Veterans Affairs intensive care unit risk adjustment model: ... : Critical Care Medicine (original) (raw)

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Veterans Affairs intensive care unit risk adjustment model: Validation, updating, recalibration*

Render, Marta L. MD; Deddens, James PhD; Freyberg, Ron MS; Almenoff, Peter MD; Connors, Alfred F. Jr MD; Wagner, Douglas PhD; Hofer, Timothy P. MD, MSc

From the Veterans Affairs Medical Center, Cincinnati, OH (MLR); Veterans Affairs Ann Arbor Health Services Research and Development Center of Excellence, Ann Arbor, MI (TPH); Division of Pulmonary/Critical Care (MLR), Section of Health Outcomes and Institute of Health Policy and Health Services Research (RF), and Department of Mathematical Sciences (JD), University of Cincinnati, Cincinnati, OH; Kansas City Veterans Affairs Medical Center, Kansas City, MO (PA); University of Kansas School of Medicine, Kansas City, KS (PA); MetroHealth Medical Center, Case Western Reserve University, Cleveland, OH (AFC); Department of Health Evaluation Sciences, University of Virginia School of Medicine, Charlottesville, VA (DW); and the Department of Medicine, University of Michigan, Ann Arbor, MI (TPH).

The authors have not disclosed any potential conflicts of interest.

Supported, in part, by grant IIR 02-051-1 from the Health Services Research and Development, Veterans Health Administration, U.S. Department of Veterans Affairs.

The opinions are the authors’ and do not reflect those of the U.S. Department of Veterans Affairs, the Veterans Health Administration, or Health Services Research and Development.

For information regarding this article, E-mail: [email protected]

Abstract

Background:

A valid metric is critical to measure and report intensive care unit (ICU) outcomes and drive innovation in a national system.

Objectives:

To update and validate the Veterans Affairs (VA) ICU severity measure (VA ICU).

Research Design:

A validated logistic regression model was applied to two VA hospital data sets: 36,240 consecutive ICU admissions to a stratified random sample of moderate and large hospitals in 1999–2000 (cohort 1) and 81,964 cases from 42 VA Medical Centers in fiscal years 2002–2004 (cohort 2). The model was updated by adding diagnostic groups and expanding the source of admission variables.

Measures:

C statistic, Hosmer-Lemeshow goodness-of-fit statistic, and Brier's score measured predictive validity. Coefficients from the 1997 model were applied to predictors (fixed) in a logistic regression model. A 10 × 10 table compared cases with both VA ICU and National Surgical Quality Improvement Performance metrics. The standardized mortality ratios divided observed deaths by the sum of predicted mortality.

Results:

The fixed model in both cohorts had predictive validity (cohort 1: C statistic = 0.874, Hosmer-Lemeshow goodness-of-fit C statistic chi-square = 72.5; cohort 2: 0.876, 307), as did the updated model (cohort 2: C statistic = 0.887, Hosmer-Lemeshow goodness-of-fit C statistic chi-square = 39). In 7,411 cases with predictions in both systems, the standardized mortality ratio was similar (1.04 for VA ICU, 1.15 for National Surgical Quality Improvement Performance), and 92% of cases matched (±1 decile) when ordered by deciles of mortality. The VA ICU standardized mortality ratio correlates with the National Surgical Quality Improvement Performance standardized mortality ratio (_r_2 = .74). Variation in discharge and laboratory practices may affect performance measurement.

Conclusion:

The VA ICU severity model has face, construct, and predictive validity.