Janssen K J M, Moons K G M, Kalkman C J, Grobbee D E, Vergouwe Y
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
J Clin Epidemiol. 2008 Jan;61(1):76-86. doi: 10.1016/j.jclinepi.2007.04.018. Epub 2007 Nov 26.
Ideally, clinical prediction models are generalizable to other patient groups. Unfortunately, they perform regularly worse when validated in new patients and are then often redeveloped. While the original prediction model usually has been developed on a large data set, redevelopment then often occurs on the smaller validation set. Recently, methods to update existing prediction models with the data of new patients have been proposed. We used an existing model that preoperatively predicts the risk of severe postoperative pain (SPP) to compare five updating methods.
The model was tested and updated with a set of 752 new patients (274 [36] with SPP). We studied the discrimination (ability to distinguish between patients with and without SPP) and calibration (agreement between the predicted risks and observed frequencies of SPP) of the five updated models in 283 other patients (100 [35%] with SPP).
Simple recalibration methods improved the calibration to a similar extent as revision methods that made more extensive adjustments to the original model. Discrimination could not be improved by any of the methods.
When the performance is poor in new patients, updating methods can be applied to adjust the model, rather than to develop a new model.
理想情况下，临床预测模型应能推广至其他患者群体。遗憾的是，这些模型在新患者中进行验证时，表现往往更差，随后常常需要重新开发。虽然最初的预测模型通常是基于大型数据集开发的，但重新开发往往是在较小的验证集上进行。最近，有人提出了利用新患者数据更新现有预测模型的方法。我们使用一个术前预测严重术后疼痛（SPP）风险的现有模型，比较了五种更新方法。
该模型用一组752例新患者（274例[36%]发生SPP）进行测试和更新。我们在另外283例患者（100例[35%]发生SPP）中研究了五种更新模型的区分度（区分发生和未发生SPP患者的能力）和校准度（预测风险与观察到的SPP发生频率之间的一致性）。
简单的重新校准方法在校准方面的改善程度与对原始模型进行更广泛调整的修订方法相似。任何方法均无法提高区分度。
当新患者中模型表现不佳时，可应用更新方法来调整模型，而非开发新模型。
