Validation of prediction models: examining temporal and geographic stability of baseline risk and estimated covariate effects.
Validation of prediction models: examining temporal and geographic stability of baseline risk and estimated covariate effects.
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DOI：
10.1186/s41512-017-0012-3
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发表时间：
2017-01-01
影响因子：
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通讯作者：
Steyerberg, Ewout W
中科院分区：
文献类型：
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作者：
Austin, Peter C;van Klaveren, David;Steyerberg, Ewout W
背景技术背景：基线风险和估计的预测效应在地理上和时间上的稳定性是临床预测模型的理想属性。然而，这个问题在方法论文献中很少受到关注。我们的目的是研究方法评估时间和地理异质性的基线风险和预测的预测models.METHODS：我们研究了14,857例心力衰竭住院患者在90家医院在安大略，加拿大，在两个时间段。我们重点研究了用于预测因心力衰竭住院患者1年死亡率的APTT-HF死亡率模型的基线风险（截距）和预测效应（回归系数）的地理和时间变化。我们使用随机效应逻辑回归模型为14，857 patients.RESULTS：死亡率的基线风险显示中度地理变异，与医院特定的概率1年死亡率为一个参考病人躺在0.168和0.290之间的95%的医院。此外，第二阶段的死亡几率比第一阶段低11%。然而，我们发现最小的地理或时间变化的预测效果。在预测变量的时间差异的11个测试中，只有一个有中度显着的P值（0.03）。结论：这项研究说明了如何时间和地理异质性的预测模型，可以评估在不同的时间段，从大量的中心，在一个大样本的患者设置。
BACKGROUND: Stability in baseline risk and estimated predictor effects both geographically and temporally is a desirable property of clinical prediction models. However, this issue has received little attention in the methodological literature. Our objective was to examine methods for assessing temporal and geographic heterogeneity in baseline risk and predictor effects in prediction models.METHODS: We studied 14,857 patients hospitalized with heart failure at 90 hospitals in Ontario, Canada, in two time periods. We focussed on geographic and temporal variation in baseline risk (intercept) and predictor effects (regression coefficients) of the EFFECT-HF mortality model for predicting 1-year mortality in patients hospitalized for heart failure. We used random effects logistic regression models for the 14,857 patients.RESULTS: The baseline risk of mortality displayed moderate geographic variation, with the hospital-specific probability of 1-year mortality for a reference patient lying between 0.168 and 0.290 for 95% of hospitals. Furthermore, the odds of death were 11% lower in the second period than in the first period. However, we found minimal geographic or temporal variation in predictor effects. Among 11 tests of differences in time for predictor variables, only one had a modestly significant P value (0.03).CONCLUSIONS: This study illustrates how temporal and geographic heterogeneity of prediction models can be assessed in settings with a large sample of patients from a large number of centers at different time periods.