Center for Outcomes Research, 3535 Market Street, Suite 1029, Philadelphia, PA 19104, USA.
Health Serv Res. 2010 Oct;45(5 Pt 1):1148-67. doi: 10.1111/j.1475-6773.2010.01130.x.
We ask whether Medicare's Hospital Compare random effects model correctly assesses acute myocardial infarction (AMI) hospital mortality rates when there is a volume-outcome relationship.
DATA SOURCES/STUDY SETTING: Medicare claims on 208,157 AMI patients admitted in 3,629 acute care hospitals throughout the United States.
We compared average-adjusted mortality using logistic regression with average adjusted mortality based on the Hospital Compare random effects model. We then fit random effects models with the same patient variables as in Medicare's Hospital Compare mortality model but also included terms for hospital Medicare AMI volume and another model that additionally included other hospital characteristics.
Hospital Compare's average adjusted mortality significantly underestimates average observed death rates in small volume hospitals. Placing hospital volume in the Hospital Compare model significantly improved predictions.
The Hospital Compare random effects model underestimates the typically poorer performance of low-volume hospitals. Placing hospital volume in the Hospital Compare model, and possibly other important hospital characteristics, appears indicated when using a random effects model to predict outcomes. Care must be taken to insure the proper method of reporting such models, especially if hospital characteristics are included in the random effects model.
我们研究了医疗保险的医院比较随机效应模型在存在量效关系时,是否能正确评估急性心肌梗死(AMI)医院死亡率。
数据来源/研究范围:美国 3629 家急症护理医院的 208157 名 AMI 患者的医疗保险索赔。
我们使用逻辑回归比较了平均调整后的死亡率和基于医院比较随机效应模型的平均调整后的死亡率。然后,我们使用与医疗保险医院死亡率模型相同的患者变量拟合了随机效应模型,但还包括了医院 Medicare AMI 量的术语和另一个模型,该模型还包括其他医院特征。
医院比较的平均调整后的死亡率显著低估了小容量医院的平均观察死亡率。在医院比较模型中放置医院容量显著提高了预测效果。
医院比较的随机效应模型低估了低容量医院通常较差的表现。在使用随机效应模型预测结果时,将医院容量放入医院比较模型中,并可能放入其他重要的医院特征,似乎是合适的。在报告此类模型时,必须小心确保使用正确的方法,特别是如果医院特征包含在随机效应模型中。