Development and validation of a joint model for dynamic prediction of overall survival in nasopharyngeal carcinoma based on longitudinal post-treatment plasma cell-free Epstein-Barr virus DNA load

Objectives: To develop and validate a joint model for dynamic prediction of overall survival (OS) in nasopha-ryngeal carcinoma (NPC) based on longitudinal post-treatment plasma cell-free Epstein-Barr virus (cfEBV) DNA load.Patients and methods: We analyzed 695 patients with non-metastatic NPC and detectable post-treatment cfEBV DNA load who did not receive adjuvant therapy. We fitted the trajectories of post-treatment cfEBV DNA load as a function of time into a linear mixed-effect model and fitted a Cox regression model with covariates including age, T and N stages, and lactate dehydrogenase level. Finally, we combined both via joint modeling to develop and validate our dynamic model.Results: A strong positive correlation was found between the individual longitudinal post-treatment cfEBV DNA load and the risk of death from any cause (P < 0.001). We developed a joint model capable of providing subject -specific dynamic prediction of conditional OS based on the evolution of the individual plasma cfEBV DNA load trajectory. The joint model showed reliable performance in both training and validation cohorts, with a large area under the curve (interquartile range [IQR]: training cohort, 0.775-0.850; validation cohort, 0.826-0.900) and low prediction errors (IQR: training cohort, 0.017-0.078; validation cohort, 0.034 -0.071). An increasing amount of data on cfEBV DNA load was associated with better model performance.Conclusion: Our model provided reliable subject-specific dynamic prediction of conditional OS, which could help guide individualized post-treatment surveillance, risk stratification, and management of NPC in the future.

Automated summary

The study developed and validated a joint model for dynamic prediction of overall survival in nasopharyngeal carcinoma patients based on post-treatment plasma cfEBV DNA load. The model showed reliable performance and could provide subject-specific dynamic prediction for personalized post-treatment surveillance and risk stratification.