Consult the medical resource doctors trust

UpToDate is one of the most respected medical information resources in the world, used by over 360,000 doctors and thousands of patients to find answers to medical questions.

  • Content written by a faculty of over 4,000 physicians from leading medical institutions
  • Unbiased: free of advertising or pharmaceutical funding
  • Evidence-based treatment recommendations
  • Continuously updated to incorporate new medical findings

Predictive scoring systems for the severity of illness in the intensive care unit

INTRODUCTION

Clinicians can accurately predict the outcome for patients who are severely ill and for those who have an excellent prognosis. However, disease severity among most patients in the intensive care unit (ICU) lies between these two extremes.

Measuring the severity of disease and prognosis in patients in the ICU is very important because the quality of patient care across ICUs cannot be compared without some objective index of disease severity, predictive scoring systems can provide a stable foundation for research into the therapeutic efforts and the economics of care in the ICU, and predictive scoring systems may plot the course of critical illness and help clinical decision making.

In this topic review, the characteristics and clinical use of three validated scoring systems, the Acute Physiologic and Chronic Health Evaluation (APACHE) system, the Simplified Acute Physiologic Score (SAPS), and the Mortality Prediction Model (MPM) are discussed.

CHARACTERISTICS OF SCORING SYSTEMS

There are two important principles in assessing outcome instruments. First, the tools should measure an important outcome. As an example, most ICU scoring systems examine hospital mortality; however, new interest has developed in the assessment of long-term mortality and functional status. Second, the scoring instruments must be easy to use, since collecting data on critically ill patients can be time-consuming and costly.

All critical care predictive scoring systems utilize numerical values to describe the severity of a patient's illness. Scores are then assigned predicted mortalities using a mathematical formula. The usefulness of any system depends upon its predictive accuracy. The two characteristics used to judge the value of a predictive system are discrimination and calibration.
To continue reading this article you need to subscribe.

Read the rest of this article and others like it

The content on the UpToDate website is not intended nor recommended as a substitute for medical advice, diagnosis, or treatment. Always seek the advice of your own physician or other qualified health care professional regarding any medical questions or conditions. The use of this website is governed by the UpToDate Terms of Use (click here) ©2010 UpToDate, Inc.
References Top
  1. Cowen, JS, Kelley, MA. Predicting intensive care unit outcome: Errors and bias in using predictive scoring systems. Crit Care Clin 1994; 10:53.
  2. Escarce, JJ, Kelley, MA. Admission source to the medical intensive care unit predicts hospital death independent of APACHE II score. JAMA 1990; 264:2389.
  3. Capuzzo, M, Valpondi, V, Sgarbi, A, et al. Validation of severity scoring systems SAPS II and APACHE II in a single-center population. Intensive Care Med 2000; 26:1779.
  4. 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.
  5. Zimmerman, JE, Kramer, AA, McNair, DS, Malila, FM. Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today's critically ill patients. Crit Care Med 2006; 34:1297.
  6. Zimmerman, JE, Kramer, AA, McNair, DS, et al. Intensive care unit length of stay: Benchmarking based on Acute Physiology and Chronic Health Evaluation (APACHE) IV. Crit Care Med 2006; 34:2517.
  7. 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.
  8. Castella, X, Artigas, A, Bion, J, Kari, A. 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 1995; 23:1327.
  9. Auriant, I, Vinatier, I, Thaler, F, et al. Simplified acute physiology score II for measuring severity of illness in intermediate care units. Crit Care Med 1998; 26:1368.
  10. Metnitz, PG, Valentin, A, Vesely, H, et al. Prognostic performance and customization of the SAPS II: results of a multicenter Austrian study. Simplified Acute Physiology Score. Intensive Care Med 1999; 25:192.
  11. 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.
  12. Lemeshow, S, LeGall, JR. Modeling the severity of illness of ICU patients. A systems update. JAMA 1994; 272:1049.
  13. Glance, LG, Osler, TM, Dick, A. Rating the quality of intensive care units: is it a function of the intensive care unit scoring system?. Crit Care Med 2002; 30:1976.
  14. Glance, LG, Osler, TM, Dick, AW. 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. Crit Care Med 2002; 30:1995.
  15. Ho, KM, Dobb, GJ, Knuiman, M, et al. A comparison of admission and worst 24-hour Acute Physiology and Chronic Health Evaluation II scores in predicting hospital mortality: a retrospective cohort study. Crit Care 2006; 10:R4.
  16. Wagner, DP, Knaus, WA, Harrell, FE, et al. Daily prognostic estimates for critically ill adults in intensive care units: Results from a prospective, multicenter, inception cohort analysis. Crit Care Med 1994; 22:1359.
  17. A controlled trial to improve care for seriously ill hospitalized patients. The study to understand prognoses and preferences for outcomes and risks of treatments (SUPPORT). The SUPPORT Principal Investigators. JAMA 1995; 274:1591.
  18. Kollef, MH, Schuster, DP. Predicting intensive care unit outcome with scoring systems. Underlying concepts and principles. Crit Care Clin 1994; 10:1.
  19. Knaus, WA. Variations in mortality and length of stay in intensive care units. Ann Intern Med 1993; 188:753.
  20. 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.
  21. Multz, AS, Chalfin, DB, Samson, IM, et al. A "closed" medical intensive care unit (MICU) improves resource utilization when compared with an "open" MICU. Am J Respir Crit Care Med 1998; 157:1468.
  22. Dimick, JB, Pronovost, PJ, Heitmiller, RF, Lipsett, PA. Intensive care unit physician staffing is associated with decreased length of stay, hospital cost, and complications after esophageal resection. Crit Care Med 2001; 29:753.
  23. Li, TC, Phillips, MC, Shaw, L, et al. On-site physician staffing in a community hospital intensive care unit. Impact on test and procedure use and on patient outcome. JAMA 1984; 252:2023.
  24. Brown, JJ, Sullivan, GS. Effect on ICU mortality of a full-time critical care specialist. Chest 1989; 9:127.
  25. Carson, SS, Stocking, C, Podsadecki, T, et al. Effects of organizational change in the medical intensive care unit of a teaching hospital: A comparison of 'open' and 'closed' formats. JAMA 1996; 276:322.
  26. 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.
  27. Zimmerman, JE, Alzola, C, Von Rueden, KT. The use of benchmarking to identify top performing critical care units: a preliminary assessment of their policies and practices. J Crit Care 2003; 18:76.
  28. 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.
  29. Zimmerman, JE, Shortell, S, Knaus, WA, et al. Value and cost of teaching hospitals: A prospective, multicenter, inception cohort study. Crit Care Med 1993; 21:1432.
  30. Kuzniewicz, MW, Vasilevskis, EE, Lane, R, et al. Variation in ICU risk-adjusted mortality: impact of methods of assessment and potential confounders. Chest 2008; 133:1319.
  31. 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.
  32. Duke, GJ, Green, JV, Briedis, JH. Night-shift discharge from intensive care unit increases the mortality-risk of ICU survivors. Anaesth Intensive Care 2004; 32:697.
  33. Ensminger, SA, Morales, IJ, Peters, SG, et al. The hospital mortality of patients admitted to the ICU on weekends. Chest 2004; 126:1292.
  34. Zimmerman, JE, Wagner, DP, Knaus, WA, et al. The use of risk predictions to identify candidates for intermediate care units: Implications for intensive care utilization and cost. Chest 1995; 108:490.
  35. Fisher, CJ Jr, Agosti, JM, Opal, SM, Lowry, SF. Treatment of septic shock with the tumor necrosis factor receptor: Fc fusion protein. The Soluble TNF Receptor Sepsis Study Group. N Engl J Med 1996; 334:1697.
  36. Anzueto, A, Baughman, RP, Guntupalli, KK, et al. Aerosolized surfactant in adults with sepsis-induced acute respiratory distress syndrome. N Engl J Med 1996; 334:1417.
  37. Rivers, E, Nguyen, B, Havstad, S, et al. Early goal-directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med 2001; 345:1368.
  38. Abraham, E, Reinhart, K, Svoboda, P, et al. Assessment of the safety of recombinant tissue factor pathway inhibitor in patients with severe sepsis: a multicenter, randomized, placebo-controlled, single-blind, dose escalation study. Crit Care Med 2001; 29:2081.
  39. Katsaragakis, S, Papadimitropoulos, K, Antonakis, P, et al. Comparison of Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score II (SAPS II) scoring systems in a single Greek intensive care unit. Crit Care Med 2000; 28:426.
  40. Patel, PA, Grant, BJ. Application of mortality prediction systems to individual intensive care units. Intensive Care Med 1999; 25:977.
  41. Barie, PS, Hydo, LJ, Fischer, E. Comparison of APACHE II and III scoring systems for mortality prediction in critical surgical illness. Arch Surg 1995; 130:77.
  42. Brown, MC, Crede, WB. Predictive ability of acute physiology and chronic health evaluation II scoring applied to human immunodeficiency virus-positive patients. Crit Care Med 1995; 23:848.
  43. Lewinsohn, G, Herman, A, Leonov, Y, Klinowski, E. Critically ill obstetrical patients: outcome and predictability. Crit Care Med 1994; 22:1412.
white circle LOG IN
white circle DEMO