Predicting Patient Outcome from Intensive Care: A Guide to APACHE, MPM, SAPS, PRISM, and Other Prognostic Scoring Systems

Seneff, Michael G.; Knaus, William A.

doi:10.1177/088506669000500107

Cited by 62 publications

(27 citation statements)

References 87 publications

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“…Although prognostic scores are important in the decision-making process, it must be borne in mind that no model can predict the outcome for a patient with 100% accuracy (53)(54)(55). Thus, prognostic models should be used as tools to supplement, rather than substitute, clinical assessment.…”

Section: Discussionmentioning

confidence: 99%

Prognostic scores in cirrhotic patients admitted to a gastroenterology intensive care unit

Freire

Romãozinho²,

Amaro³

et al. 2011

Rev. esp. enferm. dig.

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Aim: to assess the prognostic accuracy of Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II, Sequential Organ Failure Assessment (SOFA), Model for End-stage Liver Disease (MELD) and ChildPugh-Turcotte (CPT) in predicting GICU mortality in cirrhotic patients.Methods: the study involved 124 consecutive cirrhotic admissions to a GICU. Clinical data, prognostic scores and mortality were recorded. Discrimination was evaluated with area under receiver operating characteristic curves (AUC). Calibration was assessed with Hosmer-Lemeshow goodness-of-fit test.Results: GICU mortality was 9.7%. Mean APACHE II, SAPS II, SOFA, MELD and CPT scores for survivors (13.6, 25.4, 3.5, 18.0 and 8.6, respectively) were found to be significantly lower than those of non-survivors (22.0, 47.5, 10.1, 30.7 and 12.5, respectively) (p < 0.001). All the prognostic systems showed good discrimination, with AUC = 0.860, 0.911, 0.868, 0.897 and 0.914 for APACHE II, SAPS II, SOFA, MELD and CPT, respectively. Similarly, APACHE II, SAPS II, SOFA, MELD and CPT scores achieved good calibration, with p = 0.146, 0.120, 0.686, 0.267 and 0.120, respectively. The overall correctness of prediction was 81.9%, 86.1%, 93.3%, 90.7% and 87.7% for the APA-CHE II, SAPS II, SOFA, MELD and CPT scores, respectively.Conclusions: in cirrhotics admitted to a GICU, all the tested scores have good prognostic accuracy, with SOFA and MELD showing the greatest overall correctness of prediction. INTRODUCTIONPrognostic models are used for comparison and quality assessment of different Intensive Care Units (ICUs) and within the same ICU over time, for audit and clinical research, for evaluating therapeutic effectiveness and for guiding discussions between clinicians and families (1-6).The incidence of liver cirrhosis is increasing exponentially and cirrhotic patients admitted to ICUs have high mortality rates and a high rate of consumption of resources (7-18). Furthermore, a significant percentage (36.7%) of the total cost of ICU care for cirrhotic patients is spent on those who do not survive (11,16,19). Additionally, it must be borne in mind that ICUs are a very limited health resource (10,11,18). Thus, predictive models could be very important decision-making tools in this setting.Two types of prognostic scores can be used in cirrhotics admitted to ICUs: general prognostic models and disease-specific models. The general prognostic models include two main categories: firstly, those evaluating severity of illness, namely the Acute Physiology and Chronic Health Evaluation (APACHE) II (20) and Simplified Acute Physiology Score (SAPS) II (21); secondly, models quantifying organ dysfunction and failure, of which the most widely-used is the Sequential Organ Failure Assessment (SOFA) (22,23 Vol. 103. N.° 4, pp. 177-183, 2011 Received: 04-10-10. Accepted: 02-12-10.

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Section: Discussionmentioning

confidence: 99%

Prognostic scores in cirrhotic patients admitted to a gastroenterology intensive care unit

Freire

Romãozinho²,

Amaro³

et al. 2011

Rev. esp. enferm. dig.

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“…9,10 As part of this, an increased emphasis has been placed on the ability to predict clinical outcome soon after admission to ICU; a number of prognostic systems have been developed to identify which patients are likely to have a favorable outcome and which are unlikely to survive. 11 Making such predictions is important in allocating potentially scarce resources but is also of potential clinical benefit to physicians in making treatment decisions and advising patients and their families about likely or expected outcomes. Previous retrospective studies of ICU admissions in patients with hematologic malignancies or who have undergone BMT have studied the value of utilizing the APACHE II scoring system [12][13][14] with conflicting results.…”

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confidence: 99%

Admission of bone marrow transplant recipients to the intensive care unit: outcome, survival and prognostic factors

Jackson

Tweeddale

Barnett

et al. 1998

Bone Marrow Transplant

131

115

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Summary:The role of ICU support in BMT patients is controversial. In an era of constrained resources, the use of prognostic factors predicting outcome may be helpful in identifying patients who are most likely (or unlikely) to benefit from this intervention. We attempted to define the survival of patients admitted to ICU following autologous or allogeneic BMT and to identify those factors important in determining patient outcome. A retrospective study of all adult BMT recipients admitted to intensive care over a 6 year study period was performed to determine overall and prognostic indicators of poor outcome. Pre-treatment, pre-ICU admission and ICU admission data were analyzed to identify factors predicting long-term survival. 116 patients were admitted to ICU on 135 separate occasions with the primary reasons for admission being respiratory failure (66%), sepsis associated with hypotension (10%), and cardiorespiratory failure (8%). No pre-ICU characteristics were predictive of survival. Univariate analysis identified the number of support measures required, the need for ventilation or hemodynamic support, the APACHE II score, the year of ICU admission and the serum bilirubin as significant predictors of post-discharge survival. On multivariate analysis the year of ICU admission, the need for hemodynamic support and the serum bilirubin remained significant. The APACHE II score significantly underestimated survival in the 46% of patients with scores less than 35, and could only be used to predict 100% mortality when it exceeded 45. Twenty-three percent of all BMT patients admitted to the ICU and 17% of ventilated patients survived to hospital discharge. Of the 27 patients surviving to leave hospital, 16 remain alive with a median follow-up of 4. was identified which could be used to predict futility but patients requiring both hemodynamic support and mechanical ventilation, and those with an APACHE II score greater than 45 have a very poor prognosis and are unlikely to benefit from lengthy ICU support.

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“…Only a brief description of the scoring system will be given as these systems have been extensively reviewed elsewhere [16,17]. However, their performance will be described in detail.…”

Section: General Scoring Systems Used At a Specific Timementioning

confidence: 99%

Severity of illness scoring systems and performance appraisal

Ridley¹

1998

Anaesthesia

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SummaryA large number of severity of illness scoring systems have been developed and they are widely used in intensive care practice. However, they are complex systems with their basis in mathematics. To use such systems effectively, it is important to appreciate what factors influence their performance so that they can be compared fairly and used most appropriately. The purpose of this review is to describe the methods commonly used to assess the various facets of performance in severity of illness scoring systems. The performance of the most frequently used scoring systems in adult intensive care practice are presented. The shortfalls, misuse and strengths of scoring systems are also discussed.Keywords Intensive care; severity of illness scoring systems. Severity of illness scores stratify critically ill patients, provide meaningful information in many clinical contexts and collate clinical practice. Generally, severity of illness scores measure the degree of illness and reflect the complexity of the disease process. However, such systems have had their use extended so that they may be used to predict and compare outcomes, allocate resources and examine the process of care. There is little doubt that severity scoring systems have revolutionised intensive care. However, their limitations include a failure to predict functional status or quality of life after critical illness.As with any tool or model, it is important that the correct severity scoring system is selected and then applied in the way its developers intended. Therefore, the purpose of this review is to analyse critically the development and performance of commonly employed intensive care severity of illness scores. The commonly used general adult severity of illness scores, measuring severity of illness at a fixed point and over time, will be described. Finally, their limitations and misuse will be briefly presented. AppraisalThe critical appraisal of the development of a severity of illness score involves the measurement of accuracy (calibration and discrimination), reliability, content validity and methodological rigour. Accuracy -calibrationCalibration refers to how closely the estimated probabilities of mortality generated by the severity scoring system correlate with actual mortality over the entire range of probabilities. In other words, this is the accuracy of measurement for every interval of measurement. Calibration is usually tested with a 'goodness of fit' test, where a large 'p' value is sought, suggesting that patients predicted to die and those who actually die come from the 1185ᮊ 1998 Blackwell Science Ltd same population. One such goodness of fit test is the Hosmer-Lemeshow C statistic [6] and an example of the results is shown in Table 1.The Hosmer-Lemeshow goodness of fit C statistic compares the observed and expected frequencies over the entire range of deciles of risk from low to high and expresses the likelihood of the distributions being different using the Chi-squared statistic. In the example given, the p value is 0.591, sug...

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Predicting Patient Outcome from Intensive Care: A Guide to APACHE, MPM, SAPS, PRISM, and Other Prognostic Scoring Systems

Cited by 62 publications

References 87 publications

Prognostic scores in cirrhotic patients admitted to a gastroenterology intensive care unit

Prognostic scores in cirrhotic patients admitted to a gastroenterology intensive care unit

Admission of bone marrow transplant recipients to the intensive care unit: outcome, survival and prognostic factors

Severity of illness scoring systems and performance appraisal

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