Dynamic prediction of overall survival: a retrospective analysis on 979 patients with Ewing sarcoma from the German registry

• Main Authors: Chuchu Liu, Anja J Rueten-Budde, Andreas Ranft, Uta Dirksen
• Format: Article
• Language: English
• Published: BMJ Publishing Group 2020-10-01
• Series: BMJ Open
• Online Access: https://bmjopen.bmj.com/content/10/10/e036376.full

Objectives This study aimed at developing a dynamic prediction model for patients with Ewing sarcoma (ES) to provide predictions at different follow-up times. During follow-up, disease-related information becomes available, which has an impact on a patient’s prognosis. Many prediction models include predictors available at baseline and do not consider the evolution of disease over time.Setting In the analysis, 979 patients with ES from the Gesellschaft für Pädiatrische Onkologie und Hämatologie registry, who underwent surgery and treatment between 1999 and 2009, were included.Design A dynamic prediction model was developed to predict updated 5-year survival probabilities from different prediction time points during follow-up. Time-dependent variables, such as local recurrence (LR) and distant metastasis (DM), as well as covariates measured at baseline, were included in the model. The time effects of covariates were investigated by using interaction terms between each variable and time.Results Developing LR, DM in the lungs (DMp) or extrapulmonary DM (DMo) has a strong effect on the probability of surviving an additional 5 years with HRs and 95% CIs equal to 20.881 (14.365 to 30.353), 6.759 (4.465 to 10.230) and 17.532 (13.210 to 23.268), respectively. The effects of primary tumour location, postoperative radiotherapy (PORT), histological response and disease extent at diagnosis on survival were found to change over time. The HR of PORT versus no PORT at the time of surgery is equal to 0.774 (0.594 to 1.008). One year after surgery, the HR is equal to 1.091 (0.851 to 1.397).Conclusions The time-varying effects of several baseline variables, as well as the strong impact of time-dependent variables, show the importance of including updated information collected during follow-up in the prediction model to provide accurate predictions of survival.