Subgroup mortality probability models: Are they necessary for specialized intensive care units?

Critical Care Medicine

Volumn 37, Issue 8, 2009, Pages 2375-2386

  Nathanson, Brian H ^a   Higgins, Thomas L ^b,c   Kramer, Andrew A ^d   Copes, Wayne S ^d   Stark, Maureen ^d   Teres, Daniel ^c  

^a OptiStatim LLC   (United States)

^b BAYSTATE MEDICAL CENTER   (United States)

^c TUFTS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d CERNER CORPORATION   (United States)

Author keywords

Benchmarking; Case mix; Critical illness; Hospital mortality; Intensive care unit performance assessment; Logistic regression; Medical informatics; Mortality Probability Model (MPM); Quality of care; Scoring system; Severity of illness index; Subgroup models

Indexed keywords

ADULT; AGED; ARTICLE; CALIBRATION; CARDIOVASCULAR SURGERY; ELECTIVE SURGERY; EMERGENCY SURGERY; HUMAN; INTENSIVE CARE UNIT; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MORTALITY; PRIORITY JOURNAL; PROBABILITY; STANDARDIZED MORTALITY RATIO; STATISTICAL MODEL; TRAUMATOLOGY; VALIDATION PROCESS;

EID: 68249089731     PISSN: 00903493     EISSN: 15300293     Source Type: Journal    
DOI: 10.1097/CCM.0b013e3181a12851     Document Type: Article

References (31)

1. Zimmerman JE, Kramer AA, McNair DS, et al: Acute physiology and chronic health evaluation (APACHE) IV: Hospital mortality assessment for today's critically ill patients. Crit Care Med 2006; 34:1297-1310
2. Moreno RP, Metnitz PGH, Almeida E, et al: SAPS 3: From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensive Care Med 2005; 31:1345-1355
3. Higgins TL, Teres D, Copes WS, et al: Assessing contemporary ICU outcome: An updated mortality probability admission model (MPM0-III). Crit Care Med 2007; 35:827-835
4. www.cerner.com Accessed on 2/13/08
5. http://www.jcaho.org/pms/core+measures/ candidate+core+measure+sets.htm Accessed on 2/17/05
6. Lindenauer PK, Remus D, Roman S, et al: Public reporting and pay for performance in hospital quality improvement. N Engl J Med 2007; 356:486-496
7. Rosenstein AH: Hospital report cards: intent, impact, and illusion. Am J Med Qual 2004; 19:183-192
8. Glance LG, Dick AW, Osler TM, et al: Accuracy of hospital report cards based on administrative data. Health Serv Res 2006; 41: 1413-1437
9. Glance LG, Dick A, Osler TM, et al: Impact of changing the statistical methodology on hospital and surgeon ranking: The case of the New York state cardiac surgery report card. Med Care 2006; 44:311-319
10. Kramer AA, Zimmerman JE: Predicting outcomes for cardiac surgery patients after intensive care unit admission. Semin Cardiothorac Vasc Anesth 2008; 12:175-183
11. Bouamra I, Wrotchford A, Hollis S, et al: A new approach to outcome prediction in trauma: A comparison with the TRISS model. J Trauma 2006; 61:701-710
12. Calfee CS, Eisner MD, Ware LB, et al: Trauma-associated lung injury differs clinically and biologically from acute lung injury due to other clinical disorders. Crit Care Med 2007; 35:2243-2250
13. Murphy-Filkins RL, Teres D, Lemeshow S, 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. Crit Care Med 1996; 24:1968-1973
14. Kim EH, Kwon YD, Hwang JH. Comparing the performance of three severity scoring systems for ICU patients: APACHE III, SAPS II, MPM II. J Prev Med Pub Health 2005; 38:276-282
15. Kramer AA: Predictive mortality models are not like fine wine. Crit Care 2005; 9:636-637
16. Higgins TL, Kramer AA, Nathanson BH, et al: Prospective Validation of the Mortality Probability Model, Version III. Abstr 0402. Presented at the 20th ESICM Annual Congress, Berlin, Germany, 2007
17. Project Impact provides answers. Healthcare Benchmarks 1999; 6:91-92
18. Cook SF, Visscher WA, Hobbs CL, et al: Project IMPACT. Results from a pilot validity study of a new observational database. Crit Care Med 2002; 30:2765-2770
19. White H: A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 1980; 48:817-830
20. Hanley JA, McNeil BJ: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143: 29-36
21. Hosmer DW, Lemeshow S: Applied Logistic Regression. Second Edition. New York, John Wiley & Sons, 2000
22. Rapoport J, Teres D, Lemeshow S, et al: A method for assessing the clinical performance and cost-effectiveness of intensive care units: A multicenter inception cohort study. Crit Care Med 1994; 22:1385-1391
23. Woolson RF: Statistical Methods for the Analysis of Biomedical Data. New York, John Wiley & Sons, 1987
24. Natanson C, Shelhamer JH, Parrillo JE: Intubation of the trachea in the critical care setting. JAMA 1985; 253:1160-1165
25. Mathru M, Rao TLK, El-Etr AA, et al: Hemodynamic response to changes in ventilatory patterns in patients with normal and poor left ventricular reserve. Crit Care Med 1982; 10:423-426
26. Hosmer DW, Hosmer T, Le Cessie S, et al: A comparison of goodness-of-fit tests for the logistic regression model. Stat Med 1997; 16:965-980
27. Vergouwe Y, Steyerberg EW, Eijkemans R, et al: Sample size considerations for the performance assessment of predictive models: A simulation study. Control Clin Trials 2003; 24(2 Suppl):S24-S44
28. Den Boer S, De Keizer NF, De Jonge E: Performance of prognostic models in critically ill cancer patients-A review. Crit Care 2005; 9:R458-R463
29. Kramer AA, Zimmerman JE: Assessing the calibration of mortality benchmarks in critical care: The Hosmer-Lemeshow test revisited. Crit Care Med 2007; 35:2052-2056
30. Austin PC, Tu J: Bootstrap methods for developing predictive models. American Statistician 2004; 58:131-137
31. Raftery AE: Bayesian model selection in social research. In: Sociological Methodology. Marsden PV (Ed). Oxford, Basil Blackwell, 1996, pp 111-163