Assessing contemporary intensive care unit outcome: An updated Mortality Probability Admission Model (MPM0-III)*

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.

[1]  H. White.  A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity , 1980 .

[2]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[3]  S Lemeshow,et al.  Refining intensive care unit outcome prediction by using changing probabilities of mortality. , 1988, Critical care medicine.

[4]  E. Fiaccadori,et al.  Cell metabolism in patients undergoing major valvular heart surgery: Relationship with intra and postoperative hemodynamics, oxygen transport, and oxygen utilization patterns , 1989, Critical care medicine.

[5]  Development of APACHE III , 1989 .

[6]  Chapter 5. Development of APACHE III , 1989 .

[7]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

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

[9]  S. Lemeshow,et al.  Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. , 1993, JAMA.

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

[11]  D. Neuberg,et al.  Relationship of patient age to pathologic features of the tumor and prognosis for patients with stage I or II breast cancer. , 1994, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[12]  S Lemeshow,et al.  A method for assessing the clinical performance and cost‐effectiveness of intensive care units: A multicenter inception cohort study , 1994, Critical care medicine.

[13]  A. Raftery.  Bayesian Model Selection in Social Research , 1995 .

[14]  J. Bion,et al.  A comparison of severity of illness scoring systems for intensive care unit patients: results of a multicenter, multinational study. The European/North American Severity Study Group. , 1995, Critical care medicine.

[15]  S Lemeshow,et al.  Effect of changing patient mix on the performance of an intensive care unit severity-of-illness model: how to distinguish a general from a specialty intensive care unit. , 1996, Critical care medicine.

[16]  J. Svenson,et al.  Critical care of medical and surgical patients in the ED: length of stay and initiation of intensive care procedures. , 1997, The American journal of emergency medicine.

[17]  G. Smith,et al.  The effect of lead time bias on severity of illness scoring, mortality prediction and standardised mortality ratio in intensive care — a pilot study , 1998, Anaesthesia.

[18]  Reliability of the general severity scoring systems, APACHE II and SAPS II , 1998 .

[19]  T. Osler,et al.  Identifying quality outliers in a large, multiple-institution database by using customized versions of the Simplified Acute Physiology Score II and the Mortality Probability Model II0* , 2002, Critical care medicine.

[20]  T. Osler,et al.  Rating the quality of intensive care units: Is it a function of the intensive care unit scoring system?* , 2002, Critical care medicine.

[21]  S. Cook,et al.  Project IMPACT: Results from a Pilot Validity Study of a New Observational Database , 2002, Critical care medicine.

[22]  P. Kotler,et al.  Analytic Reviews: Considerations in the Development of Intensive Care Unit Report Cards , 2002 .

[23]  Charles L Sprung,et al.  End-of-life practices in European intensive care units: the Ethicus Study. , 2003, JAMA.

[24]  J. Zimmerman,et al.  The use of benchmarking to identify top performing critical care units: a preliminary assessment of their policies and practices. , 2003, Journal of critical care.

[25]  R. Poupon,et al.  Improved survival after variceal bleeding in patients with cirrhosis over the past two decades , 2004, Hepatology.

[26]  S. Tenner,et al.  Early Intensive Resuscitation of Patients With Upper Gastrointestinal Bleeding Decreases Mortality , 2001, American Journal of Gastroenterology.

[27]  J. Zimmerman,et al.  Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients* , 2006, Critical care medicine.

[28]  James R Carpenter,et al.  Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom* , 2006, Critical care medicine.

[29]  Jenny Wong,et al.  Synthesis of the oxysterol, 24(S), 25-epoxycholesterol, parallels cholesterol production and may protect against cellular accumulation of newly-synthesized cholesterol , 2007, Lipids in Health and Disease.