Rationale, aims, and objectives: To externally validate the PREDICT tool in a cohort of women participating in a population-based breast cancer screening programme who were diagnosed with breast cancer between 2000 and 2008 in Spain.
Methods: A total of 535 women were included in the validation study. We calculated predicted 5-year survival using the beta values from the development of the PREDICT model and predicted and observed events for a given risk groups. Model fit, discrimination, and calibration were evaluated. Seeking to improve the model, we also explored the impact on discrimination of the inclusion of additional variables, not in the PREDICT algorithm.
Results: In patients who were oestrogen receptor (ER) positive (negative), PREDICT overestimated (underestimated) the 5-year overall survival in all the subgroups studied. Analysis of model performance showed good calibration but modest discrimination (C-index, 0.697 [ER negative] and 0.768 [ER positive]). When updating the model, no additional variables were found to be significant in ER-negative patients, but for ER-positive patients, concurrent liver disease was a significant factor, its inclusion improving model discrimination (C-index, 0.817).
Conclusions: The PREDICT tool does not discriminate well in our population considering only the variables of the original algorithm. More accurate tools are needed to obtain a better discrimination.
Keywords: PREDICT; breast cancer; external validation; model; screening programme.
© 2018 John Wiley & Sons, Ltd.