A model based on dynamic hematologic parameters to predict short term clinical events in pediatric acute lymphoblastic leukemia

Background/aim: Acute lymphoblastic leukemia (ALL) treatment is frequently complicated by infections, emergency visits, and therapy interruptions, yet early prediction of short-term clinical deterioration remains challenging. Traditional prognostic markers rely on static laboratory values, whereas dynamic hematologic fluctuations may provide earlier warning signals. This study presents and internally validates a clinically applicable prediction model based on dynamic hematologic parameters and clinician-documented symptoms for predicting short-term (7-day) clinical events in children with ALL. Materials and methods: Included in this retrospective study were 44 pediatric ALL patients treated with Berlin-Frankfurt-Münster-based protocols between January 2023 and June 2025. Weekly observation units were created by aggregating complete blood count values and clinician-documented symptoms. Dynamic hematologic indices included mean absolute neutrophil count (ANC), coefficient of variation (ANC-CV), and time in target range (ANC-TTR). The composite outcome was defined as any of the following occurring within 7 days: unplanned emergency visit, ≥48-h chemotherapy interruption, or infection requiring systemic antibiotics. Mixed-effects logistic regression was used to account for within-patient clustering. Model performance was assessed using discrimination, calibration, decision curve analysis, and bootstrap internal validation. Results: A total of 1136 weekly observations were analyzed. Composite clinical events occurred in 32.3% of weeks. Event weeks demonstrated lower ANC, higher ANC-CV, reduced ANC-TTR, lower hemoglobin levels, and higher symptom burden (all p <0.01). In the hematology-only model, ANC, ANC-CV, ANC-TTR, hemoglobin levels, and platelet counts were independent predictors (AUROC = 0.77). Adding the symptom score improved discrimination (AUROC = 0.83) and calibration. Decision curve analysis demonstrated greater net clinical benefit for the combined model across threshold probabilities of 10-40%. Conclusion: Dynamic hematologic trajectories and clinician-documented symptoms enable accurate early prediction of short-term clinical events in pediatric ALL. This low-cost, accessible prediction model may support individualized risk stratification and proactive supportive care.