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.

Rowan, Kathy; Kerr, John H.; Major, E.; McPherson, Klim; Short, Alasdair; Mp, Vessey

doi:10.1136/bmj.307.6910.977

Cited by 275 publications

(139 citation statements)

References 26 publications

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“…Although more recent severity scores versions were developed in the nineties [2][3][4], the APACHE II remains, to date, the most widely used scoring system for ICUs assessment and for clinical trials conducted in the field of critical care medicine. Nevertheless, several studies showed a deterioration of both APACHE II and SAPS II scores performances [5][6][7][8]. The recently published SAPS 3 admission score [9] is a model built to predict hospital mortality from admission data (recorded within ±1 h).…”

Section: Introductionmentioning

confidence: 99%

SAPS 3 admission score: an external validation in a general intensive care population

et al. 2008

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Objectives: To validate the SAPS 3 admission score in an independent general intensive care case mix and to compare its performances with the APACHE II and the SAPS II scores. Design: Cohort observational study. Setting: A 26-bed general ICU from a Tertiary University Hospital. Patients and participants: Eight hundred and fifty-one consecutive patients admitted to the ICU over an 8-month period. Of these patients, 49 were readmissions, leaving 802 patients for further analysis. Intervention: None. Measurements and results: APACHE II, SAPS II and SAPS 3 variables were prospectively collected; scores and their derived probability of death were calculated according to their original manuscript description. The discriminative power was assessed using the area under the ROC curve (AUROC) and calibration was verified with the Hosmer-Lemeshow goodness-of-fit test. The AUROC of the APACHE II model (AUROC = 0.823) was significantly lower than those of the SAPS II (AUROC = 0.850) and SAPS 3 models (AUROC = 0.854) (P = 0.038). The calibration of the APACHE II model (P = 0.037) and of the SAPS 3 global model (P = 0.035) appeared unsatisfactory. On the contrary, both SAPS II model and SAPS 3 model customised for Central and Western Europe had a good calibration. However, in our study case mix, SAPS II model tended to overestimate the probability of death. Conclusion: In this study, the SAPS 3 admission score and its prediction model customised for Central and Western Europe was more discriminative and better calibrated than APACHE II, but it was not significantly better than the SAPS II.

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Section: Introductionmentioning

confidence: 99%

SAPS 3 admission score: an external validation in a general intensive care population

et al. 2008

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“…Prospective validation in independent patient population (n 5030 patients) showed good results; the area under the ROC curve in a US population was 0.86 and the p value for the goodness of fit statistic was 0.74 [2]. The Intensive Care Society APACHE II study [19] reported an ROC value of 0.83 for its performance in 8724 critically ill patients in the UK; the goodness of fit statistic had a p value less than 0.05. This need not necessarily have reflected poor calibration in the UK sample but may have been a function of the large numbers of critically ill patients enrolled.…”

Section: Apache I and Iimentioning

confidence: 97%

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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“…This has recently been reviewed by Knaus and colleagues [2] and its importance is emphasised in the RICP manual which states 'If the patient has been given centrally acting depressant drugs prior to admission then a normal neurological score should be given (GCS = 15)'. Hope and Plenderleith admit that data incorrect in this respect may have contributed to five of their cases of false prediction of death, but how did they exclude it in the others?…”

Section: Mortality Prediction Algorithm and Intensive Carementioning

confidence: 99%