Dynamic prediction of overall survival for patients with high-grade extremity soft tissue sarcoma. (December 2018)

• Record Type: Journal Article
• Authors: Jeys, Lee M.; Laitinen, Minna K.; Pollock, Rob; Aston, Will; van der Hage, Jos A.; Dijkstra, PD Sander; Ferguson, Peter C.; Griffin, Anthony M.; Willeumier, Julie J.; Wunder, Jay S.; Styring, Emelie; Posch, Florian; Zaikova, Olga; Maretty-Kongstad, Katja; Keller, Johnny; Leithner, Andreas; Smolle, Maria A.; Haas, Rick L.; Rueten-Budde, A.J.; van Praag, V.M.; van de Sande, M.A.J.; Fiocco, M.
• Is Part Of: Surgical oncology. Volume 27:Number 4(2018)
• Page Start: 695
• Page End: 701
• Publication Date: 2018-12
• Subjects: Dynamic prediction -- Landmark analysis -- Survival -- Soft tissue sarcoma -- Prognostic factor -- Margin; Cancer -- Surgery -- Periodicals; Neoplasms -- surgery -- Periodicals; Cancer -- Chirurgie -- Périodiques; Electronic journals; 616.994059
• DOI: 10.1016/j.suronc.2018.09.003
• Languages: English
• ISSNs: 0960-7404

Abstract

Abstract: Purpose: There is increasing interest in personalized prediction of disease progression for soft tissue sarcoma patients. Currently, available prediction models are limited to predictions from time of surgery or diagnosis. This study updates predictions of overall survival at different times during follow-up by using the concept of dynamic prediction. Patients and methods: Information from 2232 patients with high-grade extremity soft tissue sarcoma, who underwent surgery at 14 specialized sarcoma centers, was used to develop a dynamic prediction model. The model provides updated 5-year survival probabilities from different prediction time points during follow-up. Baseline covariates as well as time-dependent covariates, such as status of local recurrence and distant metastases, were included in the model. In addition, the effect of covariates over time was investigated and modelled accordingly in the prediction model. Results: Surgical margin and tumor histology show a significant time-varying effect on overall survival. The effect of margin is strongest shortly after surgery and diminishes slightly over time. Development of local recurrence and distant metastases during follow-up have a strong effect on overall survival and updated predictions must account for their occurrence. Conclusion: The presence of time-varying effects, as well as the effect of local recurrence and distant metastases on survival, suggest the importance of updating predictions during follow-up. This newly developed dynamic prediction model which updates survival probabilities over time can be used to make better individualized treatment decisions based on a dynamic assessment of a patient's prognosis. Highlights: A dynamic prediction model predicts survival at different times during follow-up. Updated patient information is used for predictions as it becomes available. Local recurrence and distant metastasis are included in the model. The effect of surgical margin changes over time.