Epidemiology and Biostatistics, School of Population Health, University of Auckland, 261 Morrin Road, Private Bag 92019, Auckland Mail Centre, Auckland, 1142, New Zealand.
Waikato Clinical Campus, Department of Surgery, University of Auckland, Hamilton, New Zealand.
BMC Cancer. 2018 Sep 17;18(1):897. doi: 10.1186/s12885-018-4791-x.
The only available predictive models for the outcome of breast cancer patients in New Zealand (NZ) are based on data in other countries. We aimed to develop and validate a predictive model using NZ data for this population, and compare its performance to a widely used overseas model, the Nottingham Prognostic Index (NPI).
We developed a model to predict 10-year breast cancer-specific survival, using data collected prospectively in the largest population-based regional 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 Dec 31, 2014. We used multivariate Cox proportional hazards regression to assess predictors and to calculate predicted 10-year breast cancer mortality, and therefore survival, probability 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 NZ model with the Nottingham Prognostic Index (NPI) in this validation data set.
Discrimination was good: C statistics were 0.84 for internal validity and 0.83 for an independent external validity. For calibration, for both internal and external validity the predicted 10-year survival probabilities in all groups of patients, ordered by predicted survival, were within the 95% confidence intervals (CI) of the observed Kaplan-Meier survival probabilities. The NZ model showed good discrimination even within the prognostic groups defined by the NPI.
These results for the New Zealand model show good internal and external validity, transportability, and potential clinical value of the model, and its clear superiority over the NPI. Further research is needed to assess other potential predictors, to assess the model's performance in specific subgroups of patients, and to compare it to other models, which have been developed in other countries and have not yet been tested in NZ.
新西兰(NZ)乳腺癌患者结局的唯一可用预测模型均基于其他国家的数据。我们旨在使用 NZ 数据为该人群开发和验证一个预测模型,并将其性能与广泛使用的海外模型,即诺丁汉预后指数(NPI)进行比较。
我们使用在 NZ 最大的基于人群的区域性乳腺癌登记处(奥克兰,9182 例患者)前瞻性收集的数据,开发了一个预测 10 年乳腺癌特异性生存的模型,并在该数据集(内部验证)和奥克兰另一个基于人群的 2625 例患者系列(外部验证)中评估其性能。数据包括 2000 年 6 月 1 日至 2014 年 6 月 30 日期间诊断为原发性浸润性乳腺癌的所有女性,随访至死亡或 2014 年 12 月 31 日。我们使用多变量 Cox 比例风险回归来评估预测因子,并计算每位患者的 10 年乳腺癌死亡率和生存概率。我们使用 Kaplan-Meier 方法评估观察到的生存情况。我们通过 C 统计量评估区分度,并通过比较按预测 10 年生存率排序的 10 组患者的预测和观察生存率来评估校准情况。我们在验证数据集中将此 NZ 模型与诺丁汉预后指数(NPI)进行了比较。
区分度良好:内部有效性的 C 统计量为 0.84,外部有效性的 C 统计量为 0.83。对于校准,对于内部和外部有效性,所有患者的预测 10 年生存率组,按预测生存率排序,预测 10 年生存率的概率均在观察到的 Kaplan-Meier 生存率概率的 95%置信区间(CI)内。即使在 NPI 定义的预后组内,新西兰模型也显示出良好的区分度。
这些新西兰模型的结果表明该模型具有良好的内部和外部有效性、可转移性和潜在的临床价值,并且明显优于 NPI。需要进一步研究来评估其他潜在预测因子,评估该模型在特定患者亚组中的性能,并将其与其他在其他国家开发但尚未在 NZ 测试的模型进行比较。