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Variations in Mortality and Length of Stay in Intensive Care Units

William A. Knaus, MD; Douglas P. Wagner, PhD; Jack E. Zimmerman, MD; and Elizabeth A. Draper, MS
[+] Article and Author Information

From George Washington University Medical Center and APACHE Medical Systems, Inc., Washington, DC. Requests for Reprints: William A. Knaus, MD, ICU Research Unit, 2300 K Street, NW, George Washington University Medical Center, Washington, DC 20037. Disclosure: Drs. Knaus, Zimmerman, and Wagner are founders of and shareholders in APACHE Medical Systems, Inc. (AMS) and are prohibited by University policy from receiving any payment, royalties, or other fees from AMS. Elizabeth Draper is an employee of and shareholder in AMS. APACHE Medical Systems produces a management information system for critical care units and holds the commercial copyright on the equations for in-hospital mortality and length of ICU stay. APACHE and APACHE III are trademarks of AMS. Although both equations and the APACHE III database are protected by commercial copyright, they are available to researchers for independent verification and further analysis by contacting the authors or AMS. Acknowledgments: The authors thank participating hospitals for their data collection efforts and leadership; Theodore Lotring for research assistance; and Vea Thomas for clerical support. Grant Support: In part by the Agency for Health Care Policy and Research (grant HS 5787), the John A. Hartford Foundation (grant 87267), the Department of Anesthesiology, George Washington University Medical Center, and APACHE Medical Systems, Inc.


Copyright 2004 by the American College of Physicians


Ann Intern Med. 1993;118(10):753-761. doi:10.7326/0003-4819-118-10-199305150-00001
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Objective: To evaluate the amount of variation in in-hospital mortality and length of intensive care unit (ICU) stay that can be accounted for by clinical data available at ICU admission.

Design: Inception cohort study.

Setting: Forty-two ICUs in 40 hospitals, including 26 hospitals that were randomly selected and 14 large tertiary care hospitals that volunteered for the study.

Participants: A consecutive sample of 16 622 patients and 17 440 ICU admissions.

Measurements and Main Outcomes: Data on selected demographic characteristics, comorbidity, and specific physiologic variables were recorded during the first ICU day for an average of 415 admissions at each ICU; hospital discharge status (dead or alive) and length of ICU stay were recorded for individual patients; and the ratio of actual to predicted in-hospital mortality, standardized mortality ratios, and the ratio of actual to predicted length of ICU stay were recorded for individual ICUs.

Results: Unadjusted in-hospital mortality rates for the 42 units varied from 6.4% to 40%, and 90% (R2 = 0.90) of this variation was attributable to patient characteristics at admission. The standard mortality ratio varied from 0.67 to 1.25. The mean unadjusted length of ICU stay varied from 3.3 to 7.3 days, and 78% of the variation (R2 = 0.78) was attributed to patient and selected institutional characteristics. The best performing unit had a length of stay ratio of 0.88, whereas the poorest performing unit had a ratio of 1.21.

Conclusions: Clinicians can use readily available admission data to adjust for considerable variations in patient severity and type in different ICUs. Such data should permit precise evaluation and comparison of ICU effectiveness and efficiency, which varied substantially in this study, and result in improved methods of risk prediction and evaluation of new medical practices.

Figures

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Figure 1.
Top.Bottom.

Relative contribution of each factor to in-hospital mortality. The Other category includes length of stay before admission to the intensive care unit (ICU), 1.6%; mean duration of hospital stay for survivors, 1.4%; location before ICU admission, 0.1%; and emergency surgery, 0.01%. Relative contribution of each factor to length of ICU stay. The Other category includes location before ICU admission, 6.7%; region, 3.2%; ICU readmission, 1.1%; bed size of the hospital, 0.8%; emergency surgery, 0.7%; and teaching status, 0.2%. The relative contributions were calculated as the percentage of chi-square uniquely associated with each variable. Asterisks indicate percentages as represented in the APACHE III score. The Disease category included 78 mutually exclusive indications for ICU admission.

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Figure 2.
Top.Bottom.

Distribution of patients and the association between first-day APACHE III score and in-hospital mortality rate. The mortality analysis included 16 662 intensive care unit (ICU) patients. Distribution of patients and the association between first-day APACHE III score and length of ICU stay. The length of stay analysis included 17 105 ICU admissions.

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Figure 3.
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Relative performance of 42 intensive care units according to the actual and predicted death rate at hospital discharge. A linear regression of observed death rate on mean predicted death rate across the 42 units yielded an intercept of 0.006 and a regression coefficient of 1.036 (SE, 0.055). This indicates that the equation is well calibrated across all levels of risk. Relative performance of 42 intensive care units according to the actual and predicted length of ICU stay. A linear regression of observed mean length of ICU stay on mean predicted length of stay across these 42 units yielded an intercept of 0.062 and a regression coefficient of 0.989 (SE, 0.083), indicating that the equation is well calibrated across all levels of risk. Units with statistically significant ( < 0.05) variations are denoted by the box or star symbol.

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