Mortality and length-of-stay outcomes, 1993-2003, in the binational Australian and New Zealand intensive care adult patient database

Critical Care Medicine

Volumn 36, Issue 1, 2008, Pages 46-61

  Moran, John L ^a   Bristow, Peter ^b   Solomon, Patricia J ^c   George, Carol ^d   Hart, Graeme K ^d,e  

^a QUEEN ELIZABETH HOSPITAL   (Australia)

^b TOOWOOMBA HOSPITAL   (Australia)

^c UNIVERSITY OF ADELAIDE   (Australia)

^d Australian and New Zealand Intensive Care Society   (Australia)

^e AUSTIN HOSPITAL   (Australia)

Author keywords

Database; Generalized linear model; Hospital mortality; Intensive care; Length of stay; Longitudinal study; Random effects

Indexed keywords

ADULT; APACHE; ARTICLE; ARTIFICIAL VENTILATION; CONTROLLED STUDY; DATA BASE; DEMOGRAPHY; FEMALE; HOSPITAL ADMISSION; HUMAN; INTENSIVE CARE; LENGTH OF STAY; LINEAR REGRESSION ANALYSIS; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; MEDICAL RECORD; MEDICAL SOCIETY; MORTALITY; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC;

EID: 37549001731     PISSN: 00903493     EISSN: None     Source Type: Journal    
DOI: 10.1097/01.CCM.0000295313.08084.58     Document Type: Article

References (83)

1. Afessa B, Keegan MT, Hubmayr RD, et al: Evaluating the performance of an institution using an intensive care unit benchmark. Mayo Clin Proc 2005; 80:174-180
2. Knaus WA, Wagner DP, Zimmerman JE, et al: Variations in mortality and length of stay in intensive care units. Ann Intern Med 1993; 118:753-761
3. Moran JL, Solomon PJ: Mortality and other event rates: What do they tell us about performance? Crit Care Resusc 2003; 5:292-303
4. Thomas JW, Guire KE, Horvat GG: Is patient length of stay related to quality of care? Hosp Health Serv Adm 1997; 42:489-507
5. The Australian and New Zealand Intensive Care Society. http://www.anzics.com.au/. Accessed November 1, 2005
6. Project IMPACT CCM I. Project IMPACT CCM, Inc. http://www.cerner.com/ piccm/about.html. Accessed November 1, 2006
7. Intensive Care National Audit & Research Centre. http://www.icnarc. org.htm. Accessed November 1, 2005
8. Clough JD, Engler D, Canuto PE: Cleveland Health Quality Choice was a failure, not a martyr. Qual Saf Health Care 2002; 11:391
9. The Leapfrog Group. http://www.leapfrog-group.org/. Accessed November 1, 2005
10. Stow PJ, Hart GK, Higlett T, et al: Development and implementation of a high-quality clinical database: The Australian and New Zealand Intensive Care Society Adult Patient Database. J Crit Care 2006; 21:133-141
11. Bastos PG, Knaus WA, Zimmerman JE, et al: The importance of technology for achieving superior outcomes from intensive care. Brazil APACHE III Study Group. Intensive Care Med 1996; 22:664-669
12. Gordon HS, Aron DC, Fuehrer SM, et al: Using severity-adjusted mortality to compare performance in a Veterans Affairs hospital and in private-sector hospitals. Am J Med Qual 2000; 15:207-211
13. Manheim LM, Feinglass J, Shortell SM, et al: Regional variation in Medicare hospital mortality. Inquiry 1992; 29:55-66
14. Sirio CA, Shepardson LB, Rotondi AJ, et al: Community-wide assessment of intensive care outcomes using a physiologically based prognostic measure: Implications for critical care delivery from Cleveland Health Quality Choice. Chest 1999; 115:793-801
15. Knaus WA, Draper EA, Wagner DP, et al: APACHE II: A severity of disease classification system. Crit Care Med 1985; 13:818-829
16. Knaus WA, Wagner DP, Draper EA, et al: The APACHE III prognostic system: Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 1991; 100:1619-1636
17. Le Gall JR, Lemeshow S, Saulnier F: A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA 1993; 270:2957-2963
18. Angus DC, Linde-Zwirble WT, Sirio CA, et al: The effect of managed care on ICU length of stay: Implications for Medicare. JAMA 1996; 276:1075-1082
19. Eagle KA, Mulley AG, Skates SJ, et al: Length of stay in the intensive care unit: Effects of practice guidelines and feedback. JAMA 1990; 264:992-997
20. Rapoport J, Teres D, Zhao Y, et al: Length of stay data as a guide to hospital economic performance for ICU patients. Med Care 2003; 41:386-397
21. Rosenberg AL, Zimmerman JE, Alzola C, et al: Intensive care unit length of stay: recent changes and future challenges. Crit Care Med 2000; 28:3465-3473
22. Marik PE, Hedman L: What's in a day? Determining intensive care unit length of stay. Crit Care Med 2000; 28:2090-2093
23. Woods AW, MacKirdy FN, Livingston BM, et al: Evaluation of predicted and actual length of stay in 22 Scottish intensive care units using the APACHE III system. Acute Physiology and Chronic Health Evaluation. Anaesthesia 2000; 55:1058-1065
24. Render ML, Kim HM, Deddens J, et al: Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure. Crit Care Med 2005; 33:930-939
25. Sirio CAM, Tajimi KM, Taenaka NM, et al: A cross-cultural comparison of critical care delivery: Japan and the United States. Chest 2002; 121:539-548
26. Silber JH, Rosenbaum PR, Ross R: Comparing the contributions of groups of predictors: Which outcomes vary with hospital rather than patient characteristics. J Am Stat Assoc 1995; 90:7-18
27. DiGiuseppe DL, Aron DC, Payne SM, et al: Risk adjusting cesarean delivery rates: A comparison of hospital profiles based on medical record and birth certificate data. Health Serv Res 2001; 36:959-977
28. Justice AC, Covinsky KE, Berlin JA: Assessing the generalizability of prognostic information. Ann Intern Med 1999; 130:515-524
29. ANZICS Adult Data Base. Data Dictionary Version 1.5. http://www.anzics. com.au/uploads/ANZICS_APD_Data_Dictionary_Version2.1_sept07.pdf. Accessed September 1, 2007
30. Lee AH, Xiao J, Vemuri SR, et al: A discordancy test approach to identify outliers of length of hospital stay. Stat Med 1998; 17:2199-2206
31. Gutierrez R, Drukker DM: Citing references for Stata's cluster-correlated robust variance estimates. http://www.stata.com/support/faqs/stat/robust_ref. html. Accessed November 2006
32. Belsley DA: Conditioning Diagnostics, Collinearity and Weak Data in Regression. New York, Wiley, 1991
33. Hilbe J. sqv5: unilogit. Univariate log-likelihood tests for model identification. Stata Technical Bulletin Reprints 1993; 2:172-174
34. Cox DR, Solomon PJ: Components of Variance. Boca Raton, FL, Chapman & Hall/CRC Press, 2003
35. Rabe-Hesketh S, Pickles A, Taylor C: sg129: Generalized linear latent and mixed models. Stata Technical Bulletin Reprints 2000;9:293-306
36. Duan N: Smearing estimate: A nonparametric retransformation method. J Am Stat Assoc 1983; 78:605-610
37. 2. Am Stat 1991; 45:127-129
38. Thomas JW, Ashcraft ML: Measuring severity of illness: Six severity systems and their ability to explain cost variations. Inquiry 1991; 28:39-55
39. Harrell FE Jr: Regression Modelling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York, Springer-Verlag, 2001
40. Milberg JA, Davis DR, Steinberg KP, et al: Improved survival of patients with acute respiratory distress syndrome (ARDS): 1983-1993. JAMA 1995; 273:306-309
41. Goldstein R. srd8: logdummy. Stata Technical Bulletin Reprints 1992; 1:183-184
42. Greenland S: Basic problems in interaction assessment. Environ Health Perspect 1993; 101(Suppl 4):59-66
43. Chelluri L, Grenvik A, Silverman M: Intensive care for critically ill elderly: Mortality, costs, and quality of life. Review of the literature. Arch Intern Med 1995; 155:1013-1022
44. Chernow B: Variables affecting outcome in critically ill patients. Chest 1999; 115:71S-76S
45. Reinikainen M, Niskanen M, Uusaro A, et al: Impact of gender on treatment and outcome of ICU patients. Acta Anaesthesiol Scand 2005; 49:984-990
46. Kollef MH, O'Brien JD, Silver P: The impact of gender on outcome from mechanical ventilation. Chest 1997; 111:434-441
47. Epstein SK, Vuong V: Lack of influence of gender on outcomes of mechanically ventilated medical ICU patients. Chest 1999; 116:732-739
48. Esteban A, Anzueto A, Frutos F, et al: Characteristics and outcomes in adult patients receiving mechanical ventilation: A 28-day international study. JAMA 2002; 287:345-355
49. Needham DM, Bronskill SE, Sibbald WJ, et al: Mechanical ventilation in Ontario, 1992-2000: Incidence, survival, and hospital bed utilization of noncardiac surgery adult patients. Crit Care Med 2004; 32:1504-1509
50. Wagner DP, Knaus WA, Draper EA: Physiologic abnormalities and outcome from acute disease: Evidence for a predictable relationship. Arch Intern Med 1986; 146:1389-1396
51. Lemeshow S, Teres D, Klar J, et al: Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. JAMA 1993; 270:2478-2486
52. Authors for the ARDS Network: Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. The Acute Respiratory Distress Syndrome Network. N Engl J Med 2000; 342:1301-1318
53. Halm EA, Lee C, Chassin MR: Is volume related to outcome in health care? A systematic review and methodologic critique of the literature. Ann Intern Med 2002; 137:511-520
54. Farley DE, Ozminkowski RJ: Volume-outcome relationships and in-hospital mortality: The effect of changes in volume over time. Med Care 1992; 30:77-94
55. Tarnow-Mordi WO, Hau C, Warden A, et al: Hospital mortality in relation to staff workload: A 4-year study in an adult intensive-care unit. Lancet 2000; 356:185-189
56. Rosenberg AL, Hofer TP, Strachan C, et al: Accepting critically ill transfer patients: Adverse effect on a referral center's outcome and benchmark measures. Ann Intern Med 2003; 138:882-890
57. Jarman B, Gault S, Alves B, et al: Explaining differences in English hospital death rates using routinely collected data. BMJ 1999; 318:1515-1520
58. Aiken LHP, Clarke SPP, Sloane DMP, et al: Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA 2002; 288:1987-1993
59. Pronovost PJ, Angus DC, Dorman T, et al: Physician staffing patterns and clinical outcomes in critically ill patients: A systematic review. JAMA 2002; 288:2151-2162
60. Hutchings A, Raine R, Brady A, et al: Socioeconomic status and outcome from intensive care in England and Wales. Med Care 2004; 42:943-951
61. Diehr P, Yanez D, Ash A, et al: Methods for analyzing health care utilization and costs. Annu Rev Public Health 1999; 20:125-144
62. Joint Faculty of Intensive Care Medicine. Joint Faculty of Intensive Care Medicine: History. http://www.jficm.anzca.edu.au/about/history/index.htm. Accessed May 16, 2006
63. Luft HS, Romano PS: Chance, continuity, and change in hospital mortality rates: Coronary artery bypass graft patients in California hospitals, 1983 to 1989. JAMA 1993; 270:331-337
64. Azoulay E, Alberti C, Bornstain C, et al: Improved survival in cancer patients requiring mechanical ventilatory support: Impact of noninvasive mechanical ventilatory support. Crit Care Med 2001; 29:519-525
65. Bernard GR, Vincent JL, Laterre PF, et al: Efficacy and safety of recombinant human activated protein C for severe sepsis. N Engl J Med 2001; 344:699-709
66. Minneci PC, Deans KJ, Banks SM, et al: Corticosteroids for septic shock. Ann Intern Med 2004; 141:742-743
67. van den Berghe G, Wouters P, Weekers F, et al: Intensive insulin therapy in the critically ill patients. N Engl J Med 2001; 345:1359-1367
68. Jencks SF, Huff ED, Cuerdon T: Change in the quality of care delivered to Medicare beneficiaries, 1998-1999 to 2000-2001. JAMA 2003; 289:305-312
69. Friedman G, Silva E, Vincent JL: Has the mortality of septic shock changed with time. Crit Care Med 1998; 26:2078-2086
70. Webb SA: Sepsis outcomes have improved, but why? Crit Care Resusc 2007; 9:5-7
71. Campbell DT: Regression artifacts in time-series and longitudinal data. Eval Program Plann 1996; 19:377-389
72. Stukel TA, Fisher ES, Wennberg DE, et al: Analysis of observational studies in the presence of treatment selection bias: Effects of invasive cardiac management on ami survival using propensity score and instrumental variable methods. JAMA 2007; 297:278-285
73. Christenfeld NJ, Sloan RP, Carroll D, et al: Risk factors, confounding, and the illusion of statistical control. Psychosom Med 2004; 66:868-875
74. Schafer JL: Marginal modeling of intensive longitudinal data by generalized estimating equations. In: Models for Intensive Longitudinal Data. Walls TA, Schafer JL (Eds). Oxford University Press, New York, 2006, pp 38-62
75. Kuha J: AIC and BIC: Comparisons of assumptions and performance. Sociol Methods Res 2005; 33:188-229
76. Hanley JA, McNeil BJ: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143:29-36
77. Weesie J: sg87: Windmeijer's goodness-of-fit test for logistic regression. Stata Technical Bulletin Reprints 1999; 8:153-160
78. Hosmer DW, Lemeshow S: Applied Logistic Regression. Second Edition. New York: Wiley, 2000
79. Rowan KM, Kerr JH, Major E, et al: Intensive Care Society's APACHE II study in Britain and Ireland - I: Variations in case mix of adult admissions to general intensive care units and impact on outcome. BMJ 1993; 307:972-977
80. Halvorsen R, Palmquist R: The interpretation of dummy variables in semilogarithmic equations. Am Econ Rev 1980; 70:474-475
81. Nieto FJ, Coresh J: Adjusting survival curves for confounders: A review and a new method. Am J Epidemiol 1996; 143:1059-1068
82. Ai C, Norton EC: Interaction terms in logit and probit models. Econ Lett 2003; 80:123-129
83. Rabe-Hesketh S, Everitt B: Summary measure analysis of longitudinal data. In: A Handbook of Statistical Analyses using Stata. Rabe-Hesketh S, Everitt B (Eds). Boca Raton, FL, Chapman & Hall/CRC Press, 2007, pp. 157-172