1. External validation and adaptation of a dynamic prediction model for patients with high-grade extremity soft tissue sarcoma
- Author
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PERSARC Study Group, Rueten-Budde, Anja J., van Praag, Veroniek M., van de Sande, Michiel A. J., Fiocco, Marta, Aston, Will, Bonenkamp, Han, Callegaro, Dario, Dijkstra, P. D. Sander, Ferguson, Peter C., Griffin, Anthony M., Gronchi, Alessandro, Grünhagen, Dirk, Haas, Rick, Hayes, Andrew, Jeys, Lee M, Keller, Johnny, Laitinen, Minna K., Leithner, Andreas, Maretty-Kongstad, Katja, Pollock, Rob, Posch, Florian, Smith, Myles, Smolle, Maria, Styring, Emelie, Tunn, Per-Ulf, van der Hage, Jos A., van Ginkel, Robert, van Houdt, Winan, Verhoef, Kees, Willegger, Madeleine, Willeumier, Julie J., Windhager, Reinhard, Wunder, Jay S., Zaikova, Olga, HUS Musculoskeletal and Plastic Surgery, I kirurgian klinikka (Töölö), Department of Surgery, and Surgery
- Subjects
Male ,medicine.medical_specialty ,Dynamic prediction ,Calibration (statistics) ,survival ,03 medical and health sciences ,0302 clinical medicine ,external validation ,medicine ,Humans ,Model development ,Time point ,Retrospective Studies ,Models, Statistical ,business.industry ,Soft tissue sarcoma ,Melanoma/Sarcoma ,External validation ,dynamic prediction ,Extremities ,Sarcoma ,General Medicine ,Middle Aged ,medicine.disease ,3126 Surgery, anesthesiology, intensive care, radiology ,3. Good health ,Survival Rate ,Nomograms ,landmark analysis ,Oncology ,030220 oncology & carcinogenesis ,soft tissue sarcoma ,Cohort ,030211 gastroenterology & hepatology ,Surgery ,Female ,Radiology ,Neoplasm Grading ,Neoplasm Recurrence, Local ,business ,Follow-Up Studies - Abstract
Background and Objectives: A dynamic prediction model for patients with soft tissue sarcoma of the extremities was previously developed to predict updated overall survival probabilities throughout patient follow‐up. This study updates and externally validates the dynamic model. Methods: Data from 3826 patients with high‐grade extremity soft tissue sarcoma, treated surgically with curative intent were used to update the dynamic PERsonalised SARcoma Care (PERSARC) model. Patients were added to the model development cohort and grade was included in the model. External validation was performed with data from 1111 patients treated at a single tertiary center. Results: Calibration plots show good model calibration. Dynamic C‐indices suggest that the model can discriminate between high‐ and low‐risk patients. The dynamic C‐indices at 0, 1, 2, 3, 4, and 5 years after surgery were equal to 0.697, 0.790, 0.822, 0.818, 0.812, and 0.827, respectively. Conclusion: Results from the external validation show that the dynamic PERSARC model is reliable in predicting the probability of surviving an additional 5 years from a specific prediction time point during follow‐up. The model combines patient‐, treatment‐specific and time‐dependent variables such as local recurrence and distant metastasis to provide accurate survival predictions throughout follow‐up and is available through the PERSARC app.
- Published
- 2021