Scoring Systems

  • R. P. Moreno
  • A. C. Diogo
  • S. Afonso


Intensivists, the physicians that practice the art and science of intensive care medicine, have a challenging task. Our field of action presents unique characteristics that make it distinct from most fields of medicine: we deal with a quite heterogeneous population, with our patients presenting a wide range of ages, comorbid diseases, reasons for seeking medical care, and specific needs for care. Moreover, the time window for our interventions is measured often in minutes rather than in days or months. For this reason, we traditionally practice our specialty inside special places in the hospital, the so-called Intensive Care Units (ICUs) where all the technical and human expertise are assembled together in order to optimize the science and art of preventing, detecting, and managing patients at risk or with already-established critical illness in order to achieve the best possible outcomes of care. This task is a complex process, carried out on a very heterogeneous patient population, and influenced by several variables that include religious and cultural background, different structures and organizations of the health care systems, and major differences in the baseline characteristics of the populations.


Intensive Care Unit Intensive Care Medicine Simplified Acute Physiology Score Drotrecogin Alfa Outcome Prediction Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Apgar V (1953) A proposal for a new method of evaluation of the newborn infant. Anesth Analg 32:260–267CrossRefGoogle Scholar
  2. 2.
    Killip 3rd. T, Kimball JT (1967) Treatment of myocardial infarction in a coronary care unit. Am J Cardiol 20:457–464CrossRefPubMedGoogle Scholar
  3. 3.
    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 100:1619–1636CrossRefPubMedGoogle Scholar
  4. 4.
    Le Gall J-R, Loirat P, Alperovitch A (1983) Simplified acute physiological score for intensive care patients. Lancet 2:741CrossRefPubMedGoogle Scholar
  5. 5.
    Atkinson S, Bihari D, Smithies M et al (1994) Identification of futility in intensive care. Lancet 344:1203–1206CrossRefPubMedGoogle Scholar
  6. 6.
    Fleegler BM, Jackson DK, Turnbull J et al (2002) Identifying potentially ineffective care in a community hospital. Crit Care Med 30:1803–1807CrossRefPubMedGoogle Scholar
  7. 7.
    Bernard GR, Vincent J-L, Laterre P-F et al for the Recombinant Human Activated Protein C Worldwide Evaluation in Severe Sepsis (PROWESS) Study Group (2001) Efficacy and safety of recombinant human activated protein C for severe sepsis. N Engl J Med 344:699–709CrossRefPubMedGoogle Scholar
  8. 8.
    Shortell SM, Zimmerman JE, Rousseau DM et al (1994) The performance of intensive care units: does good management make a difference? Med Care 32:508–525CrossRefPubMedGoogle Scholar
  9. 9.
    Azoulay E, Metnitz B, Sprung C et al (2009) End-of-life practices in 282 intensive care units: data from the SAPS 3 database. Intensive Care Med 35:623–630CrossRefPubMedGoogle Scholar
  10. 10.
    Capuzzo M, Moreno RP, Jordan B et al (2006) Predictors of early recovery of health status after intensive care. Intensive Care Med 32:1832–1838CrossRefPubMedGoogle Scholar
  11. 11.
    Rothen HU, Stricker K, Einfalt J et al (2007) Variability in outcome and resource use in intensive care units. Intensive Care Med 33:1329–1336CrossRefPubMedGoogle Scholar
  12. 12.
    Rothen HU, Takala J (2008) Can outcome prediction data change patrent outcomes and organizational outcomes? Curr Opin Crit Care 14:513–519CrossRefPubMedGoogle Scholar
  13. 13.
    Moreno RP, Metnitz PG, Almeida E et al SAPS 3 Investigators (2005) 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 31:1345–1355CrossRefPubMedGoogle Scholar
  14. 14.
    Vestrup JA (1992) Critical care audit. Can J Anaesth 39:210–213CrossRefPubMedGoogle Scholar
  15. 15.
    Chisakuta AM, Alexander JP (1990) Audit in Intensive Care. The APACHE II classification of severity of disease. Ulster Med J 59:161–167PubMedGoogle Scholar
  16. 16.
    Boyd O, Grounds RM (1993) Physiological scoring systems and audit. Lancet 341:1573–1574CrossRefPubMedGoogle Scholar
  17. 17.
    Zimmerman JE, Alzola C, Von Rueden JT (2003) The use of benchmarking to identify top performing critical care units: a preliminary assessment of their policies and practices. J Crit Care 18:76–86CrossRefPubMedGoogle Scholar
  18. 18.
    Afessa B, Keegan MT, Hubmayer RD et al (2005) Evaluating the performance of an institution using an intensive care unit benchmark. Mayo Clin Proc 80:174–180CrossRefPubMedGoogle Scholar
  19. 19.
    Zimmerman JE, Kramer AA, McNair DS et al (2006) Intensive care unit length of stay: benchmarking based on Acute Physiology and Chronic Health Evaluation (APACHE) IV. Crit Care Med 34:2517–2529CrossRefPubMedGoogle Scholar
  20. 20.
    Moreno R, Matos R (2001) New issues in severity scoring: interfacing the ICU and evaluating it. Curr Opin Crit Care 7:469–474CrossRefPubMedGoogle Scholar
  21. 21.
    Moreno R, Matos R (2001) Outcome prediction in intensive care. Solving the paradox. Intensive Care Med 27:962–964CrossRefPubMedGoogle Scholar
  22. 22.
    Talmor M, Hydo LJ, Barie PS (1999) Relationship of systemic inflammatory respponse syndrome to organ dysfunction, length of stay, and mortality in critical surgical illness: effect of intensive care unit resuscitation. Arch Surg 134:81–87CrossRefPubMedGoogle Scholar
  23. 23.
    Holzheimer RG, Capel P, Cavaillon JM et al (2000) Immunological surrogate parameters in a prognostic model for multi-organ failure and death. Eur J Med Res 5:283–294PubMedGoogle Scholar
  24. 24.
    Slotman GJ (2000) Prospectively validated predictions of shock and organ failure in individual septic surgical patients: the Systemic Mediator Associated Response Test. Crit Care 4:319–326CrossRefPubMedGoogle Scholar
  25. 25.
    Slotman GJ (2000) Prospectively validated prediction of organ failure and hypotension in patients with septic shock: the systemic mediator associated response test. Shock 14:101–106CrossRefPubMedGoogle Scholar
  26. 26.
    Katja B, Hartmut K, Pawel M et al (2001) The value of immune modulating parameters in predicting the progression from peritonitis to septic shock. Shock 15:95–100CrossRefPubMedGoogle Scholar
  27. 27.
    Saenz JJ, Izura JJ, Manrique A et al (2001) Early prognosis in severe sepsis via analyzing the monocyte immunophenotype. Intensive Care Med 27:970–977CrossRefPubMedGoogle Scholar
  28. 28.
    Russell JA, Singer J, Bernard GR et al (2000) Changing pattern of organ dysfunction in early human sepsis is related to mortality. Crit Care Med 28:3405–3411CrossRefPubMedGoogle Scholar
  29. 29.
    Rué M, Quintana S, Álvarez M, Artigas A (2001) Daily assessment of severity of illness and mortality prediction for individual patients. Crit Care Med 29:45–50CrossRefPubMedGoogle Scholar
  30. 30.
    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 27:1012–1021CrossRefPubMedGoogle Scholar
  31. 31.
    Hutchinson C, CCraig S, Ridley S (2000) Sequential organ scoring as a measure of effectiveness of critical care. Anaesthesia 55:1149–1154CrossRefPubMedGoogle Scholar
  32. 32.
    Seely AJ, Christou NV (2000) Multiple organ dysfunction syndrome: exploring the paradigm of complex nonlinear systems. Crit Care Med 28:2193–2200CrossRefPubMedGoogle Scholar
  33. 33.
    Baxt WG (1994) Complexity, chaos and human physiology: the justification for non-linear neural computational analysis. Cancer Lett 77:85–93CrossRefPubMedGoogle Scholar
  34. 34.
    Dybowski R, Weller P, Chang R, Gant V (1996) Prediction of outcome in critically ill patients using artificial neural network, synthesised by genetic algorithm. Lancet 347:1146–1150CrossRefPubMedGoogle Scholar
  35. 35.
    Wong LS, Young JD (1999) A comparison of ICU mortality prediction using the APACHE II scoring system and artificial neural networks. Anaesthesia 54:1048–1054CrossRefPubMedGoogle Scholar
  36. 36.
    Metnitz P, Lang T, Vesely H et al (2000) Ratios of observed to expected mortality are affected by differences in case mix and quality of care. Intensive Care Med 26:1466–1472CrossRefPubMedGoogle Scholar
  37. 37.
    Fang XM, Schroder S, Hoeft A, Stuber F (1999) Comparison of two polymorphisms of the interleukin-1 gene family: interleukin-1 receptor antagonist polymorphism contributes to susceptibility to severe sepsis. Crit Care Med 27:1330–1334CrossRefPubMedGoogle Scholar
  38. 38.
    Schroder J, Kahlke V, Book M, Stuber F (2000) Gender differences in sepsis: genetically determined? Shock 14:307–310CrossRefPubMedGoogle Scholar
  39. 39.
    Waterer GW, Quasney MW, Cantor RM, Wunderink RG (2001) Septic shock and respiratory failure in community-acquired pneumonia have different TNF polymorphism associations. Am J Respir Crit Care Med 163:1599–1604PubMedGoogle Scholar
  40. 40.
    Blunt MC, Burchett KR (2000) Out-of-hours consultant cover and case-mix-adjusted mortality in intensive care. Lancet 356:735–736CrossRefPubMedGoogle Scholar
  41. 41.
    Dimick JB, Pronovost PJ, Heitmiller RF, Lipsett PA (2001) Intensive care unit physician staffing is associated with decreased length of stay, hospital cost, and complications after esophageal resection. Crit Care Med 29:753–758CrossRefPubMedGoogle Scholar
  42. 42.
    Pronovost PJ, Jenckes MW, Dorman T et al (1999) Organizational characteristics of intensive care units related to outcomes of abdominal aortic surgery. JAMA 281:1310–1317CrossRefPubMedGoogle Scholar
  43. 43.
    Rosenfeld BA, Dorman T, Breslow MJ, Pronovost P et al (2000) Intensive care unit telemedicine: alternate paradigm for providing continuous intensivist care. Crit Care Med 28:3925–3931CrossRefPubMedGoogle Scholar
  44. 44.
    Fisher M (1995) Intensive care: do intensivists matter? Intensive Care World 12:71–72Google Scholar
  45. 45.
    Amaravadi RK, Dimick JB, Pronovost PJ, Lipsett PA (2000) ICU nurse-to-patient ratio is associated with complications and resource use after esophagectomy. Intensive Care Med 26:1857–1862CrossRefPubMedGoogle Scholar
  46. 46.
    Ridley S, Jones S, Shahani A, Brampton W et al (1998) Classification trees. A possible method for iso-resource grouping in intensive care. Anaesthesia 53:833–840CrossRefPubMedGoogle Scholar
  47. 47.
    Burgess JF Jr, Christiansen CL, Michalak SE, Morris CN (2000) Medical profiling: improving standards and risk adjustments using hierarchical models. J Health Econ 19:291–309CrossRefPubMedGoogle Scholar
  48. 48.
    Sahu SK, Dey DK (2000) A comparison of frailty and other models for bivariate survival data. Lifetime Data Anal 207–228Google Scholar
  49. 49.
    Moreno R, Jordan B, Metnitz P (2007) The changing prognostic determinants in the critically ill patient In: Vincent JL (ed) 2007 Yearbook of intensive care and emergency medicine. Springer-Verlag, Berlin, pp 899–907Google Scholar
  50. 50.
    Moreno RP, Afonso S (2008) Building and using outcome prediction models: should we be lumpers or splitters? In: Kuhlen R, Moreno R, Ranieri M, Rhodes A (eds) Controversies in intensive care medicine. Medizinisch Wissenschaftiche Verlagsgesellschaft, Berlin, pp 415–419Google Scholar
  51. 51.
    Knaus WA, Draper EA, Wagner DP, Zimmerman JE (1985) APACHE II: a severity of disease classification system. Crit Care Med 13:818–829CrossRefPubMedGoogle Scholar
  52. 52.
    Zimmerman JE, Kramer AA, McNair DS, Malila FM (2006) Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients. Crit Care Med 34:1297–1310CrossRefPubMedGoogle Scholar
  53. 53.
    Higgins TL, Teres D, Copes WS, Nathanson BH et al (2007) Assessing contemporary intensive care unit outcome: an updated Mortality Probability Admission Model (MPM0-III). Crit Care Med 35:827–835CrossRefPubMedGoogle Scholar
  54. 54.
    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 307:977–981CrossRefGoogle Scholar
  55. 55.
    Harrison DA, Parry GJ, Carpenter JR et al (2007) A new risk prediction model for critical care: the Intensive Care National Audit & Research Centre (ICNARC) model. Crit Care Med 35:1091–1098CrossRefPubMedGoogle Scholar
  56. 56.
    Bastos PG, Sun X, Wagner DP et al The Brazil APACHE III Study Group (1996) Application of the APACHE III prognostic system in Brazilian intensive care units: a prospective multicenter study. Intensive Care Med 22:564–570CrossRefPubMedGoogle Scholar
  57. 57.
    Bastos PG, Knaus WA, Zimmerman JE et al The Brazil APACHE III Study Group (1996) The importance of technology for achieving superior outcomes from intensive care. Intensive Care Med 22:664–669CrossRefPubMedGoogle Scholar
  58. 58.
    Moreno R, Matos R (2000) The “new” scores: what problems have been fixed, and what remain. Curr Opin Crit Care 6:158–165CrossRefGoogle Scholar
  59. 59.
    Angus DC, Linde-Zwirble WT, Lidicker J et al (2001) Epidemiology of severe sepsis in the United States: analysis of incidence, outcome and associated costs of care. Crit Care Med 29:1303–1310CrossRefPubMedGoogle Scholar
  60. 60.
    Martin GS, Mannino DM, Eaton S, Moss M (2003) The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med 348:1546–1554CrossRefPubMedGoogle Scholar
  61. 61.
    Metnitz PG, Moreno RP, Almeida E et al SAPS 3 Investigators (2005) SAPS 3. From evaluation of the patient to evaluation of the intensive care unit. Part I: objectives, methods and cohort description. Intensive Care Med 31:1336–1344CrossRefPubMedGoogle Scholar
  62. 62.
    Esteban A, Frutos-Vivar F, Ferguson ND (2007) The epidemiology of mechanical ventilation. In: Kuhlen R, Moreno R, Ranieri M, Rhodes A (eds) 25 Years of progress and innovation in intensive care medicine. Medizinisch Wissenschaftliche Verlagsgesellschaft, Berlin, pp 93–100Google Scholar
  63. 63.
    Esteban A, Anzueto A, Frutos F et al for the Mechanical Ventilation International Study Group (2002) Characteristics and outcomes in adult patients receiving mechanical ventilation: a 28-day international study. JAMA 287:345–355CrossRefPubMedGoogle Scholar
  64. 64.
    Esteban A, Anzueto A, Alia I et al (2000) How is mechanical ventilation employed in the intensive care unit? An international utilization review. Am J Respir Crit Care Med 161:1450–1458PubMedGoogle Scholar
  65. 65.
    Metnitz PGH, Metnitz B, Moreno RP et al on behalf of the SAPS 3 Investigators (2009) Epidemiology of mechanical ventilation: analysis of the SAPS 3 Database. Intensive Care Med 35(5):816–825CrossRefPubMedGoogle Scholar
  66. 66.
    Vincent J-L, Sakr Y, Sprung C et al (2004) Patterns of infection in European intensive care units: results of the SOAP study. Am J Respir Crit Care Med 169:A846Google Scholar
  67. 67.
    Moreno RP, Metnitz B, Adler L et al SAPS 3 Investigators (2008) Sepsis mortality prediction based on predisposition, infection and response. Intensive Care Med 34:496–504CrossRefPubMedGoogle Scholar
  68. 68.
    Esteban A, Ferguson ND, Meade MO et al (2008) Evolution of mechanical ventilation in response to clinical research. Am J Respir Crit Care Med 177:170–177CrossRefPubMedGoogle Scholar
  69. 69.
    Sprung CL, Maia P, Bulow H-H et al the Ethicus Study Group (2007) The importance of religious affiliation and culture on end-of-life decisions in European intensive care units. Intensive Care Med 33:1732–1739CrossRefPubMedGoogle Scholar
  70. 70.
    Livingston BM, MacKirdy FN, Howie JC et al (2000) Assessment of the performance of five intensive care scoring models within a large Scottish database. Crit Care Med 28:1820–1827CrossRefPubMedGoogle Scholar
  71. 71.
    Goldstein H, Spiegelhalter DJ (1996) League tables and their limitations: statistical issues in comparisons of institutional performance. J R Stat Soc A 159:385–443CrossRefGoogle Scholar
  72. 72.
    Lee WL, Ferguson ND (2006) SOAP and sepsis — Analyzing what comes out in the wash. Crit Care Med 34:552–554CrossRefPubMedGoogle Scholar
  73. 73.
    Bellomo R, Stow PJ, Hart GK (2007) Why is there such a difference in outcome between Australian intensive care units and others? Curr Opin Anaesthesiology 20:100–105CrossRefGoogle Scholar
  74. 74.
    Rowan K (1996) The reliability of case mix measurements in intensive care. Curr Opin Crit Care 2:209–213CrossRefGoogle Scholar
  75. 75.
    Bosman RJ, Oudemane van Straaten HM, Zandstra DF (1998) The use of intensive care information systems alters outcome prediction. Intensive Care Med 24:953–958CrossRefPubMedGoogle Scholar
  76. 76.
    Suistomaa M, Kari A, Ruokonen E, Takala J (2000) Sampling rate causes bias in APACHE II and SAPS II scores. Intensive Care Med 26:1773–1778CrossRefPubMedGoogle Scholar
  77. 77.
    Metnitz B, Schaden E, Moreno R et al on behalf of the ASDI Study Group (2009) Austrian validation and customization of the SAPS 3 Admission Score. Intensive Care Med 35:616–622CrossRefPubMedGoogle Scholar
  78. 78.
    Sinuff T, Adhikari NKJ, Cook DJ et al (2006) Mortality predictions in the intensive care unit: comparing physicians with scoring systems. Crit Care Med 34:878–885CrossRefPubMedGoogle Scholar
  79. 79.
    Booth FV, Short M, Shorr AF et al (2006) Application of a population-based severity scoring system to individual patients results in frequent misclassification. Crit Care 9:R522–R529CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia 2009

Authors and Affiliations

  • R. P. Moreno
    • 1
  • A. C. Diogo
    • 1
  • S. Afonso
    • 1
  1. 1.Unidade de Cuidados Intensivos Polivalente, Hospital de St. António dos CapuchosCentro Hospitalar de Lisboa Central E.P.E.LisbonPortugal

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