OBJECTIVE: To quantify the variability in risk-adjusted mortality and length of stay of Veterans Affairs intensive care units using a computer-based severity of illness measure. DESIGN: Retrospective cohort study. SETTING: A stratified random sample of 34 intensive care units in 17 Veterans Affairs hospitals. PARTICIPANTS: A consecutive sample of 29,377 first intensive care unit admissions from February 1996 through July 1997. INTERVENTIONS: Standardized mortality ratio (observed/expected deaths) and observed minus expected length of stay (OMELOS) with 95% confidence intervals were estimated for each unit using a hierarchical logistic (standardized mortality ratio) or linear (OMELOS) regression model with Markov Chain Monte Carlo simulation. We adjusted for patient characteristics including age, admission diagnosis, comorbid disease, physiology at admission (from laboratory data), and transfer status. MEASUREMENTS AND MAIN RESULTS: Mortality across the intensive care units for the 12,088 surgical and 17,289 medical cases averaged 11% (range, 2-30%). Length of stay in the intensive care units averaged 4.0 days (range, mean unit length of stay 3.0-5.9). Standardized mortality ratio of the intensive care units varied from 0.62 to 1.27; the standardized mortality ratio and 95% confidence interval were <1 for four intensive care units and >1.0 for seven intensive care units. OMELOS of the intensive care units ranged from -0.89 to 1.34 days. In a random slope hierarchical model, variation in standardized mortality ratio among intensive care units was similar across the range of severity, whereas variation in length of stay increased with severity. Standardized mortality ratio was not associated with OMELOS (Pearson's r = .13). CONCLUSIONS: We identified intensive care units whose indicators for mortality and length of stay differ substantially using a conservative statistical approach with a severity adjustment model based on data available in computerized clinical databases. Computerized risk adjustment employing routinely available data may facilitate research on the utility of intensive care unit profiling and analysis of natural experiments to understand process and outcome links and quality efforts.
OBJECTIVE: To quantify the variability in risk-adjusted mortality and length of stay of Veterans Affairs intensive care units using a computer-based severity of illness measure. DESIGN: Retrospective cohort study. SETTING: A stratified random sample of 34 intensive care units in 17 Veterans Affairs hospitals. PARTICIPANTS: A consecutive sample of 29,377 first intensive care unit admissions from February 1996 through July 1997. INTERVENTIONS: Standardized mortality ratio (observed/expected deaths) and observed minus expected length of stay (OMELOS) with 95% confidence intervals were estimated for each unit using a hierarchical logistic (standardized mortality ratio) or linear (OMELOS) regression model with Markov Chain Monte Carlo simulation. We adjusted for patient characteristics including age, admission diagnosis, comorbid disease, physiology at admission (from laboratory data), and transfer status. MEASUREMENTS AND MAIN RESULTS: Mortality across the intensive care units for the 12,088 surgical and 17,289 medical cases averaged 11% (range, 2-30%). Length of stay in the intensive care units averaged 4.0 days (range, mean unit length of stay 3.0-5.9). Standardized mortality ratio of the intensive care units varied from 0.62 to 1.27; the standardized mortality ratio and 95% confidence interval were <1 for four intensive care units and >1.0 for seven intensive care units. OMELOS of the intensive care units ranged from -0.89 to 1.34 days. In a random slope hierarchical model, variation in standardized mortality ratio among intensive care units was similar across the range of severity, whereas variation in length of stay increased with severity. Standardized mortality ratio was not associated with OMELOS (Pearson's r = .13). CONCLUSIONS: We identified intensive care units whose indicators for mortality and length of stay differ substantially using a conservative statistical approach with a severity adjustment model based on data available in computerized clinical databases. Computerized risk adjustment employing routinely available data may facilitate research on the utility of intensive care unit profiling and analysis of natural experiments to understand process and outcome links and quality efforts.
Authors: Eduard E Vasilevskis; Michael W Kuzniewicz; Brian A Cason; Rondall K Lane; Mitzi L Dean; Ted Clay; Deborah J Rennie; Eric Vittinghoff; R Adams Dudley Journal: Chest Date: 2009-04-10 Impact factor: 9.410
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Authors: Elizabeth M Viglianti; Sean M Bagshaw; Rinaldo Bellomo; Joanne McPeake; Xiao Qing Wang; Sarah Seelye; Theodore J Iwashyna Journal: Intensive Care Med Date: 2020-06-04 Impact factor: 17.440
Authors: Daniel Baram; Feroza Daroowalla; Ruel Garcia; Guangxiang Zhang; John J Chen; Erin Healy; Syed Ali Riaz; Paul Richman Journal: Clin Med Circ Respirat Pulm Med Date: 2008-04-18