title: An in-hospital mortality equation for mechanically ventilated patients in intensive care units.
abstract: OBJECTIVE
  To develop an equation model of in-hospital mortality for mechanically ventilated patients in adult intensive care using administrative data for the purpose of retrospective performance comparison among intensive care units (ICUs).
  
  
  METHODS
  Two models were developed using the split-half method, in which one test dataset and two validation datasets were used to develop and validate the prediction model, respectively. Nine candidate variables (demographics: age; gender; clinical factors hospital admission course; primary diagnosis; reason for ICU entry; Charlson score; number of organ failures; procedures and therapies administered at any time during ICU admission: renal replacement therapy; pressors/vasoconstrictors) were used for developing the equation model.
  
  
  METHODS
  In acute-care teaching hospitals in Japan: 282 ICUs in 2008, 310 ICUs in 2009, and 364 ICUs in 2010.
  
  
  METHODS
  Mechanically ventilated adult patients discharged from an ICU from July 1 to December 31 in 2008, 2009, and 2010.
  
  
  METHODS
  The test dataset consisted of 5,807 patients in 2008, and the validation datasets consisted of 10,610 patients in 2009 and 7,576 patients in 2010. Two models were developed: Model 1 (using independent variables of demographics and clinical factors), Model 2 (using procedures and therapies administered at any time during ICU admission in addition to the variables in Model 1). Using the test dataset, 8 variables (except for gender) were included in multiple logistic regression analysis with in-hospital mortality as the dependent variable, and the mortality prediction equation was constructed. Coefficients from the equation were then tested in the validation model.
  
  
  RESULTS
  Hosmer-Lemeshow χ(2) are values for the test dataset in Model 1 and Model 2, and were 11.9 (P = 0.15) and 15.6 (P = 0.05), respectively; C-statistics for the test dataset in Model 1and Model 2 were 0.70 and 0.78, respectively. In-hospital mortality prediction for the validation datasets showed low and moderate accuracy in Model 1 and Model 2, respectively.
  
  
  CONCLUSIONS
  Model 2 may potentially serve as an alternative model for predicting mortality in mechanically ventilated patients, who have so far required physiological data for the accurate prediction of outcomes. Model 2 may facilitate the comparative evaluation of in-hospital mortality in multicenter analyses based on administrative data for mechanically ventilated patients.
authors.affiliation: Department of Anesthesiology, Kansai Medical University, Osaka, Japan. take-u-ane12429@snow.odn.ne.jp
authors.forename: Takeshi
authors.initials: T
authors.lastname: Umegaki
authors.forename: Masaji
authors.initials: M
authors.lastname: Nishimura
authors.forename: Kimitaka
authors.initials: K
authors.lastname: Tajimi
authors.forename: Kiyohide
authors.initials: K
authors.lastname: Fushimi
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Ikai
authors.forename: Yuichi
authors.initials: Y
authors.lastname: Imanaka
mesh_terms: D000368:Aged; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D007564:Japan; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D012121:Respiration, Artificial / Q000401:mortality* / Q000706:statistics & numerical data; D012189:Retrospective Studies