Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

Logo MUG-Forschungsportal

Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid

Rueten-Budde, AJ; van Praag, VM; PERSARC studygroup; van de Sande, MAJ; Fiocco, M.
Dynamic prediction of overall survival for patients with high-grade extremity soft tissue sarcoma.
Surg Oncol. 2018; 27(4): 695-701. [OPEN ACCESS]
Web of Science PubMed FullText FullText_MUG

 

Study Group Mitglieder der Med Uni Graz:
Leithner Andreas
Posch Florian
Smolle Maria
Altmetrics:

Dimensions Citations:

Plum Analytics:
Abstract:
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. 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. 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. 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. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Find related publications in this database (Keywords)
Dynamic prediction
Landmark analysis
Survival
Soft tissue sarcoma
Prognostic factor
Margin
© Med Uni Graz Impressum