External Validation of the Individualized Prediction of Breast Cancer Survival (IPBS) Model for Estimating Survival after Surgery for Patients with Breast Cancer in Northern Thailand

SIMPLE SUMMARY: Recently, a prediction model was developed specifically for predicting the probability of survival and disease progression five years after diagnosis for Thai female patients with breast cancer. The model was composed of twelve routinely available clinical predictors. Even though it seemed to provide accurate predictions in the development dataset, it has never been tested in other external datasets. This study validated this new prediction model, entitled IPBS (Individualized Prediction of Breast cancer Survival), in another dataset of Northern Thai female patients with breast cancer and found that the model carried an acceptable discriminative ability comparable to when it was developed. Nonetheless, model recalibration to each specific context is encouraged, as it may overestimate the probability of events when the underlying baseline survival of the patient cohort is different from the development dataset. ABSTRACT: The individualized prediction of breast cancer survival (IPBS) model was recently developed. Although the model showed acceptable performance during derivation, its external performance remained unknown. This study aimed to validate the IPBS model using the data of breast cancer patients in Northern Thailand. An external validation study was conducted based on female patients with breast cancer who underwent surgery at Maharaj Nakorn Chiang Mai hospital from 2005 to 2015. Data on IPBS predictors were collected. The endpoints were 5-year overall survival (OS) and disease-free survival (DFS). The model performance was evaluated in terms of discrimination and calibration. Missing data were handled with multiple imputation. Of all 3581 eligible patients, 1868 were included. The 5-year OS and DFS were 85.2% and 81.9%. The IPBS model showed acceptable discrimination: C-statistics 0.706 to 0.728 for OS and 0.675 to 0.689 for DFS at 5 years. However, the IPBS model minimally overestimated both OS and DFS predictions. These overestimations were corrected after model recalibration. In this external validation study, the IPBS model exhibited good discriminative ability. Although it may provide minimal overestimation, recalibrating the model to the local context is a practical solution to improve the model calibration.