The Effect of Preexisting Conditions on Hospital Quality Measurement for Injured Patients
The Effect of Preexisting Conditions on Hospital Quality Measurement for Injured Patients
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DOI：
10.1097/sla.0b013e3181d56770
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发表时间：
2010-04-01
影响因子：
9
通讯作者：
Osler, Turner M.
中科院分区：
文献类型：
--
作者：
Glance, Laurent G.;Dick, Andrew W.;Osler, Turner M.
目的：为了确定是否调整合并症显着影响医院的质量测量相比，调整损伤severity.Background：预先存在的条件有一个显着的影响损伤后的死亡率。包括合并症对医院质量measurement.Methods的影响：回顾性队列研究使用医疗保健成本和利用项目全国住院患者样本（2005-2006年）。对创伤死亡概率模型（TMPM-ICD 9）进行了重新估计，在卫生研究和质量共病算法中添加和不添加共病指标。医院质量的测量使用调整后的比值比（OR），通过分层逻辑回归模型获得。OR量化了在特定医院接受治疗的创伤患者与在普通医院接受治疗的患者相比死亡的可能性。调整后OR显著大于或小于1的医院分别被归类为低质量或高质量离群值。使用组内相关系数、斯皮尔曼相关系数、Bland-Altman图和kappa statistics.Results对基于TMPM-ICD 9的有和无合并症信息的医院质量进行成对比较。组内相关系数为0.943（95% CI，0.931-0.951），斯皮尔曼相关系数为0.953（95% CI，0.944-0.960），kappa统计量为0.863（95% CI，0.792-0.934）。病人在最差的5%医院死亡的几率是1.73（95%CI，1.61-1.86），而在最好的5%的医院中，患者死亡的几率为0.37（95% CI，0.31-0.44）。在这项对511家医院的148，280名创伤患者进行的大型研究中，我们没有发现证据表明将合并症添加到用于基准医院绩效的风险调整模型中会改变医院排名。此外，表现最好和最差的医院之间的死亡率结果似乎存在显著差异。医院之间结果的这种差异可能代表了改善受伤患者健康状况的重要机会。
Objective: To determine whether adjusting for comorbidities significantly affects hospital quality measurement compared with adjusting for injury severity alone.Background: Pre-existing conditions have a significant impact on mortality after injury. The impact of including comorbidities on hospital quality measurement is not well understood.Methods: Retrospective cohort study using the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (2005-2006). The Trauma Mortality Probability Model (TMPM-ICD9) was re-estimated with and without the addition of the comorbidity measures in the Agency for Health Research and Quality comorbidity algorithm. Hospital quality was measured using an adjusted odds ratio (OR) obtained using hierarchical logistic regression modeling. The OR quantifies the likelihood that trauma patients treated at a specific hospital are more or less likely to die compared with patients treated at an average hospital. Hospitals with an adjusted OR significantly greater than, or less than 1 were classified as low-quality or high-quality outliers, respectively. Pairwise comparison of hospital quality based on TMPM-ICD9 with and without comorbidity information were performed using the intraclass correlation coefficient, the Spearman correlation coefficient, the Bland-Altman Plot, and the kappa statistic.Results: There was nearly perfect agreement between hospital ranking based on TMPM-ICD9 and TMPM-ICD9 with comorbidities. The intraclass correlation coefficient was 0.943 (95% CI, 0.931-0.951), the Spearman correlation coefficient was 0.953 (95% CI, 0.944-0.960), and the kappa statistic was 0.863 (95% CI, 0.792-0.934). The odds of a patient dying in the worst 5% hospitals was 1.73 (95% CI, 1.61-1.86), whereas the odds of a patient dying in the best 5% of the hospitals was 0.37 (95% CI, 0.31-0.44).Conclusion: In this large study of 148,280 trauma patients in 511 hospitals, we found no evidence that adding comorbidites to the risk-adjustment model used to benchmark hospital performance changes hospital ranking. In addition, there appears to be significant variability in mortality outcomes between the best and worst performing hospitals. This difference in outcomes across hospitals may represent a significant opportunity to improve health outcomes for injured patients.