Longitudinal Patient-Reported Symptom Change Patterns and Prediction of Future Health-Related Quality of Life in Childhood Cancer Survivors: A Machine Learning Approach from the Childhood Cancer Survivor Study and the St. Jude Lifetime Cohort

Background: Adult survivors of childhood cancer face a significant risk for treatment-related late effects that may impair health-related quality of life (HRQoL). Incorporating longitudinal changes in patient-reported symptoms beyond treatment-based risk factors may enhance the prediction of HRQoL. Methods: Survivors (N = 576) dually enrolled in the St. Jude Lifetime Cohort Study and Childhood Cancer Survivor Study reported 37 symptoms across 10 domains at three time points over 20 years to ascertain longitudinal symptom change patterns. HRQoL was subsequently assessed using SF-36 scores. Prediction models were developed using Bayesian Information Criterion Elastic Net (BIEN), first including demographic, diagnosis, and treatment variables, then adding symptom change patterns. Prediction of suboptimal HRQoL (score < 40) was evaluated using 10-fold cross-validated area under the receiver operating characteristic curve values (AUC). Results: Participants (median baseline age 26.7 years, 52% female, 90% non-Hispanic white, 41% leukemia, and 30% Hodgkin/non-Hodgkin lymphoma survivors) most frequently reported symptom domains of sensory, pain, and anxiety (50–60% at any time point), followed by depression and memory (40–50%). Consistent absence throughout follow-up was the most common pattern (41.7–98.1%), while patterns requiring symptom presence at ≥1 time point were less common (0.0–16.7%), and persistent presence throughout follow-up was rare (0.0–6.8%). Across 10 SF36-HRQoL scores, symptom-enhanced models improved prediction over non-symptom models (AUCs 0.75–0.85 vs. 0.56–0.66; p-values < 0.001). Conclusions: Longitudinal symptom change patterns substantially improved future HRQoL prediction, achieving prediction accuracy that may be of clinical effectiveness. This supports regular symptom assessment and further research towards symptom-informed risk stratification in survivorship care.