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Scoring Systems and Outcome

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Intensive and Critical Care Medicine

Abstract

The evaluation of severity of illness in the critically ill patient is made through the use of severity scores and prognostic models. Severity scores are instruments that aim at stratifying patients based on the severity of illness, assigning to each patient an increasing score as their severity of illness increases. Prognostic models, apart from their ability to stratify patients according to their severity, predict a certain outcome (usually the vital status at hospital discharge) based on a given set of prognostic variables and a certain modeling equation.

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References

  1. Knaus WA, Zimmerman JE, Wagner DP et al (1981) APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Crit Care Med 9:591–597

    PubMed  CAS  Google Scholar 

  2. Knaus WA, Draper EA, Wagner DP et al (1982) Evaluating outcome from intensive care: A preliminary multihospital comparison. Crit Care Med 10:491–496

    PubMed  CAS  Google Scholar 

  3. Knaus WA, Le Gall JR, Wagner DP et al (1982) A comparison of intensive care in the U.S.A. and France. Lancet 2:642–646

    PubMed  CAS  Google Scholar 

  4. Wagner DP, Draper EA, Abizanda Campos R et al (1984) Initial international use of APACHE: an acute severity of disease measure. Med Decis Making 4:297

    PubMed  CAS  Google Scholar 

  5. Le Gall JR, Loirat P, Alperovitch A et al (1984) A Simplified Acute Physiologic Score for ICU patients. Crit Care Med 12:975–977

    PubMed  Google Scholar 

  6. Knaus WA, Draper EA, Wagner DP et al (1985) APACHE II: a severity of disease classification system. Crit Care Med 13:818–829

    PubMed  CAS  Google Scholar 

  7. Lemeshow S, Teres D, Avrunin J et al (1988) Refining intensive car e unit o utcome by using changing probabilities of mortality. Crit Care Med 16:470–477

    PubMed  CAS  Google Scholar 

  8. Knaus WA, Wagner DP, Draper EA et al (1991) The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest;100:1619–1636

    PubMed  CAS  Google Scholar 

  9. Le Gall JR, Lemeshow S, Saulnier F (1993) A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA 270:2957–2963

    PubMed  Google Scholar 

  10. Lemeshow S, Teres D, Klar J (1993) Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. JAMA 270:2478–2486

    PubMed  CAS  Google Scholar 

  11. Castella X, Artigas A, Bion J (1995) The European / North American Severity Study Group. A comparison of severity of illness scoring systems for intensive care unit patients: results of a multicenter, multinational study. Crit Care Med 23:1327–1335

    PubMed  CAS  Google Scholar 

  12. Bertolini G, D’Amico R, Apolone G et al (1998) Predicting outcome in the intensive care unit using scoring systems: is new better? A comparison of SAPS and SAPS II in a cohort of 1393 patients. Med Care 36:1371–1382

    PubMed  CAS  Google Scholar 

  13. Higgins T, Teres D, Copes W (2005) Preliminary update of the Mortality Prediction Model (MPM0). Crit Care 9:S97 (abs)

    Google Scholar 

  14. Render ML, Kim M, Deddens J et al (2005) Variation in outcomeds in Veterans Affairs intensive care units with a computerized severity measure. Crit Care Med 33:930–939

    PubMed  Google Scholar 

  15. Dybowski R, Weller P, Chang R (1996) Prediction of outcome in critically ill patients using artificial neural network, synthesised by genetic algorithm. Lancet 347:1146–1150

    PubMed  CAS  Google Scholar 

  16. Engoren M, Moreno R, Reis Miranda D (1999) A genetic algorithm to predict hospital mortality in an ICU population. Crit Care Med 27:A52

    Google Scholar 

  17. Ruttimann U, Pollack MM, Fiser DH (1996) Prediction of three outcome states from pediatric intensive care. Crit Care Med 24:78–85

    PubMed  CAS  Google Scholar 

  18. Parsonnet V, Dean D, Bernstein A (1989) A method for uniform stratification of risk for evaluating the results of surgery in acquired adult heart disease. Circulation 79:I3–I12

    PubMed  CAS  Google Scholar 

  19. Smith EJ, Ward AJ, Smith D (1990) Trauma scoring methods. Br J Hosp Med 44:114–118

    PubMed  CAS  Google Scholar 

  20. Champion HR, Copes WS, Sacco WJ et al (1996) Improved predictions from a severity characterization of trauma (ASCOT) over trauma and injury severity score (TRISS): results of an independent evaluation. J Trauma 40:42–49

    PubMed  CAS  Google Scholar 

  21. Boyd C, Tolson M, Copes W (1987) Evaluating trauma care: the TRISS method. J Trauma 27:370–378

    PubMed  CAS  Google Scholar 

  22. Alberti C, Brun-Buisson C, Chevret S et al (2005) Systemic Inflammatory Response and Progression to Severe Sepsis in Critically Ill Infected Patients. Am J Respir Crit Care Med 171:461–468

    PubMed  Google Scholar 

  23. Vincent J-L, Moreno R, Takala J et al (1996) The SOFA (Sepsis-related organ failure assessment) score to describe organ dysfunction/failure. Intensive Care Med 22:707–710

    PubMed  CAS  Google Scholar 

  24. Marshall JD (1997) The multiple organ dysfunction (MOD) score. Sepsis 1:49–52

    Google Scholar 

  25. Le Gall J-R, Klar J, Lemeshow S (1997) How to assess organ dysfunction in the intensive care unit? The logistic organ dysfunction (LOD) system. Sepsis 1:45–47

    Google Scholar 

  26. Timsit JF, Fosse JP, Troche G et al (2001) Accuracy of a composite score using daily SAPS II and LOD scores for predicting hospital mortality in ICU patients hospitalized for more than 72 h. Intensive Care Med 27:1012–1021

    PubMed  CAS  Google Scholar 

  27. Moreno R, Vincent J-L, Matos R et al (1999) The use of maximum SOFA score to quantify organ dysfunction/failure in intensive care. Results of a prospective, multicentre study. Intensive Care Med 25:686–696

    PubMed  CAS  Google Scholar 

  28. Bernard GR (1998) Quantification of organ dysfunction: seeking standardization. Crit Care Med 26:1767–1768

    PubMed  CAS  Google Scholar 

  29. Boyd O, Grounds M (1994) Can standardized mortality ratio be used to compare quality of intensive care unit performance? Crit Care Med 22:1706–1708

    PubMed  CAS  Google Scholar 

  30. Bastos PG, Sun X, Wagner DP (1996) The Brazil APACHE III Study Group. Application of the APACHE III prognostic system in Brazilian intensive care units: a prospective multicenter study. Intensive Care Med 22:564–570

    PubMed  CAS  Google Scholar 

  31. Bastos PG, Knaus WA, Zimmerman JE (1996) The Brazil APACHE III Study Group. The importance of technology for achieving superior outcomes from intensive care. Intensive Care Med 22:664–669

    PubMed  CAS  Google Scholar 

  32. Rivera-Fernandez R, Vazquez-Mata G, Bravo M et al (1998) The Apache III prognostic system: customized mortality predictions for Spanish ICU patients. Intensive Care Med 24:574–581

    PubMed  CAS  Google Scholar 

  33. Cho D-Y, Wang Y-C (1997) Comparison of APACHE III, APACHE II and Glasgow Coma Scale in acute head injury for prediction of mortality and functional outcome. Intensive Care Med 23:77–84

    PubMed  CAS  Google Scholar 

  34. Pappachan JV, Millar B, Bennett ED (1999) Comparison of outcome from intensive care admission after adjustment for case mix by the APACHE III prognostic system. Chest 115:802–810

    PubMed  CAS  Google Scholar 

  35. Beck DH, Smith GB, Pappachan JV et al (2003) External validation of the SAPS II, APACHE II and APACHE III prognostic models in South England: a multicentre study. Intensive Care Med 29:249–256

    PubMed  Google Scholar 

  36. Lemeshow S, Klar J, Teres D et al (1994) Mortality probability models for patients in the intensive care unit for 48 or 72 hours: a prospective, multicenter study. Crit Care Med 22:1351–1358

    PubMed  CAS  Google Scholar 

  37. Abizanda Campos R, Balerdi B, Lopez J et al (1994) Fallos de prediccion de resultados mediante APACHE II. Analisis de los errores de prediction de mortalidad en pacientes criticos. Med Clin Barc 102:527–531

    Google Scholar 

  38. Fery-Lemmonier E, Landais P, Kleinknecht D et al (1995) Evaluation of severity scoring systems in the ICUs: translation, conversion and definitions ambiguities as a source of inter-observer variability in APACHE II, SAPS, and OSF. Intensive Care Med 21:356–360

    Google Scholar 

  39. Rowan K (1996) The reliability of case mix measurements in intensive care. Curr Opin Crit Care 2:209–213

    Google Scholar 

  40. Bosman RJ, Oudemane van Straaten HM, Zandstra DF (1998) The use of intensive care information systems alters outcome prediction. Intensive Care Med 24:953–958

    PubMed  CAS  Google Scholar 

  41. Suistomaa M, Kari A, Ruokonen E et al (2000) Sampling rate causes bias in APACHE II and SAPS II scores. Intensive Care Med 26:1773–1778

    PubMed  CAS  Google Scholar 

  42. Moreno R, Miranda DR, Matos R et al (2001) Mortality after discharge from intensive care: the impact of organ system failure and nursing workload use at discharge. Intensive Care Med 27:999–1004

    PubMed  CAS  Google Scholar 

  43. Flora JD (1978) A method for comparing survival of burn patients to a standard survival curve. J Trauma 18:701–705

    PubMed  Google Scholar 

  44. Hosmer DW, Lemeshow S (1989) Applied logistic regression. New York, John Wiley & Sons, pp

    Google Scholar 

  45. Lemeshow S, Hosmer DW (1982) A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol 115:92–106

    PubMed  CAS  Google Scholar 

  46. Hosmer DW, Lemeshow S (1980) A goodness-of-fit test for the multiple logistic regression model. Comm Stat A 10:1043–1069

    Google Scholar 

  47. Hadorn DC, Keeler EB, Rogers WH (1993) Assessing the performance of mortality prediction models. Santa Monica, CA, RAND/UCLA/Harvard Center for Health Care Financing Policy Research

    Google Scholar 

  48. Bertolini G, D’Amico R, Nardi D (2000) One model, several results: the paradox of the Hosmer-Lemeshow goodness-of-fit test for the logistic regression model. J Epidemiol Biostatistics 5:251–253

    CAS  Google Scholar 

  49. Zhu B-P, Lemeshow S, Hosmer DW (1996) Factors affecting the performance of the models in the mortality probability model and strategies of customization: a simulation study. Crit Care Med 24:57–63

    PubMed  CAS  Google Scholar 

  50. Harrell Jr. FE, Califf RM, Pryor DB (1982) Evaluating the yield of medical tests. JAMA 247:2543–2546

    PubMed  Google Scholar 

  51. Hanley J, McNeil B (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36

    PubMed  CAS  Google Scholar 

  52. Ma G, Hall WJ (1993) Confidence bands for receiver operating characteristic curves. Med Decis Making 13:191–197

    PubMed  CAS  Google Scholar 

  53. Hanley J, McNeil B (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148:839–843

    PubMed  CAS  Google Scholar 

  54. McClish DK (1987) Comparing the areas under more than two independent ROC curves. Med Decis Making 7:149–155

    PubMed  CAS  Google Scholar 

  55. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

    PubMed  CAS  Google Scholar 

  56. Hilden J (1991) The area under the ROC curve and its competitors. Med Decis Making 11:95–101

    PubMed  CAS  Google Scholar 

  57. Schuster DP (1992) Predicting outcome after ICU admission. The art and science of assessing risk. Chest 102:1861–1870

    PubMed  CAS  Google Scholar 

  58. Kollef MH, Schuster DP (1994) Predicting intensive care unit outcome with scoring systems. Underlying concepts and principles. Crit Care Clin 10:1–18

    PubMed  CAS  Google Scholar 

  59. Miller ME, Hui SL (1991) Validation techniques for logistic regression models. Stat Med 10:1213–1226

    PubMed  CAS  Google Scholar 

  60. Rowan KM, Kerr JH, Major E et al (1993) Intensive Care Society’s APACHE II study in Britain and Ireland-II: Outcome comparisons of intensive care units after adjustment for case mix by the American APACHE II method. Br Med J 307:977–981

    CAS  Google Scholar 

  61. Goldhill DR, Withington PS (1996) The effects of casemix adjustment on mortality as predicted by APACHE II. Intensive Care Med 22:415–419

    PubMed  CAS  Google Scholar 

  62. Sicignano A, Carozzi C, Giudici D et al (1996) The influence of length of stay in the ICU on power of discrimination of a multipurpose severity score (SAPS). Intensive Care Med 22:1048–1051

    PubMed  CAS  Google Scholar 

  63. Apolone G, D’Amico R, Bertolini G et al (1996) The performance of SAPS II in a cohort of patients admitted in 99 Italian ICUs: results from the GiViTI. Intensive Care Med 22:1368–1378

    PubMed  CAS  Google Scholar 

  64. Moreno R, Apolone G, Reis Miranda D (1998) Evaluation of the uniformity of fit of general outcome prediction models. Intensive Care Med 24:40–47

    PubMed  CAS  Google Scholar 

  65. Moreno R, Apolone G (1997) The impact of different customization strategies in the performance of a general severity score. Crit Care Med 25:2001–2008

    PubMed  CAS  Google Scholar 

  66. Metnitz PG, Valentin A, Vesely H et al (1999) Prognostic performance and customization of the SAPS II: results of a multicenter Austrian study. Intensive Care Med 25:192–197

    PubMed  CAS  Google Scholar 

  67. Knaus WA, Harrell FE, Fisher CJ et al (1993) The clinical evaluation of new drugs for sepsis. A prospective study design based on survival analysis. JAMA 270:1233–1241

    CAS  Google Scholar 

  68. Le Gall J-R, Lemeshow S, Leleu G et al (1995) Customized probability models for early severe sepsis in adult intensive care patients. JAMA 273:644–650

    PubMed  Google Scholar 

  69. Knaus WA, Harrell FE, LaBrecque JF et al (1996) Use of predicted risk of mortality to evaluate the efficacy of anticytokine therapy in sepsis. Crit Care Med 24:46–56

    PubMed  CAS  Google Scholar 

  70. Castella X, Gilabert J, Torner F et al (1991) Mortality prediction models in intensive care: Acute Physiology and Chronic Health Evaluation II and Mortality Prediction Model compared. Crit Care Med 19:191–197

    PubMed  CAS  Google Scholar 

  71. Sirio CA, Tajimi K, Tase C et al (1992) An initial comparison of intensive care in Japan and United States. Crit Care Med 20:1207–1215

    PubMed  CAS  Google Scholar 

  72. Moreno R, Morais P (1997) Outcome prediction in intensive care: results of a prospective, multicentre, Portuguese study. Intensive Care Med 23:177–186

    PubMed  CAS  Google Scholar 

  73. Moreno R (2003) From the evaluation of the individual patient to the evaluation of the ICU. Réanimation 12:47s–48s

    Google Scholar 

  74. Perkins HS, Jonsen AR, Epstein WV (1986) Providers as predictors: using outcome predictions in intensive care. Crit Care Med 14:105–110

    PubMed  CAS  Google Scholar 

  75. Silverstein MD (1988) Predicting instruments and clinical judgement in critical care. JAMA 260:1758–1759

    PubMed  CAS  Google Scholar 

  76. Dawes RM, Faust D, Mechl PE (1989) Clinical versus actuarial judgement. Sci Med Man 243:1674–1688

    Google Scholar 

  77. Kleinmuntz B (1990) Why we still use our heads instead of formulas: toward an integrative approach. Psychol Bull 107:296–310

    PubMed  CAS  Google Scholar 

  78. McClish DK, Powell SH (1989) How well can physicians estimate mortality in a medical intensive care unit? Med Decis Making 9:125–132

    PubMed  CAS  Google Scholar 

  79. Poses RM, Bekes C, Winkler RL et al (1990) Are two (inexperienced) heads better than one (experienced) head? Averaging house officers prognostic judgement for critically ill patients. Arch Intern Med 150:1874–1878

    PubMed  CAS  Google Scholar 

  80. Poses RM, Bekes C, Copare FJ et al (1989) The answer to ‘what are my chances, doctor?’ depends on whom is asked: prognostic disagreement and inaccuracy for critically ill patients. Crit Care Med 17:827–833

    PubMed  CAS  Google Scholar 

  81. Winkler RL, Poses RM (1993) Evaluating and combining physicians’ probabilities of survival in an intensive care unit. Management science 39:1526–1543

    Google Scholar 

  82. Chang RWS, Lee B, Jacobs S et al (1989) Accuracy of decisions to withdraw therapy in critically ill patients: clinical judgement versus a computer model. Crit Care Med 17:1091–1097

    PubMed  CAS  Google Scholar 

  83. Knaus WA, Rauss A, Alperovitch A et al (1990) Do objective estimates of chances for survival influence decisions to withhold or withdraw treatment? Med Decis Making 10:163–171

    PubMed  CAS  Google Scholar 

  84. Zimmerman JE, Wagner DP, Draper EA et al (1994) Improving intensive care unit discharge decisions: supplementary physician judgment with predictions of next day risk for life support. Crit Care Med 22:1373–1384

    PubMed  CAS  Google Scholar 

  85. Branner AL, Godfrey LJ, Goetter WE (1989) Prediction of outcome from critical illness: a comparison of clinical judgement with a prediction rule. Arch Intern Med 149:1083–1086

    Google Scholar 

  86. Kruse JA, Thill-Baharozin MC, Carlson RW (1988) Comparison of clinical assessment with APACHE II for predicting mortality risk in patients admitted to a medical intensive care unit. JAMA 260:1739–1742

    PubMed  CAS  Google Scholar 

  87. Marks RJ, Simons RS, Blizzard RA et al (1991) Predicting outcome in intensive therapy units-a comparison of APACHE II with subjective assessments. Intensive Care Med 17:159–163

    PubMed  CAS  Google Scholar 

  88. Knaus WA, Wagner DP, Lynn J (1991) Short-term mortality predictions for critically ill hospitalized adults: science and ethics. Sci Med Man 254:389–394

    CAS  Google Scholar 

  89. Lemeshow S, Klar J, Teres D (1995) Outcome prediction for individual intensive care patients: useful, misused, or abused? Intensive Care Med 21:770–776

    PubMed  CAS  Google Scholar 

  90. Suter P, Armagandis A, Beaufils F et al (1994) Predicting outcome in ICU patients: consensus conference organized by the ESICM and the SRLF. Intensive Care Med 20:390–397

    Google Scholar 

  91. Chang RW, Jacobs S, Lee B (1986) Use of APACHE II severity of disease classification to identify intensive-care-unit patients who would not benefit from total parenteral nutrition. Lancet 1483-1486

    Google Scholar 

  92. Atkinson S, Bihari D, Smithies M et al (1994) Identification of futility in intensive care. Lancet 344:1203–1206

    PubMed  CAS  Google Scholar 

  93. Murray LS, Teasdale GM, Murray GD et al (1993) Does prediction of outcome alter patient management? Lancet 341:1487–1491

    PubMed  CAS  Google Scholar 

  94. Gattinoni L, Brazzi L, Pelosi P et al (1995) A trial of goal orientated hemodynamic therapy in critically ill patients. N Engl J Med 333:1025–1032

    PubMed  CAS  Google Scholar 

  95. Henning RJ, McClish D, Daly B et al (1987) Clinical characteristics and resource utilization of ICU patients: implementation for organization of intensive care. Crit Care Med 15:264–269

    PubMed  CAS  Google Scholar 

  96. Wagner DP, Knaus WA, Draper EA (1987) Identification of low-risk monitor admissions to medical-surgical ICUs. Chest 92:423–428

    PubMed  CAS  Google Scholar 

  97. Wagner DP, Knaus WA, Draper EA et al (1983) Identification of low-risk monitor patients within a medical-surgical ICU. Med Care 21:425–433

    PubMed  CAS  Google Scholar 

  98. Zimmerman JE, Wagner DP, Knaus WA et al (1995) The use of risk predictors to identify candidates for intermediate care units. Implications for intensive care unit utilization. Chest;108:490–499

    PubMed  CAS  Google Scholar 

  99. Zimmerman JE, Wagner DP, Sun X et al (1996) Planning patient services for intermediate care units: insights based on care for intensive care unit low-risk monitor admissions. Crit Care Med 24:1626–1632

    PubMed  CAS  Google Scholar 

  100. Strauss MJ, LoGerfo JP, Yeltatzie JA et al (1986) Rationing of intensive care unit services. An everyday occurrence. JAMA 255:1143–1146

    PubMed  CAS  Google Scholar 

  101. Civetta JM, Hudson-Civetta JA, Nelson LD (1990) Evaluation of APACHE II for cost containment and quality assurance. Ann Surg 212:266–276

    PubMed  CAS  Google Scholar 

  102. Jones AE, Fitch MT, Kline JA (2005) Operational performance of validated physiologic scoring systems for predicting in-hospital mortality among critically ill emergency department patients. Crit Care Med 33:974–978

    PubMed  Google Scholar 

  103. Hillman K, Chen J, Cretikos M et al; MERIT study investigators (2005) Introduction of the medical emergency team (MET) system: a cluster-randomised controlled trial. Lancet 365:2091–2097

    PubMed  Google Scholar 

  104. Moreno R, Reis Miranda D (1998) Nursing staff in intensive care in Europe. The mismatch between planning and practice. Chest 113:752–758

    PubMed  CAS  Google Scholar 

  105. Clermont G, Kaplan V, Moreno R et al (2004) Dynamic microsimulation to model multiple outcomes in cohorts of critically ill patients. Intensive Care Med 30:2237–2244

    PubMed  Google Scholar 

  106. Knaus WA, Wagner DP, Zimmerman JE et al (1993) Variations in mortality and length of stay in Intensive Care Units. Ann Intern Med 118:753–761

    PubMed  CAS  Google Scholar 

  107. Zimmerman JE, Shortell SM, Knaus WA et al (1993) Value and cost of teaching hospitals: a prospective, multicenter, inception cohort study. Crit Care Med 21:1432–1442

    PubMed  CAS  Google Scholar 

  108. Rapoport J, Teres D, Lemeshow S et al (1994) A method for assessing the clinical performance and cost-effectiveness of intensive care units: a multicenter inception cohort study. Crit Care Med 22:1385–1391

    PubMed  CAS  Google Scholar 

  109. Teres D, Rapoport J (1991) Identifying patients with high risk of high cost. Chest 99:530–531

    PubMed  CAS  Google Scholar 

  110. Cerra FB, Negro F, Abrams J (1990) APACHE II score does not predict multiple organ failure or mortality in post-operative surgical patients. Arch Surg 125:519–522

    PubMed  CAS  Google Scholar 

  111. Rapoport J, Teres D, Lemeshow S et al (1990) Explaining variability of cost using a severity of illness measure for ICU patients. Med Care 28:338–348

    PubMed  CAS  Google Scholar 

  112. Oye RK, Bellamy PF (1991) Patterns of resource consumption in medical intensive care. Chest 99:695–689

    Google Scholar 

  113. Knaus WA, Draper EA, Wagner DP et al (1986) An evaluation of outcome from intensive care in major medical centers. Ann Intern Med 104:410–418

    PubMed  CAS  Google Scholar 

  114. Hosmer DW, Lemeshow S (1995) Confidence interval estimates of an index of quality performance based on logistic regression estimates. Stat Med 14:2161–2172

    PubMed  CAS  Google Scholar 

  115. Rapoport J, Teres D, Barnett R et al (1995) A comparison of intensive care unit utilization in Alberta and western Massachusetts. Crit Care Med 23:1336–1346

    PubMed  CAS  Google Scholar 

  116. Wong DT, Crofts SL, Gomez M et al (1995) Evaluation of predictive ability of APACHE II system and hospital outcome in Canadian intensive care unit patients. Crit Care Med 23:1177–1183

    PubMed  CAS  Google Scholar 

  117. Moreno R, Reis Miranda D, Fidler V et al (1998) Evaluation of two outcome predictors on an independent database. Crit Care Med 26:50–61

    PubMed  CAS  Google Scholar 

  118. Le Gall JR, Loirat P, Nicolas F et al (1983) Utilisation d’un indice de gravité dans huit services de réanimation multidisciplinaire. Presse Med 12:1757–1761

    PubMed  Google Scholar 

  119. Zimmerman JE, Rousseau DM, Duffy J et al (1994) Intensive care at two teaching hospitals: an organizational case study. Am J Crit Care 3:129–138

    PubMed  CAS  Google Scholar 

  120. Chisakuta AM, Alexander JP (1990) Audit in Intensive Care. The APACHE II classification of severity of disease. Ulster Med J 59:161–167

    PubMed  CAS  Google Scholar 

  121. Marsh HM, Krishan I, Naessens JM et al (1990) Assessment of prediction of mortality by using the APACHE II scoring system in intensive care units. Mayo Clin Proc 65:1549–1557

    PubMed  CAS  Google Scholar 

  122. Turner JS, Mudaliar YM, Chang RW et al (1991) Acute physiology and chronic health evaluation (APACHE II) scoring in a cardiothoracic intensive care unit. Crit Care Med 19:1266–1269

    PubMed  CAS  Google Scholar 

  123. Oh TE, Hutchinson R, Short S et al (1993) Verification of the acute physiology and chronic health evaluation scoring system in a Hong Kong intensive care unit. Crit Care Med 21:698–705

    PubMed  CAS  Google Scholar 

  124. Zimmerman JE, Shortell SM, Rousseau DM et al (1993) Improving intensive care: observations based on organizational case studies in nine intensive care units: a prospective, multicenter study. Crit Care Med 21:1443–1451

    PubMed  CAS  Google Scholar 

  125. Shortell SM, Zimmerman JE, Rousseau DM et al (1994) The performance of intensive care units: does good management make a difference? Med Care 32:508–25

    PubMed  CAS  Google Scholar 

  126. Reis Miranda D, Ryan DW, Schaufeli WB, Fidler V (eds) (1997) Organization and management of Intensive Care: a prospective study in 12 European countries. Berlin, Springer

    Google Scholar 

  127. Moreno R, Matos R (2000) The ‘new’ scores: what problems have been fixed, and what remain. Curr Opin Crit Care 6:158–165

    Google Scholar 

  128. Moreno R, Matos R (2001) New issues in severity scoring: interfacing the ICU and evaluating it. Curr Opin Crit Care 7:469–474

    PubMed  CAS  Google Scholar 

  129. Teres D, Lemeshow S (1993) Using severity measures to describe high performance intensive care units. Crit Care Clin 9:543–954

    PubMed  CAS  Google Scholar 

  130. Teres D, Lemeshow S (1994) Why severity models should be used with caution. Crit Care Clin 10:93–110

    PubMed  CAS  Google Scholar 

  131. Teres D, Lieberman S (1991) Are we ready to regionalize pediatric intensive care? Crit Care Med 19:139–140

    PubMed  CAS  Google Scholar 

  132. Pollack MM, Alexander SR, Clarke N et al (1990) Improved outcomes from tertiary center pediatric intensive care: a statewide comparison of tertiary and nontertiary care facilities. Crit Care Med 19:150–159

    Google Scholar 

  133. Goldstein H, Spiegelhalter DJ (1996) League tables and their limitations: statistical issues in comparisons of institutional performance. J R Stat Soc A 159:385–443

    Google Scholar 

  134. Lemeshow S, Teres D, Pastides H et al (1985) A method for predicting survival and mortality of ICU patients using objectively derived weights. Crit Care Med 13:519–525

    PubMed  CAS  Google Scholar 

  135. Lemeshow S, Teres D, Avrunin JS et al (1987) A comparison of methods to predict mortality of intensive care unit patients. Crit Care Med 15:715–722

    PubMed  CAS  Google Scholar 

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Authors and Affiliations

  1. Unidade de Cuidados Intensivos Polivalente, Hospital de Santo António dos Capuchos, Centro Hospitalar de Lisboa, Lisbon, Portugal

    R. Moreno

  2. Department of Anaesthesiology and Intensive Care Medicine, Medical University of Vienna, Vienna, Austria

    P. Metnitz

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  1. R. Moreno
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Editors and Affiliations

  1. Department of Perioperative Medicine Intensive Care and Emergency, Trieste University School of Medicine, Trieste, Italy

    Antonino Gullo

  2. Department of Anaesthesiology, Keck School of Medicine at USC, Los Angeles, CA, USA

    Philip D. Lumb

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Moreno, R., Metnitz, P. (2005). Scoring Systems and Outcome. In: Gullo, A., Lumb, P.D. (eds) Intensive and Critical Care Medicine. Springer, Milano. https://doi.org/10.1007/88-470-0350-4_11

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  • DOI: https://doi.org/10.1007/88-470-0350-4_11

  • Publisher Name: Springer, Milano

  • Print ISBN: 978-88-470-0349-1

  • Online ISBN: 978-88-470-0350-7

  • eBook Packages: MedicineMedicine (R0)

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