Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America.
PLoS One. 2012;7(2):e32286. doi: 10.1371/journal.pone.0032286. Epub 2012 Feb 24.
Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.
We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.
We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r(2) for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.
In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting "report cards" or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models.
医院越来越多地根据疾病严重程度调整后的临床结果进行比较。有多种方法可以调整患者之间的差异。对于公众和医疗服务提供者来说，理解风险调整模型之间的差异是一个挑战，尤其是当模型在使用行政和生理数据方面存在差异时。我们旨在研究在应用于危重症患者时，行政模型和生理模型如何相互比较。
我们前瞻性地从美国的两个重症监护病房中提取了生理和行政模型的死亡率变量。通过皮尔逊乘积矩和 Bland-Altman 分析比较预测死亡率。对直接从急诊室入院的患者进行亚组分析，以消除 ICU 入院前病情可能发生的混杂变化。
我们对来自两个学术医疗中心的 556 名患者进行了此项分析。行政模型和生理模型对合并队列的死亡率预测分别为 15.3%（95%CI 13.7%，16.8%）和 24.6%（95%CI 22.7%，26.5%）（t 检验 p 值<0.001）。这两个模型的 r(2)为 0.297。Bland-Atlman 图表明，在低预测死亡率时，两者具有良好的一致性；然而，随着死亡率的增加，模型开始出现分歧。当分析直接从急诊室入院的患者亚组时，得到了相似的结果。当比较两家医院时，使用行政模型时存在统计学差异，但使用生理模型时则没有。两种模型均很少出现无法解释的死亡率，即那些死亡率预测值小于 10%但仍死亡的患者。
总之，虽然已经证明在非危重症肺炎患者中，行政模型提供的死亡率估计与生理模型相似，但我们的结果表明，这一发现不能普遍应用于入住重症监护病房的患者。随着患者和提供者越来越多地使用公开报告的信息来做出医疗保健决策和转介，了解所提供的信息至关重要。我们的结果表明，严重程度可能会影响行政模型中的死亡率指数。我们建议，在解释“报告卡”或指标时，医疗服务提供者应确定风险调整的方法，并与其他风险调整模型进行比较。
