Using hierarchical modeling to measure ICU quality

Intensive Care Med. 2003 Dec;29(12):2223-2229. doi: 10.1007/s00134-003-1959-9. Epub 2003 Oct 8.


Objective: To determine whether hierarchical modeling agrees with conventional logistic regression modeling on the identity of ICU quality outliers within a large multi-institutional database.

Design: Retrospective database analysis.

Setting and patients: Subset of the Project IMPACT database consisting of 40435 adult patients admitted to surgical, medical, and mixed surgical-medical ICUs ( n=55) between 1997 and 1999 who met inclusion criteria for SAPS II.

Measurements and results: The SAPS II score was customized to this database using conventional logistic regression and using a hierarchical (random coefficients) model. Both models exhibited excellent discrimination ( Cstatistic) and calibration (Hosmer-Lemeshow statistic). The hierarchical and nonhierarchical models had C statistics of.870 and.865, and HL statistics of 3.71 ( p>.88, df=8) and 8.94 ( p>.35, df=8), respectively. Since the random effects component of the hierarchical model accounts for between-hospital variability, only the fixed-effects coefficients were used to calculate the expected mortality rate based on the hierarchical model. The ratio and 95% confidence intervals of the observed to expected mortality rate were calculated using both models for each ICU. ICUs whose observed/expected ratio was either less than 1 or greater than 1, and whose 95% confidence interval did not include 1 were labeled as either high-performance or low-performance outliers, respectively. Analysis using kappa statistic revealed almost perfect agreement between the two models (nonhierarchical vs. hierarchical) on the identity of ICU quality outliers.

Conclusions: Models obtained by customizing SAPS II using a nonhierarchical and a hierarchical approach exhibit excellent agreement on the identity of ICU quality outliers.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Benchmarking / methods*
  • Critical Care / standards*
  • Female
  • Humans
  • Intensive Care Units / statistics & numerical data*
  • Logistic Models*
  • Male
  • Middle Aged
  • Quality of Health Care*