OBJECTIVES: To create a tool for benchmarking intensive care units (ICUs) with respect to case-mix adjusted length of stay (LOS) and to study the association between clinical and economic measures of ICU performance. DESIGN: Observational cohort study. SETTING: Twenty-three ICUs in Finland. PATIENTS: A total of 80,854 consecutive ICU admissions during 2000-2005, of which 63,304 met the inclusion criteria. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Linear regression was used to create a model that predicted ICU LOS. Simplified Acute Physiology Score (SAPS) II, age, disease categories according to Acute Physiology and Chronic Health Evaluation III, single highest Therapeutic Intervention Scoring System score collected during the ICU stay and presence of other ICUs in the hospital were included in the model. Probabilities of hospital death were calculated using SAPS II, age, and disease categories as covariates. In the validation sample, the created model accounted for 28% of variation in ICU LOS across individual admissions and 64% across ICUs. The expected ICU LOS was 2.53 +/- 2.24 days and the observed ICU LOS was 3.29 +/- 5.37 days, P < 0.001. There was no association between the mean observed - mean expected ICU LOS and standardized mortality ratios of the ICUs (Spearman correlation 0.091, P = 0.680). CONCLUSIONS: We developed a tool for the assessment of resource use in a large nationwide ICU database. It seems that there is no association between clinical and economic quality indicators.
OBJECTIVES: To create a tool for benchmarking intensive care units (ICUs) with respect to case-mix adjusted length of stay (LOS) and to study the association between clinical and economic measures of ICU performance. DESIGN: Observational cohort study. SETTING: Twenty-three ICUs in Finland. PATIENTS: A total of 80,854 consecutive ICU admissions during 2000-2005, of which 63,304 met the inclusion criteria. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Linear regression was used to create a model that predicted ICU LOS. Simplified Acute Physiology Score (SAPS) II, age, disease categories according to Acute Physiology and Chronic Health Evaluation III, single highest Therapeutic Intervention Scoring System score collected during the ICU stay and presence of other ICUs in the hospital were included in the model. Probabilities of hospital death were calculated using SAPS II, age, and disease categories as covariates. In the validation sample, the created model accounted for 28% of variation in ICU LOS across individual admissions and 64% across ICUs. The expected ICU LOS was 2.53 +/- 2.24 days and the observed ICU LOS was 3.29 +/- 5.37 days, P < 0.001. There was no association between the mean observed - mean expected ICU LOS and standardized mortality ratios of the ICUs (Spearman correlation 0.091, P = 0.680). CONCLUSIONS: We developed a tool for the assessment of resource use in a large nationwide ICU database. It seems that there is no association between clinical and economic quality indicators.
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