Article

The Effect of Preexisting Conditions on Hospital Quality Measurement for Injured Patients

Department of Anesthesiology, University of Rochester School of Medicine, Rochester, NY 14642, USA.
Annals of surgery (Impact Factor: 7.19). 03/2010; 251(4):728-34. DOI: 10.1097/SLA.0b013e3181d56770
Source: PubMed

ABSTRACT To determine whether adjusting for comorbidities significantly affects hospital quality measurement compared with adjusting for injury severity alone.
Pre-existing conditions have a significant impact on mortality after injury. The impact of including comorbidities on hospital quality measurement is not well understood.
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.
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).
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.

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