OBJECTIVE: To update the Mortality Probability Model at intensive care unit (ICU) admission (MPM0-II) using contemporary data. DESIGN: Retrospective analysis of data from 124,855 patients admitted to 135 ICUs at 98 hospitals participating in Project IMPACT between 2001 and 2004. Independent variables considered were 15 MPM0-II variables, time before ICU admission, and code status. Univariate analysis and multivariate logistic regression were used to identify risk factors associated with hospital mortality. SETTING: One hundred thirty-five ICUs at 98 hospitals. PATIENTS: Patients in the Project IMPACT database eligible for MPM0-II scoring. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Hospital mortality rate in the current data set was 13.8% vs. 20.8% in the MPM0-II cohort. All MPM0-II variables remained associated with mortality. Clinical conditions with high relative risks in MPM0-II also had high relative risks in MPM0-III. Gastrointestinal bleeding is now associated with lower mortality risk. Two factors have been added to MPM0-III: "full code" resuscitation status at ICU admission, and "zero factor" (absence of all MPM0-II risk factors except age). Seven two-way interactions between MPM0-II variables and age were included and reflect the declining marginal contribution of acute and chronic medical conditions to mortality risk with increasing age. Lead time before ICU admission and pre-ICU location influenced individual outcomes but did not improve model discrimination or calibration. MPM0-III calibrates well by graphic comparison of actual vs. expected mortality, overall standardized mortality ratio (1.018; 95% confidence interval, 0.996-1.040) and a low Hosmer-Lemeshow goodness-of-fit statistic (11.62; p = .31). The area under the receiver operating characteristic curve was 0.823. CONCLUSIONS: MPM0-II risk factors remain relevant in predicting ICU outcome, but the 1993 model significantly overpredicts mortality in contemporary practice. With the advantage of a much larger sample size and the addition of new variables and interaction effects, MPM0-III provides more accurate comparisons of actual vs. expected ICU outcomes.
OBJECTIVE: To update the Mortality Probability Model at intensive care unit (ICU) admission (MPM0-II) using contemporary data. DESIGN: Retrospective analysis of data from 124,855 patients admitted to 135 ICUs at 98 hospitals participating in Project IMPACT between 2001 and 2004. Independent variables considered were 15 MPM0-II variables, time before ICU admission, and code status. Univariate analysis and multivariate logistic regression were used to identify risk factors associated with hospital mortality. SETTING: One hundred thirty-five ICUs at 98 hospitals. PATIENTS: Patients in the Project IMPACT database eligible for MPM0-II scoring. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Hospital mortality rate in the current data set was 13.8% vs. 20.8% in the MPM0-II cohort. All MPM0-II variables remained associated with mortality. Clinical conditions with high relative risks in MPM0-II also had high relative risks in MPM0-III. Gastrointestinal bleeding is now associated with lower mortality risk. Two factors have been added to MPM0-III: "full code" resuscitation status at ICU admission, and "zero factor" (absence of all MPM0-II risk factors except age). Seven two-way interactions between MPM0-II variables and age were included and reflect the declining marginal contribution of acute and chronic medical conditions to mortality risk with increasing age. Lead time before ICU admission and pre-ICU location influenced individual outcomes but did not improve model discrimination or calibration. MPM0-III calibrates well by graphic comparison of actual vs. expected mortality, overall standardized mortality ratio (1.018; 95% confidence interval, 0.996-1.040) and a low Hosmer-Lemeshow goodness-of-fit statistic (11.62; p = .31). The area under the receiver operating characteristic curve was 0.823. CONCLUSIONS: MPM0-II risk factors remain relevant in predicting ICU outcome, but the 1993 model significantly overpredicts mortality in contemporary practice. With the advantage of a much larger sample size and the addition of new variables and interaction effects, MPM0-III provides more accurate comparisons of actual vs. expected ICU outcomes.
Authors: Anthony D Slonim; Sachin Khandelwal; Jianping He; Matthew Hall; David C Stockwell; Wendy M Turenne; Samir S Shah Journal: Pediatrics Date: 2010-05-10 Impact factor: 7.124
Authors: Nicole B Gabler; Sarah J Ratcliffe; Jason Wagner; David A Asch; Gordon D Rubenfeld; Derek C Angus; Scott D Halpern Journal: Am J Respir Crit Care Med Date: 2013-10-01 Impact factor: 21.405
Authors: Sara E Erickson; Eduard E Vasilevskis; Michael W Kuzniewicz; Brian A Cason; Rondall K Lane; Mitzi L Dean; Deborah J Rennie; R Adams Dudley Journal: Crit Care Med Date: 2011-03 Impact factor: 7.598
Authors: Eduard E Vasilevskis; Michael W Kuzniewicz; Brian A Cason; Rondall K Lane; Mitzi L Dean; Ted Clay; Deborah J Rennie; Eric Vittinghoff; R Adams Dudley Journal: Chest Date: 2009-04-10 Impact factor: 9.410
Authors: Louis Mayaud; Peggy S Lai; Gari D Clifford; Lionel Tarassenko; Leo Anthony Celi; Djillali Annane Journal: Crit Care Med Date: 2013-04 Impact factor: 7.598