Abstract 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.