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). DESIGN: 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. SETTING: In acute-care teaching hospitals in Japan: 282 ICUs in 2008, 310 ICUs in 2009, and 364 ICUs in 2010. PARTICIPANTS: Mechanically ventilated adult patients discharged from an ICU from July 1 to December 31 in 2008, 2009, and 2010. MAIN OUTCOME MEASURES: 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.
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). DESIGN: 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. SETTING: In acute-care teaching hospitals in Japan: 282 ICUs in 2008, 310 ICUs in 2009, and 364 ICUs in 2010. PARTICIPANTS: Mechanically ventilated adult patients discharged from an ICU from July 1 to December 31 in 2008, 2009, and 2010. MAIN OUTCOME MEASURES: 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: Raúl Lombardi; Nicolás Nin; José A Lorente; Fernando Frutos-Vivar; Niall D Ferguson; Javier Hurtado; Carlos Apezteguia; Pablo Desmery; Konstantinos Raymondos; Vinko Tomicic; Nahit Cakar; Marco González; José Elizalde; Peter Nightingale; Fekri Abroug; Manuel Jibaja; Yaseen Arabi; Rui Moreno; Dimitros Matamis; Antonio Anzueto; Andrés Esteban Journal: Clin J Am Soc Nephrol Date: 2011-06-23 Impact factor: 8.237