Case-mix-adjusted length of stay and mortality in 23 Finnish ICUs

ObjectivesTo 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.DesignObservational cohort study.SettingTwenty-three ICUs in Finland.PatientsA total of 80,854 consecutive ICU admissions during 2000–2005, of which 63,304 met the inclusion criteria.InterventionsNone.Measurements and resultsLinear 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).ConclusionsWe 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.

[1]  W. Knaus The APACHE III Prognostic System , 1992 .

[2]  Rolf Rossaint,et al.  Epidemiology of sepsis in Germany: results from a national prospective multicenter study , 2007, Intensive Care Medicine.

[3]  D. Cullen,et al.  Therapeutic Intervention Scoring System: Update 1983 , 1983, Critical care medicine.

[4]  W. Knaus,et al.  The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. , 1991, Chest.

[5]  U. Jonasson,et al.  Outcome of the elderly critically ill after intensive care in an era of cost containment , 2004, Acta anaesthesiologica Scandinavica.

[6]  M. Suistomaa,et al.  Customised prediction models based on APACHE II and SAPS II scores in patients with prolonged length of stay in the ICU , 2002, Intensive Care Medicine.

[7]  S. Lemeshow,et al.  Length of Stay Data as a Guide to Hospital Economic Performance for ICU Patients , 2003, Medical care.

[8]  C Weissman,et al.  Analyzing intensive care unit length of stay data: problems and possible solutions. , 1997, Critical care medicine.

[9]  J. Tenhunen,et al.  Data completeness in the Finnish Intensive Care Quality Consortium database , 2007, Critical Care.

[10]  James Deddens,et al.  Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure* , 2005, Critical care medicine.

[11]  J. Takala,et al.  Resource use in the ICU: short‐ vs. long‐term patients * , 2003, Acta anaesthesiologica Scandinavica.

[12]  J. Zimmerman,et al.  Intensive care unit length of stay: Benchmarking based on Acute Physiology and Chronic Health Evaluation (APACHE) IV* , 2006, Critical care medicine.

[13]  S. Lemeshow,et al.  A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. , 1993, JAMA.

[14]  Wayne S Copes,et al.  A revised method to assess intensive care unit clinical performance and resource utilization* , 2007, Critical care medicine.

[15]  M. Suistomaa,et al.  Sampling rate causes bias in APACHE II and SAPS II scores , 2000, Intensive Care Medicine.

[16]  Peter Bauer,et al.  Variability in outcome and resource use in intensive care units , 2007, Intensive Care Medicine.