Open AccessA New Predictive Model for Breast Cancer Survival in New Zealand: Development, Internal and External Validation, and Comparison With the Nottingham Prognostic Index
Author(s) -
Mark Elwood,
Sandar Tin Tin,
Essa Tawfiq,
Roger Marshall,
Minh Tung Phung,
Ross Lawrenson,
Ian Campbell,
Ver Harvey
Publication year - 2018
Publication title -
journal of global oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.002
H-Index - 17
ISSN - 2378-9506
DOI - 10.1200/jgo.18.91800
Subject(s) - medicine , breast cancer , proportional hazards model , nottingham prognostic index , population , multivariate analysis , multivariate statistics , cancer registry , oncology , cancer , statistic , survival analysis , gynecology , statistics , mathematics , environmental health
Background: Women diagnosed with breast cancer, their doctors, and their families, would find a valid estimate of her prognosis helpful in planning treatment and support. Assessing prognosis is complex as many factors influence it. Several predictive models have been produced, but none has been developed or tested on patients in New Zealand (NZ). Aim: We aimed to develop and validate a NZ predictive model (NZPM) for breast cancer, and compare its performance to a widely used UK-developed model, the Nottingham Prognostic Index (NPI). Methods: We developed a model to predict 10-year breast cancer-specific survival, using data collected prospectively in the largest population-based breast cancer registry in NZ (Auckland, 9182 patients), and assessed its performance in this data set (internal validation) and in an independent NZ population-based series of 2625 patients in Waikato (external validation). The data included all women with primary invasive breast cancer diagnosed from 1 June 2000 to 30 June 2014, with follow-up to death or to 31 December 2014. We used multivariate Cox proportional hazards regression to assess predictors and to estimate the probability of breast cancer mortality within 10 years, and therefore 10-year survival, for each patient. We assessed observed survival by the Kaplan-Meier method. We assessed discrimination by the C-statistic, and calibration by comparing predicted and observed survival rates for patients in 10 groups ordered by predicted 10-year survival. We compared this NZPM with the NPI in the validation data set. Results: The final NZPM used continuous variables of age, tumor size, and number of positive lymph nodes, and categorical variables of ethnicity, tumor stage, tumor grade, ER and PR receptors, HER2 status, and histologic type of tumor. Discrimination was good: C-statistics were 0.84 for internal validity and 0.83 for independent external validity. For calibration, for both internal and external validity, the predicted 10-year survival probabilities in 10 groups of patients, ordered by predicted survival, were all within the 95% confidence intervals (CI) of the observed Kaplan-Meier survival probabilities. The NZPM showed good discrimination even within the prognostic groups defined by the NPI. Conclusion: These results for the NZPM show good internal and external validity, transportability, potential clinical value, and its clear superiority over the NPI. Further research will assess other potential predictors, other outcomes, performance in specific subgroups of patients, and compare the NZPM to other models, which have been developed in other countries and have not yet been tested in NZ.