University of Edinburgh, Edinburgh, UK.
University of Lausanne, Lausanne, Switzerland.
Br J Cancer. 2018 Oct;119(7):808-814. doi: 10.1038/s41416-018-0256-x. Epub 2018 Sep 17.
PREDICT is a widely used online prognostication and treatment benefit tool for patients with early stage breast cancer. The aim of this study was to conduct an independent validation exercise of the most up-to-date version of the PREDICT algorithm (version 2) using real-world outcomes from the Scottish population of women with breast cancer.
Patient data were obtained for all Scottish Cancer Registry (SCR) records with a diagnosis of primary invasive breast cancer diagnosed in the period between January 2001 and December 2015. Prognostic scores were calculated using the PREDICT version 2 algorithm. External validity was assessed by statistical analysis of discrimination and calibration. Discrimination was assessed by area under the receiver-operator curve (AUC). Calibration was assessed by comparing the predicted number of deaths to the observed number of deaths across relevant sub-groups.
A total of 45,789 eligible cases were selected from 61,437 individual records. AUC statistics ranged from 0.74 to 0.77. Calibration results showed relatively close agreement between predicted and observed deaths. The 5-year complete follow-up sample reported some overestimation (11.5%), while the 10-year complete follow-up sample displayed more limited overestimation (1.7%).
Validation results suggest that the PREDICT tool remains essentially relevant for contemporary patients with early stage breast cancer.
PREDICT 是一种广泛使用的在线预后和治疗效益工具，适用于早期乳腺癌患者。本研究的目的是使用来自苏格兰乳腺癌人群的真实数据，对 PREDICT 算法的最新版本（版本 2）进行独立验证。
从 2001 年 1 月至 2015 年 12 月期间苏格兰癌症登记处（SCR）诊断为原发性浸润性乳腺癌的所有 SCR 记录中获取患者数据。使用 PREDICT 版本 2 算法计算预后评分。通过区分度和校准的统计分析评估外部有效性。区分度通过接收者操作特征曲线（AUC）下面积评估。校准通过在相关亚组中比较预测死亡人数与实际死亡人数来评估。
从 61437 份个人记录中选择了 45789 例符合条件的病例。AUC 统计数据范围为 0.74 至 0.77。校准结果表明预测死亡人数与实际死亡人数之间存在相对一致的关系。5 年完整随访样本显示出一定程度的高估（11.5%），而 10 年完整随访样本显示出更有限的高估（1.7%）。
验证结果表明，PREDICT 工具对于当代早期乳腺癌患者仍然具有重要意义。
