratification algorithms dichotomise variables and apply risk factors independently which may wrongly assume identical associations across biologically heterogeneous subsets and reduce statistical power. Accordingly, we developed and validated a prognostic index (PIUKALL) which integrates multiple risk factors and uses continuous data. We created discovery (n=2,405) and validation (n=2,313) cohorts using data from four recent trials (UKALL2003, COALL-03, DCOG-ALL10, NOPHO-ALL2008). Using the discovery cohort, multivariate Cox regression modelling defined a minimal model that included white cell count at diagnosis, pre-treatment cytogenetics and end of induction minimal residual disease. Using this model we defined PIUKALL – a continuous variable that assigns personalised risk scores. The PIUKALL correlated with risk of relapse and validated in an independent cohort. Using PIUKALL to risk stratify patients improved the C-index for all endpoints compared to the traditional algorithms. We used PIUKALL to define four clinically relevant risk groups which had differential relapse rates at 5 years and were similar between the two cohorts: discovery – low 3% (95% CI 2-4), standard 8%(6-10), intermediate 17%(14-21), high 48%(36-60) and validation low 4%(3-6), standard 9%(6-12), intermediate 17%(14-21), high 35%(24-48). An analysis of the area under the curve confirmed the PIUKALL groups were significantly better at predicting outcome than the algorithms employed in each trial. The PIUKALL developed in this study provides an accurate method for predicting outcome and a more flexible method for defining risk groups in future studies.Copyright © 2020 American Society of Hematology.
About The Expert
Amir Enshaei
David O’Connor
Jack Bartram
Jeremy P Hancock
Christine Harrison
Rachael E Hough
Sujith Samarasinghe
Monique L Den Boer
Judith M Boer
Hester A de Groot-Kruseman
Hanne Vibeke Hansen Marquart
Ulrika Noren-Nystrom
Kjeld Schmiegelow
Claire J Schwab
Martin A Horstmann
Gabriele Escherich
Mats Heyman
Rob Pieters
Ajay Vora
John P Moppett
Anthony V Moorman
References
PubMed