Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-24T08:50:40.376Z Has data issue: false hasContentIssue false
  • English
  • Français

Assessment of three different mortality prediction models in four well-defined critical care patient groups at two points in time: a prospective cohort study

Published online by Cambridge University Press:  01 August 2007

L. Fischler ,
F. Lelais ,
J. Young ,
B. Buchmann ,
H. Pargger  and
M. Kaufmann
L. Fischler*
Affiliation:
University Hospital, Department of Anesthesiology and Surgical Intensive Care, Basel, Switzerland
F. Lelais
Affiliation:
University Hospital, Department of Anesthesiology and Surgical Intensive Care, Basel, Switzerland
J. Young
Affiliation:
University Hospital, Institute for Clinical Epidemiology, Basel, Switzerland
B. Buchmann
Affiliation:
University Hospital, Department of Anesthesiology and Surgical Intensive Care, Basel, Switzerland
H. Pargger
Affiliation:
University Hospital, Department of Anesthesiology and Surgical Intensive Care, Basel, Switzerland
M. Kaufmann
Affiliation:
University Hospital, Department of Anesthesiology and Surgical Intensive Care, Basel, Switzerland
*
Correspondence to: Lukas Fischler, Department of Anesthesiology and Surgical Intensive Care, University Hospital, CH-4031 Basel, Switzerland. E-mail: [email protected]; Tel: +41 61 2657254; Fax: +41 61 2657320
Get access

Summary

Background and objective

Mortality prediction systems have been calculated and validated from large mixed ICU populations. However, in daily practice it is often more important to know how a model performs in a patient subgroup at a specific ICU. Thus, we assessed the performance of three mortality prediction models in four well-defined patient groups in one centre.

Methods

A total of 960 consecutive adult patients with either severe head injury (n = 299), multiple injuries (n = 208), abdominal aortic aneurysm (n = 267) or spontaneous subarachnoid haemorrhage (n = 186) were included. Calibration, discrimination and standardized mortality ratios were determined for Simplified Acute Physiology Score II, Mortality Probability Model II (at 0 and 24 h) and Injury Severity Score. Effective mortality was assessed at hospital discharge and after 1 yr.

Results

Eight hundred and fifty-five (89%) patients survived until hospital discharge. Over all four patient groups, Mortality Probability Model II (24 h) had the best predictive accuracy (standardized mortality ratio 0.62) and discrimination (area under the receiver operating characteristic curve 0.9), but Simplified Acute Physiology Score II performed well for patients with subarachnoid haemorrhage. Overall calibration was poor for all models (Hosmer–Lemeshow Type C-values between 20 and 26). Injury Severity Score had the worst discrimination in trauma patients. All models over-estimated hospital mortality in all four patient groups, and these estimates were more like the mortality after 1 yr.

Conclusions

In our surgical ICU, Mortality Probability Model II (24 h) performed slightly better than Simplified Acute Physiology Score II in terms of overall mortality prediction and discrimination; Injury Severity Score was the worst model for mortality prediction in trauma patients.

Keywords

CRITICAL CARE, intensive careHEALTH STATUS INDICATOR, severity of illness indexTRAUMA SEVERITY INDICES, injury severity scoreHOSPITAL MORTALITY
Type
Original Article
Information
European Journal of Anaesthesiology , Volume 24 , Issue 8 , August 2007 , pp. 676 - 683
Copyright
Copyright © European Society of Anaesthesiology 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.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: 2957–2963.CrossRefGoogle ScholarPubMed
2.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: 2478–2486.Google Scholar
3.Baker, SP, O'Neill, B, JrHaddon, W, Long, WB. The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma 1974; 14: 187–196.Google Scholar
4.Moreno, R, Apolone, G, Miranda, DR. Evaluation of the uniformity of fit of general outcome prediction models. Intensive Care Med 1998; 24: 40–47.Google Scholar
5.Apolone, G, Bertolini, G, D'Amico, R et al. . The performance of SAPS II in a cohort of patients admitted to 99 Italian ICUs: results from GiViTI. Gruppo Italiano per la Valutazione degli interventi in Terapia Intensiva. Intensive Care Med 1996; 22: 1368–1378.Google Scholar
6.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: 192–197.Google Scholar
7.Moreno, R, Miranda, DR, Fidler, V, Van Schilfgaarde, R. Evaluation of two outcome prediction models on an independent database. Crit Care Med 1998; 26: 50–61.Google Scholar
8.Moreno, R, Morais, P. Outcome prediction in intensive care: results of a prospective, multicentre, Portuguese study. Intensive Care Med 1997; 23: 177–186.Google Scholar
9.Randolph, AG, Guyatt, GH, Carlet, J. Understanding articles comparing outcomes among intensive care units to rate quality of care. Evidence based medicine in critical care group. Crit Care Med 1998; 26: 773–781.Google Scholar
10.DeLong, ER, DeLong, DM, Clarke-Pearson, DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44: 837–845.Google Scholar
11.Hosmer, DW, Hosmer, T, Le Cessie, S, Lemeshow, S. A comparison of goodness-of-fit tests for the logistic regression model. Stat Med 1997; 16: 965–980.Google Scholar
12.Kirkwood, B, Sterne, JAC. Essential of Medical Statistics. Oxford: Blackwell Publishers, 2003: 268–271.Google Scholar
13.Arabi, Y, Al Shirawi, N, Memish, Z, Venkatesh, S, Al-Shimemeri, A. Assessment of six mortality prediction models in patients admitted with severe sepsis and septic shock to the intensive care unit: a prospective cohort study. Crit Care 2003; 7: R116–R122.Google Scholar
14.Barriere, SL, Lowry, SF. An overview of mortality risk prediction in sepsis. Crit Care Med 1995; 23: 376–393.Google Scholar
15.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: 1779–1785.Google Scholar
16.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: 1327–1335.Google Scholar
17.Le Gall, JR, Lemeshow, S, Leleu, G et al. . Customized probability models for early severe sepsis in adult intensive care patients. Intensive Care Unit Scoring Group. JAMA 1995; 273: 644–650.Google Scholar
18.Nouira, S, Belghith, M, Elatrous, S et al. . Predictive value of severity scoring systems: comparison of four models in Tunisian adult intensive care units. Crit Care Med 1998; 26: 852–859.Google Scholar
19.Patel, PA, Grant, BJ. Application of mortality prediction systems to individual intensive care units. Intensive Care Med 1999; 25: 977–982.Google Scholar
20.Pettila, V, Pettila, M, Sarna, S, Voutilainen, P, Takkunen, O. Comparison of multiple organ dysfunction scores in the prediction of hospital mortality in the critically ill. Crit Care Med 2002; 30: 1705–1711.Google Scholar
21.Schellongowski, P, Benesch, M, Lang, T et al. . Comparison of three severity scores for critically ill cancer patients. Intensive Care Med 2004; 30: 430–436.Google Scholar
22.Sculier, JP, Paesmans, M, Markiewicz, E, Berghmans, T. Scoring systems in cancer patients admitted for an acute complication in a medical intensive care unit. Crit Care Med 2000; 28: 2786–2792.Google Scholar
23.Staudinger, T, Stoiser, B, Mullner, M et al. . Outcome and prognostic factors in critically ill cancer patients admitted to the intensive care unit. Crit Care Med 2000; 28: 1322–1328.Google Scholar
24.Wong, DT, Barrow, PM, Gomez, M, McGuire, GP. A comparison of the Acute Physiology and Chronic Health Evaluation (APACHE) II Score and the Trauma-Injury Severity Score (TRISS) for outcome assessment in intensive care unit trauma patients. Crit Care Med 1996; 24: 1642–1648.Google Scholar
25.Aegerter, P, Boumendil, A, Retbi, A et al. . SAPS II revisited. Intensive Care Med 2005; 31: 416–423.CrossRefGoogle ScholarPubMed
26.Baltas, I, Gerogiannis, N, Sakellariou, P et al. . Outcome in severely head injured patients with and without multiple trauma. J Neurosurg Sci 1998; 42: 85–88.Google ScholarPubMed
27.Chiang, VL, Claus, EB, Awad, IA. Toward more rational prediction of outcome in patients with high-grade subarachnoid hemorrhage. Neurosurgery 2000; 46: 28–35.Google Scholar
28.Clayton, TJ, Nelson, RJ, Manara, AR. Reduction in mortality from severe head injury following introduction of a protocol for intensive care management. Br J Anaesth 2004; 93: 761–767.CrossRefGoogle ScholarPubMed
29.Kniemeyer, HW, Kessler, T, Reber, PU et al. . Treatment of ruptured abdominal aortic aneurysm, a permanent challenge or a waste of resources? Prediction of outcome using a multi-organ-dysfunction score. Eur J Vasc Endovasc Surg 2000; 19: 190–196.Google Scholar
30.Lai, YC, Chen, FG, Goh, MH, Koh, KF. Predictors of long-term outcome in severe head injury. Ann Acad Med Singapore 1998; 27: 326–331.Google Scholar
31.Lazarides, MK, Arvanitis, DP, Drista, H, Staramos, DN, Dayantas, JN. POSSUM and APACHE II scores do not predict the outcome of ruptured infrarenal aortic aneurysms. Ann Vasc Surg 1997; 11: 155–158.Google Scholar
32.Muckart, DJ, Bhagwanjee, S, Gouws, E. Validation of an outcome prediction model for critically ill trauma patients without head injury. J Trauma 1997; 43: 934–938; discussion 938–939.CrossRefGoogle ScholarPubMed
33.Noel, AA, Gloviczki, P, JrCherry, KJ et al. . Ruptured abdominal aortic aneurysms: the excessive mortality rate of conventional repair. J Vasc Surg 2001; 34: 41–46.Google Scholar
34.Saveland, H, Sonesson, B, Ljunggren, B et al. . Outcome evaluation following subarachnoid hemorrhage. J Neurosurg 1986; 64: 191–196.Google Scholar
35.Signorini, DF, Andrews, PJ, Jones, PA, Wardlaw, JM, Miller, JD. Predicting survival using simple clinical variables: a case study in traumatic brain injury. J Neurol Neurosurg Psychiatry 1999; 66: 20–25.Google Scholar
36.Tunnell, RD, Millar, BW, Smith, GB. The effect of lead time bias on severity of illness scoring, mortality prediction and standardised mortality ratio in intensive care – a pilot study. Anaesthesia 1998; 53: 1045–1053.Google Scholar
37.Claassen, J, Vu, A, Kreiter, KT et al. . Effect of acute physiologic derangements on outcome after subarachnoid hemorrhage. Crit Care Med 2004; 32: 832–838.Google Scholar
38.Sakr, YL, Lim, N, Amaral, AC et al. . Relation of ECG changes to neurological outcome in patients with aneurysmal subarachnoid hemorrhage. Int J Cardiol 2004; 96: 369–373.Google Scholar
39.Gotoh, O, Tamura, A, Yasui, N et al. . Glasgow Coma Scale in the prediction of outcome after early aneurysm surgery. Neurosurgery 1996; 39: 19–24.Google Scholar
40.Niskanen, MM, Hernesniemi, JA, Vapalahti, MP, Kari, A. One-year outcome in early aneurysm surgery: prediction of outcome. Acta Neurochir (Wien) 1993; 123: 25–32.Google Scholar
41.Rosen, DS, Macdonald, RL. Grading of subarachnoid hemorrhage: modification of the world World Federation of Neurosurgical Societies scale on the basis of data for a large series of patients. Neurosurgery 2004; 54: 566–575.Google Scholar
42.Dereeper, E, Ciardelli, R, Vincent, JL. Fatal outcome after polytrauma: multiple organ failure or cerebral damage? Resuscitation 1998; 36: 15–18.Google Scholar