External validation and adaptation of a dynamic prediction model for patients with high-grade extremity soft tissue sarcoma. 2021

Anja J Rueten-Budde, and Veroniek M van Praag, and Michiel A J van de Sande, and Marta Fiocco, and
Mathematical Institute, Leiden University, Leiden, The Netherlands.

OBJECTIVE 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. CONCLUSIONS 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.

UI MeSH Term Description Entries
D008297 Male Males
D008875 Middle Aged An adult aged 45 - 64 years. Middle Age
D009364 Neoplasm Recurrence, Local The local recurrence of a neoplasm following treatment. It arises from microscopic cells of the original neoplasm that have escaped therapeutic intervention and later become clinically visible at the original site. Local Neoplasm Recurrence,Local Neoplasm Recurrences,Locoregional Neoplasm Recurrence,Neoplasm Recurrence, Locoregional,Neoplasm Recurrences, Local,Recurrence, Local Neoplasm,Recurrence, Locoregional Neoplasm,Recurrences, Local Neoplasm,Locoregional Neoplasm Recurrences,Neoplasm Recurrences, Locoregional,Recurrences, Locoregional Neoplasm
D005121 Extremities The farthest or outermost projections of the body, such as the HAND and FOOT. Limbs,Extremity,Limb
D005260 Female Females
D005500 Follow-Up Studies Studies in which individuals or populations are followed to assess the outcome of exposures, procedures, or effects of a characteristic, e.g., occurrence of disease. Followup Studies,Follow Up Studies,Follow-Up Study,Followup Study,Studies, Follow-Up,Studies, Followup,Study, Follow-Up,Study, Followup
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D012189 Retrospective Studies Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons. Retrospective Study,Studies, Retrospective,Study, Retrospective
D012509 Sarcoma A connective tissue neoplasm formed by proliferation of mesodermal cells; it is usually highly malignant. Sarcoma, Epithelioid,Sarcoma, Soft Tissue,Sarcoma, Spindle Cell,Epithelioid Sarcoma,Epithelioid Sarcomas,Sarcomas,Sarcomas, Epithelioid,Sarcomas, Soft Tissue,Sarcomas, Spindle Cell,Soft Tissue Sarcoma,Soft Tissue Sarcomas,Spindle Cell Sarcoma,Spindle Cell Sarcomas
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model

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