Brief Report

Length of Stay Predictions

Improvements Through the Use of Automated Laboratory and Comorbidity Variables

Liu, Vincent MD, MSc*; Kipnis, Patricia PhD†‡; Gould, Michael K. MD, MSc§; Escobar, Gabriel J. MD†¶

Author Information

From the *Division of Pulmonary and Critical Care Medicine, Stanford University, Stanford, CA; †Systems Research Initiative and Perinatal Research Unit, Kaiser Permanente Division of Research, Oakland, CA; ‡Kaiser Foundation Health Plan Management, Information and Analysis, Oakland, CA; §Division of General Internal Medicine, University of Southern California, Los Angeles, CA; and ¶Department of Inpatient Pediatrics, Kaiser Permanente Medical Center, Walnut Creek, CA.

Supported by NIH T32-HL007948, The Permanente Medical Group, Inc, and Kaiser Foundation Hospitals, Inc.

Reprints: Vincent Liu, MD, MSc, Division of Pulmonary Critical Care Medicine, Stanford University, 300 Pasteur Drive, H-3143, Stanford, CA 94305. E-mail: [email protected].

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.lww-medicalcare.com).

Medical Care 48(8):p 739-744, August 2010. | DOI: 10.1097/MLR.0b013e3181e359f3

Abstract

Background: 

Length of stay (LOS) is a common measure of hospital resource utilization. Most methods for risk-adjusting LOS are limited by the use of only administrative data. Recent studies suggest that adding automated clinical data to these models improves performance.

Objectives: 

To evaluate the utility of adding “point of admission” automated laboratory and comorbidity measures—the Laboratory Acute Physiology Score (LAPS) and Comorbidity Point Score (COPS)—to risk adjustment models that are based on administrative data.

Methods: 

We performed a retrospective analysis of 155,474 hospitalizations between 2002 and 2005 at 17 Northern California Kaiser Permanente hospitals. We evaluated the benefit of adding LAPS and COPS in linear regression models using full, trimmed, truncated, and log-transformed LOS, as well as in logistic and generalized linear models.

Results: 

Mean age was 61 ± 19 years; females represented 55.2% of subjects. The mean LOS was 4.5 ± 7.7 days; median LOS was 2.8 days (interquartile range, 1.3–5.1 days). Adding LAPS and COPS to the linear regression model improved R2 by 29% from 0.113 to 0.146. Similar improvements with the inclusion of LAPS and COPS were observed in other regression models. Together, these variables were responsible for >50% of the predictive ability of logistic regression models that identified outliers with longer LOS.

Conclusions: 

The inclusion of automated laboratory and comorbidity data improved LOS predictions in all models, underscoring the need for more widespread adoption of comprehensive electronic medical records.

© 2010 Lippincott Williams & Wilkins, Inc.

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