Updating methods improved the performance of a clinical prediction model in new patients
Updating methods improved the performance of a clinical prediction model in new patients
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DOI：
10.1016/j.jclinepi.2007.04.018
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发表时间：
2008-01-01
影响因子：
7.2
通讯作者：
Vergouwe, Y.
中科院分区：
文献类型：
--
作者：
Janssen, K. J. M.;Moons, K. G. M.;Vergouwe, Y.
目的：理想情况下，临床预测模型可推广到其他患者群体。不幸的是，当在新患者中验证时，它们的表现通常会更差，然后通常会重新开发。虽然原始预测模型通常是在大数据集上开发的，但重新开发通常发生在较小的验证集上。我们使用了一个现有的模型，术前预测严重术后疼痛（SPP）的风险，比较五个更新的methods.Study设计和设置：该模型进行了测试，并更新了一组752名新患者（274 [36]与SPP）。我们研究了283例其他患者（100例[35%]患有SPP）的5个更新模型的区分（区分患有和不患有SPP的患者的能力）和校准（预测风险与观察到的SPP频率之间的一致性）。结果：简单的重新校准方法将校准提高到与对原始模型进行更广泛调整的修订方法相似的程度。结论：当新患者的识别效果不佳时，可以采用更新方法对模型进行调整，而不是建立新的模型。(C)2008年爱思唯尔公司All rights reserved.
Objective: 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.Study Design and Setting: 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).Results: 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.Conclusion: When the performance is poor in new patients, updating methods can be applied to adjust the model, rather than to develop a new model. (C) 2008 Elsevier Inc. All rights reserved.