Predictive Scoring Systems in the Intensive Care Unit

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Official reprint from UpToDatewww.uptodate.com 2016 UpToDate

AuthorMark A Kelley, MD

Section EditorScott Manaker, MD, PhD

Deputy EditorGeraldine Finlay, MD

Predictive scoring systems in the intensive care unit

All topics are updated as new evidence becomes available and our peer review processis complete.

Literature review current through: Dec 2015. | This topic last updated: Nov 19, 2015.

INTRODUCTION Predictive scoring systems have been developed to measure the severity of disease and the prognosis of patients in the intensive care unit

(ICU). Such measurements are helpful for clinical decision making, standardizing research, and comparing the quality of patient care across ICUs.

Four validated predictive scoring systems are described here. They include the Acute Physiologic and Chronic Health Evaluation (APACHE) system, Simplified

Acute Physiologic Score (SAPS), Mortality Prediction Model (MPM), and Sequential Organ Failure Assessment score (SOFA).

HOW PREDICTIVE SCORING SYSTEMS WORK Critical care predictive scoring systems derive a numerical value, or severity score, from a variety of clinical

variables. The derived score quantifies the severity of illness and is entered into a mathematical equation whose solution is the likelihood of mortality during that

hospitalization.

The relationship between the severity score and the outcome is determined empirically from large data sets. Predictive scoring systems cannot predict outcomes forpopulations that were not included in the derivation data sets. Thus, ICU predictive scoring systems are not reliable for evaluating patients outside of the critical

care setting.

CHARACTERISTICS OF PREDICTIVE SCORING SYSTEMS There are two principles that are important to consider when assessing a predictive scoring

system. First, an instrument should measure an important outcome. The most widely used ICU scoring systems predict the likelihood of hospital mortality for

patients admitted to the critical care unit. Second, an instrument should be easy to use, since collecting data on critically ill patients can be time consuming and

costly.

Discrimination and calibration are two characteristics used to judge a predictive system:

PREDICTIVE SCORING SYSTEMS The four major ICU predictive scoring systems are the Acute Physiologic and Chronic Health Evaluation (APACHE)

scoring system, the Simplified Acute Physiologic Score (SAPS), the Mortality Prediction Model (MPM), and the Sequential Organ Failure Assessment (SOFA).

Acute Physiologic and Chronic Health Evaluation (APACHE) The APACHE scoring system is widely used in the United States [1

]. The most recent

versions include APACHE II through IV.

Discrimination describes the accuracy of a given prediction. As an example, if a scoring instrument predicts a mortality of 90 percent, discrimination is perfect

if the observed mortality is 90 percent.

Calibration describes how the instrument performs over a wide range of predicted mortalities. In the example above, a predictive instrument would be highly

calibrated if it were accurate at mortalities of 90, 50, and 20 percent.
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studies [16].

Mortality Prediction Model (MPM) The Mortality Prediction Model II (MPM0-II) is the most common version of the MPM. A severity score is calculated from 15

variables, as assessed at the time of ICU admission (hence the term "0"). The variables are listed in the table ( table 2).

Except for age, all of the variables are dichotomous. In other words, they are either present or absent. As an example, a systolic blood pressure 90 mmHg is worth

one point, while all other systolic blood pressure values are assigned zero points. The final score is entered into a mathematical formula whose solution provides the

predicted mortality.

The MPM0-II severity score that is measured on admission can be refined after 24 hours (MPM24-II) by updating seven of the admission variables and adding sixvariables. The updated admission variables include coma, intracranial mass effect, mechanical ventilation, metastatic disease, cirrhosis, type of admission, and

patient age. The additional variables include the following:

An advantage of the MPM24-II is that it can be compared to the SAPS and APACHE, since all three scores are determined after the f irst 24 hours of admission.

The MPM0-II is based upon data from over 12,500 patients [17]. It has excellent calibration and discrimination [11,17,18]. The updated version, the MPM0-III, has

excellent calibration, as validated on a large cohort of over 55,000 ICU patients [ 19]. MPM0-III includes the physiologic variables of MPM0-II and adds time before

ICU admission and code (resuscitation) status. There is some evidence that MPM0-III provides more accurate prediction of ICU mortality [ 20].

Sequential Organ Failure Assessment (SOFA) The SOFA uses simple measurements of major organ function to calculate a severity score. The scores are

calculated 24 hours after admission to the ICU and every 48 hours thereafter. The mean and the highest scores are most predictive of mortality. In addition, scores

that increase by about 30 percent are associated with a mortality of at least 50 percent [21].

The SOFA severity score is based upon the following measurements of organ function:

Creatinine >2 mg/dL

Urine output


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The original SOFA instrument was derived from a cohort of 1449 patients admitted to 40 ICUs in 16 countries [ 22].

COMPARISON OF THE PREDICTIVE SCORING SYSTEMS The four major ICU predictive scoring systems (APACHE, SAPS, MPM, SOFA) and their most

recent updates all have acceptable discrimination and calibration [18,20,23,24]. However, there are important limitations and methodological differences among

these instruments, including the collection of data, calculation of mortality, efficacy, and cost [25,26].

Data collection The types of variables that are measured and the timing of those measurements vary among the predictive scoring systems:

Calculations All four predictive instruments (APACHE, SAPS, MPM, and SOFA) provide a severity score to describe each patient. This score is the sum of

categorical variables described above. The severity score is most commonly used to describe and compare the level of illness in ICU patients. This is particularly

useful in designing clinical trials and other interventions.

Predicted mortality can also be assessed from these severity scores. For APACHE, a predicted mortality is calculated from the computer software described

above. For SAPS and MPM, the severity score is entered into an equation that calculates a predicted mortality. For SOFA, sequential severity scores plot the

trajectory of the clinical course to provide a semiquantitative assessment of mortality, based upon multi-organ failure.

Efficacy There have been no large, prospective studies that rigorously compare the four major ICU predictive scoring systems (APACHE, SAPS, MPM, SOFA).

The following studies illustrate the existing evidence:

Cost The APACHE III and IV predictive scoring systems require proprietary computer technology and substantial data collection. In contrast, APACHE IIcalculation software is available to the public (calculator 1). The MPM, SAPS, and SOFA scoring systems are available to the public, require less data collection,

and require no computer investment. Calculations are easily made from published equations.

USES FOR PREDICTIVE SCORING SYSTEMS While determining prognosis was the original goal of these systems, the severity scores alone have proven

most useful [28]:

Variables measured The APACHE scoring system requires collection of a wide range of physiological and general health data, while the other instruments

use concise and easily measured physiological categories to facilitate data recording [27].

Timing of the measurements The APACHE and SAPS instruments use the worst physiologic values measured within 24 hours of admission to the ICU. The

updated versions of MPM use data collected upon ICU admission and then modified 24 hours later, while the SOFA instrument uses data collected 24 hours

after admission and every 48 hours thereafter.

One retrospective study of 11,300 ICU patients from 35 hospitals compared the MPM III, SAPS II, and APACHE IV instruments [ 27]. APACHE IV offered the

best predictive accuracy. However, MPM0-III proved to be an effective alternative when cost and the complexity of data collection were considered [ 27].

A systemic review of the SOFA, SAPS I I, APACHE II, and APACHE III scoring sys tems found that the APACHE systems were slightly superior to the

SAPS II and SOFA systems in predicting ICU mortality [ 23]. The accuracy of both the SAPS II and APACHE instruments improved when combined with the

assessment of sequential SOFA scores.

Severity scores facilitate evaluation of various interventions by ensuring that patients with similar baseline risk are being compared [29]. This is particularly

common among trials of potential therapies for sepsis [30,31] or acute respiratory distress syndrome [32,33].
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LIMITATIONS OF PREDICTIVE SCORING SYSTEMS The ICU is the ideal setting for predictive scoring systems because the population is well defined,

patient care is well circumscribed, and the severity of illness in the ICU is the major determinant of hospital mortality. Despite this, predictive scoring systems have

important limitations [44,45]:

SUMMARY AND RECOMMENDATIONS

Severity scores facilitate evaluation of the quality of care by ensuring that patients with similar baseline risk are being compared. As examples, studies that

compared open with closed ICUs [34-39], the outcomes of different ICUs within a hospital [40], and the outcomes of ICUs in different hospitals [ 40-42] have

used predictive scoring systems (usually APACHE) to ensure that patients with similar baseline risk were compared.

Severity scores have been used to manage hospital resources, assigning patients with lower severity scores to less expensive settings [ 43].

Disease subsets Predictive scoring systems are developed from large data sets of ICU patients. However, these data sets are too small to assess

diseases separately. As a result, the predicted mortality of patients with certain diseases (eg, liver failure, obstetrical diseases, AIDS) may be inaccurate [46-

48]. This may also apply to specialized ICUs where some investigators have used their own validation subsets to adjust for this flaw [ 49].

Uncertain accuracy beyond the ICU These instruments were developed from, and validated on, patients admitted to ICUs across many institutions. The

scoring systems have not been validated on other hospitalized patients.

Lead time bias Patients who are transferred from other hospitals and ICUs have a mortality that is higher than predicted by the APACHE II system, a

phenomenon known as lead time bias [3]. To address this flaw, treatment location was added as a variable to APACHE III. It is uncertain how lead time bias

affects the other predictive scoring systems (ie, SAPS, MPM, or SOFA).

Need for updates All of the predictive scoring systems must be periodically updated using more current data or they may fail to capture the effects of newtechnology, practice patterns, or standards of care. Failure to update predictive scoring systems can lead to gradual loss of calibration. This results in

overestimating mortality for any given severity score [16].

ICU predictive scoring systems derive a severity score from a variety of clinical variables. This score quantifies the severity of illness and can be entered into

a mathematical equation whose solution is the likelihood of hospital mortality. (See 'How predictive scoring systems work' above.)

The four major intensive care unit (ICU) predictive scoring systems are the Acute Physiologic and Chronic Health Evaluation (APACHE) scoring system,

Simplified Acute Physiologic Score (SAPS), Mortality Prediction Model (MPM), and Sequential Organ Failure Assessment (SOFA). All have been validated

and determined to be reliable. (See 'Acute Physiologic and Chronic Health Evaluation (APACHE)'above and 'Simplified Acute Physiologic Score (SAPS)'

above and 'Mortality Prediction Model (MPM)'above and 'Sequential Organ Failure Assessment (SOFA)' above and 'Comparison of the predictive scoring

systems' above.)

The most common use of the ICU predictive scoring systems is to compare patients in clinical trials or to assess ICU quality. The role of ICU scoring

systems in clinical decision-making, especially in end-of-life care, remains unclear. (See 'Uses for predictive scoring systems' above.)

The accuracy of predicting hospital mortality is less certain for ICU patients with specific diseases (eg, liver failure, obstetrical disorders, AIDS), and may be

limited by lead time bias. The predictive modeling is not accurate for patients outside the ICU.
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REFERENCES

1. Cowen JS, Kelley MA. Errors and bias in using predictive scoring systems. Crit Care Clin 1994 10:53.2. 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.

3. Escarce JJ, Kelley MA. Admission source to the medical intensive care unit predicts hospital death independent of APACHE II score. JAMA 1990 264:2389.

4. 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 Med2000 26:1779.

5. Knaus WA, Wagner DP, Draper EA, et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest1991 100:1619.

6. 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.

7. A controlled trial to improve care for s eriously ill hospitalized patients. The study to understand prognoses and preferences for outcomes and risks oftreatments (SUPPORT). The SUPPORT Principal Investigators. JAMA 1995 274:1591.

8. Zimmerman JE, Kramer AA, McNair DS, Malila FM. Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment fortoday's critically ill patients. Crit Care Med 2006 34:1297.

9. 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.

10. Le Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA1993 270:2957.

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

12. 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.

13. Metnitz PG, Valentin A, Vesely H, et al. Prognostic performance and customization of the SAPS II: results of a multicenter Austrian study. Simplified AcutePhysiology Score. Intensive Care Med 1999 25:192.

14. Ledoux D, Canivet JL, Preiser JC, et al. SAPS 3 admission score: an external validation in a general intensive care population. Intensive Care Med 200834:1873.

15. Poole D, Rossi C, Anghileri A, et al. External validation of the Simplified Acute Physiology Score (SAPS) 3 in a cohort of 28,357 patients from 147 Italianintensive care units. Intensive Care Med 2009 35:1916.

16. Nassar AP Jr, Mocelin AO, Nunes AL, et al. Caution when using prognostic models: a prospective comparison of 3 recent prognostic models. J Crit Care

ICU scoring systems must be reassessed and updated periodically to reflect contemporary practice and patient demographics. Otherwise, the calibration of

the predictive model can decline, leading to overestimation of predicted mortality. (See 'Limitations of predictive scoring systems' above.)
http://-/?-http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/16http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/15http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/14http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/13http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/12http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/11http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/10http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/9http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/8http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/7http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/6http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/5http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/4http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/3http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/2http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/1http://www.uptodate.com/contents/license


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2012 27:423.e1.

17. Lemeshow S, Teres D, Klar J, et al. Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. JAMA 1993270:2478.

18. Lemeshow S, Le Gall JR. Modeling the severity of illness of ICU patients. A systems update. JAMA 1994 272:1049.

19. Higgins TL, Kramer AA, Nathanson BH, et al. Prospective validation of the intensive care unit admission Mortality Probability Model (MPM0-III). Crit CareMed 2009 37:1619.

20. Higgins TL, Teres D, Copes WS, et al. Assessing contemporary intensive care unit outcome: an updated Mortality Probability Admission Model (MPM0-III).Crit Care Med 2007 35:827.

21. Ferreira FL, Bota DP, Bross A, et al. Serial evaluation of the SOFA score to predict outcome in critically ill patients. JAMA 2001 286:1754.

22. Vincent JL, de Mendona A, Cantraine F, et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results ofa multicenter, prospective study. Working group on "sepsis-related problems" of the European Society of Intensive Care Medicine. Crit Care Med 199826:1793.

23. Minne L, Abu-Hanna A, de Jonge E. Evaluation of SOFA-based models for predicting mortality in the ICU: A systematic review. Crit Care 2008 12:R161.

24. Kramer AA, Higgins TL, Zimmerman JE. Comparison of the Mortality Probability Admission Model III, National Quality Forum, and Acute Physiology andChronic Health Evaluation IV hospital mortality models: implications for national benchmarking*. Crit Care Med 2014 42:544.

25. 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 200230:1976.

26. 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.

27. Kuzniewicz MW, Vasilevskis EE, Lane R, et al. Variation in ICU risk-adjusted mortality: impact of methods of assessment and potential confounders. Chest2008 133:1319.

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

29. Knaus WA, Wagner DP, Zimmerman JE, Draper EA. Variations in mortality and length of stay in intensive care units. Ann Intern Med 1993 118:753.

30. 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.

31. 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.

32. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. The AcuteRespiratory Distress Syndrome Network. N Engl J Med 2000 342:1301.

33. Anzueto A, Baughman RP, Guntupalli KK, et al. Aerosolized surfactant in adults with s epsis-induced acute respiratory distress syndrome. Exosurf AcuteRespiratory Distress Syndrome Sepsis Study Group. N Engl J Med 1996 334:1417.

34. Pronovost PJ, Angus DC, Dorman T, et al. Physician staffing patterns and clinical outcomes in critically ill patients: a systematic review. JAMA 2002288:2151.

35. 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.

36. Dimick JB, Pronovost PJ, Heitmiller RF, Lipsett PA. Intensive care unit physician staffing is associated with decreased length of stay, hospital cost, andcomplications after esophageal resection. Crit Care Med 2001 29:753.

37. 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
http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/37http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/36http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/35http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/34http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/33http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/32http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/31http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/30http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/29http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/28http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/27http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/26http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/25http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/24http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/23http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/22http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/21http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/20http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/19http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/18http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/17http://www.uptodate.com/contents/predictive-scoring-systems-in-the-intensive-care-unit/abstract/16


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outcome. JAMA 1984 252:2023.

38. Brown JJ, Sullivan G. Effect on ICU mortality of a full-time critical care specialist. Chest 1989 96:127.

39. 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.

40. 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.

41. Zimmerman JE, Alzola C, Von Rueden KT. The use of benchmarking to identify top performing critical care units: a preliminary assessment of their policiesand practices. J Crit Care 2003 18:76.

42. Zimmerman JE, Shortell SM, Knaus WA, et al. Value and cost of teaching hospitals: a prospective, multicenter, inception cohort study. Crit Care Med 199321:1432.

43. Zimmerman JE, Wagner DP, Knaus WA, et al. The use of risk predictions to identify candidates for intermediate care units. Implications for intensive careutilization and cost. Chest 1995 108:490.

44. Katsaragakis S, Papadimitropoulos K, Antonakis P, et al. Comparison of Acute Physiology and Chronic Health Evaluation II (APACHE II) and SimplifiedAcute Physiology Score II (SAPS II) scoring sys tems in a single Greek intensive care unit. Crit Care Med 2000 28:426.

45. Patel PA, Grant BJ. Application of mortality prediction systems to individual intensive care units. Intensive Care Med 1999 25:977.

46. 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.

47. Brown MC, Crede WB. Predictive ability of acute physiology and chronic health evaluation II scoring applied to human immunodeficiency virus-positivepatients. Crit Care Med 1995 23:848.

48. Lewinsohn G, Herman A, Leonov Y, Klinowski E. Critically ill obstetrical patients: outcome and predictability. Crit Care Med 1994 22:1412.

49. Sakr Y, Krauss C, Amaral AC, et al. Comparison of the performance of SAPS II, SAPS 3, APACHE II, and their customized prognostic models in a surgicalintensive care unit. Br J Anaesth 2008 101:798.

Topic 1655 Version 10.0
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GRAPHICS

Simplified acute physiologic score II (SAPS II)

Variable Range Points

Patient age


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9-10 7

6-8 13


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AIDS

Yes 17

No 0

Metastatic carcinoma Yes 9

No 0

Hematologic malignancy Yes 10

No 0

Data from: Le Gall, JR, Lemeshow, S, Saulnier, F, et al. A new simplified acute physiology score (SAPS II) based on a European/North American

multicenter study. JAMA 1993 270:2957.

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Mortality prediction model II (MPM II)

Variable Response Points

Patient age*

Medical or unscheduled surgical admission? Yes 1

No 0

Cardiopulmonary resuscitation prior to admission? Yes 1

No 0

Coma (Glasgow coma scale 3-5)?

(Does not include patients whose coma is due to overdose or who received neuromuscular blocking agents)

Yes 1

No 0

Heart rate 150 bpm? Yes 1

No 0

Systolic blood pressure 90 mmHg? Yes 1

No 0

Mechanical ventilation? Yes 1

No 0

Acute renal failure?

(Does not include pre-renal azotemia)

Yes 1

No 0

Cardiac dysrhythmias? Yes 1

No 0

Cerebrovascular accident? Yes 1

No 0

Intracranial mass effect? Yes 1

No 0

Gastrointestinal bleeding? Yes 1

No 0

Metastatic carcinoma?


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(Distant metastases only does not include local lymph node involvement) Yes 1

No 0

Cirrhosis? Yes 1

No 0

Chronic renal insufficiency?

(Creatinine >2 mg/dL chronically)

Yes 1

No 0

* Patient age does not receive points when calculating the severity score however, it is used in the formula to calculate predicted mortality.

Data from: Lemeshow, S, Teres, D, Klar, J, et al. Mortality probability models (MPM II) based on an international cohort of intensive care unit patients.

JAMA 19 93 270:247 8.

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Disclosures: Mark A Kelley, MD Nothing to disclose. Scott Manaker, MD, PhD Consultant/Advisory boards: Expert witness in workers' compensation and in medical negligence matters [Generalpulmonary and critical care medicine]. Equity Ownership/Stock Options (Spouse): Johnson & Johnson Pfizer (Numerous medications and devices). Other Financial Interest: Director of ACCPEnterprises, a wholly owned for-profit subsidiary of ACCP [General pulmonary and critical care medicine (Providing pulmonary and critical care medicine education to non-members of ACCP)].Geraldine Finlay, MD Nothing to disclose.

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Predictive Scoring Systems in the Intensive Care Unit