Modeling intensive care unit outcome in a large data base: analysis of the institutional effect

Within the intensive care environment, large data-bases exist, recording patient, ICU, and hospital details. For the last 20 years, a number of competing algorithms have been developed to generate risk-adjusted outcomes for patients; the most well known is the APACHE II (acute physiology and chronic health care evaluation) algorithm. Standardized mortality rates (SMR) for individual ICUs have subsequently been generated (the "league-tables" paradigm). The method of calculation of SMR using say, the APACHE II algorithm, whereby "mortality ratios are calculated by projecting the APACHE II score-specific mortalities of the total group on case mix ...of individual ICUs" amounts to an indirect standardization, which (quoting Yule and Rothman), "is not fully a method of standardization at all". It has been recommended (Fidler 1997) to use direct standardization by either: a. logistic regression ... with separate intercepts for each ICU. The intercepts are simply the logits of directly standardized mortality rates and can be used for rankings. This approach assumes constant slopes for all ICUs... and can be tested, or b. model the differences between ICUs as random effects (DeLong et al 1997) The above matters will be addressed using data from the ANZICS (Australia and New Zealand Intensive Care Society) national data base, 1993-2003, recording APACHE II data and hospital outcomes for 280,000 patients in 201 ICUs. Implications for the use of the Stata will be illustrated.