title: To develop a regional ICU mortality prediction model during the first 24 h of ICU admission utilizing MODS and NEMS with six other independent variables from the Critical Care Information System (CCIS) Ontario, Canada.
abstract: BACKGROUND
  Intensive care unit (ICU) scoring systems or prediction models evolved to meet the desire of clinical and administrative leaders to assess the quality of care provided by their ICUs. The Critical Care Information System (CCIS) is province-wide data information for all Ontario, Canada level 3 and level 2 ICUs collected for this purpose. With the dataset, we developed a multivariable logistic regression ICU mortality prediction model during the first 24 h of ICU admission utilizing the explanatory variables including the two validated scores, Multiple Organs Dysfunctional Score (MODS) and Nine Equivalents Nursing Manpower Use Score (NEMS) followed by the variables age, sex, readmission to the ICU during the same hospital stay, admission diagnosis, source of admission, and the modified Charlson Co-morbidity Index (CCI) collected through the hospital health records.
  
  
  METHODS
  This study is a single-center retrospective cohort review of 8822 records from the Critical Care Trauma Centre (CCTC) and Medical-Surgical Intensive Care Unit (MSICU) of London Health Sciences Centre (LHSC), Ontario, Canada between 1 Jan 2009 to 30 Nov 2012. Multivariable logistic regression on training dataset (n = 4321) was used to develop the model and validate by bootstrapping method on the testing dataset (n = 4501). Discrimination, calibration, and overall model performance were also assessed.
  
  
  RESULTS
  The predictors significantly associated with ICU mortality included: age (p < 0.001), source of admission (p < 0.0001), ICU admitting diagnosis (p < 0.0001), MODS (p < 0.0001), and NEMS (p < 0.0001). The variables sex and modified CCI were not significantly associated with ICU mortality. The training dataset for the developed model has good discriminating ability between patients with high risk and those with low risk of mortality (c-statistic 0.787). The Hosmer and Lemeshow goodness-of-fit test has a strong correlation between the observed and expected ICU mortality (χ (2) = 5.48; p > 0.31). The overall optimism of the estimation between the training and testing data set ΔAUC = 0.003, indicating a stable prediction model.
  
  
  CONCLUSIONS
  This study demonstrates that CCIS data available after the first 24 h of ICU admission at LHSC can be used to create a robust mortality prediction model with acceptable fit statistic and internal validity for valid benchmarking and monitoring ICU performance.
authors.affiliation: Department of National Defense, Royal Canadian Medical Services, 1745 Alta Vista Drive, Ottawa, K1A 0K6 Ontario Canada ; London Health Sciences Center, Divisions of Critical Care and Robarts Research Institute, Western University, 800 Commissioner's Rd E., London, Ontario N6A 5W9 Canada ; Harvard School of Public Health, Harvard University, 677 Huntington Ave., Boston, 02115 MA USA.
authors.forename: Raymond
authors.initials: R
authors.lastname: Kao
authors.affiliation: London Health Sciences Center, Divisions of Critical Care and Robarts Research Institute, Western University, 800 Commissioner's Rd E., London, Ontario N6A 5W9 Canada.
authors.forename: Fran
authors.initials: F
authors.lastname: Priestap
authors.affiliation: London Health Sciences Center, Divisions of Critical Care and Robarts Research Institute, Western University, 800 Commissioner's Rd E., London, Ontario N6A 5W9 Canada.
authors.forename: Allan
authors.initials: A
authors.lastname: Donner
keywords: Hosmer and Lemeshow goodness-of-fit test; Intensive care unit (ICU); Logistic regression; MODS; Mortality; NEMS