Department of Cardiology, The Texas Heart Institute at St. Luke's Episcopal Hospital, Houston, Texas, United States.
Department of Cardiology, The Texas Heart Institute at St. Luke's Episcopal Hospital, Houston, Texas, United States; Department of Biostatistics and Epidemiology, The Texas Heart Institute at St. Luke's Episcopal Hospital, Houston, Texas, United States.
Int J Cardiol. 2011 Jun 2;149(2):227-231. doi: 10.1016/j.ijcard.2010.02.005. Epub 2010 Mar 4.
We derived a risk-assessment model for predicting mortality after coronary artery bypass surgery from patient data from the 1990s and examined the model's accuracy in predicting early mortality in more contemporary patients. We also examined the accuracy of a completely new model and an older model recalibrated with newer data.
Three mortality-prediction models were derived: an "old" model from 8959 patients treated during 1993-1999, a "new" model from 5281 patients treated during 2000-2004, and a version of the old model "recalibrated" with the 2000-2004 data. Each model's discriminatory ability was assessed by computing area under receiver-operator characteristic (ROC) curves, and precision was estimated by comparing observed and predicted mortality rates. To test the temporal applicability of the old model, we applied it to the 2000-2004 data and to data from 1879 patients operated on during 2005-2007. To compare the recalibration and remodeling strategies, the new and recalibrated models were applied to the 2005-2007 data.
The old model showed good discrimination (ROC, 0.80) and concordance between observed and predicted mortality for the 2000-2004 patients but overpredicted mortality for the 2005-2007 patients. The new and recalibrated models had good discriminatory ability (ROC, 0.81 and 0.79) and showed similarly good concordance between observed and predicted mortality when applied to the 2005-2007 data.
Predictive models for mortality after cardiac surgery lose precision within a few years after development. Recalibrating old models and creating new models (i.e., remodeling) are equally good strategies for predicting outcomes in contemporary patient cohorts.
我们从 20 世纪 90 年代的患者数据中得出了一种预测冠状动脉旁路手术后死亡率的风险评估模型，并检验了该模型在预测更现代患者的早期死亡率方面的准确性。我们还检验了一个全新模型和一个使用更新数据重新校准的旧模型的准确性。
我们得出了三种死亡率预测模型：一个来自于 1993 年至 1999 年期间接受治疗的 8959 名患者的“旧”模型，一个来自于 2000 年至 2004 年期间接受治疗的 5281 名患者的“新”模型，以及一个使用 2000 年至 2004 年数据重新校准的“旧”模型的版本。通过计算接收者操作特性（ROC）曲线下的面积来评估每个模型的判别能力，并通过比较观察到的和预测的死亡率来估计精度。为了检验旧模型的时间适用性，我们将其应用于 2000-2004 年的数据以及 2005-2007 年接受手术的 1879 名患者的数据。为了比较重新校准和重塑策略，我们将新模型和重新校准的模型应用于 2005-2007 年的数据。
旧模型对 2000-2004 年的患者显示出良好的区分能力（ROC 为 0.80）和观察到的与预测的死亡率之间的一致性，但对 2005-2007 年的患者过度预测了死亡率。新模型和重新校准的模型具有良好的判别能力（ROC 为 0.81 和 0.79），并且当应用于 2005-2007 年的数据时，观察到的与预测的死亡率之间也具有相似的一致性。
心脏手术后死亡率的预测模型在开发后的几年内精度会降低。重新校准旧模型和创建新模型（即重塑）对于预测当代患者队列的结果是同等有效的策略。
