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Outcome prediction in critical care: physicians’ prognoses vs. scoring systems

Published online by Cambridge University Press:  23 December 2004

N. Scholz
Affiliation:
Otto-von-Guericke University, Institute of Social Medicine and Health Economics, Magdeburg, Germany
K. Bäsler
Affiliation:
University Hospital Göttingen, Centre of Anaesthesiology, Emergency and Intensive Medicine, Göttingen, Germany
P. Saur
Affiliation:
University Hospital Göttingen, Centre of Anaesthesiology, Emergency and Intensive Medicine, Göttingen, Germany
H. Burchardi
Affiliation:
University Hospital Göttingen, Centre of Anaesthesiology, Emergency and Intensive Medicine, Göttingen, Germany
S. Felder
Affiliation:
Otto-von-Guericke University, Institute of Social Medicine and Health Economics, Magdeburg, Germany
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Abstract

Summary

Background and objective: To compare the accuracy of prognoses made by intensive care physicians with the performance of two indicators, the original Simplified Acute Physiology Score (SAPS) II and a modified version optimized to the patient sample.

Methods: Data from 412 patients consecutively admitted to intensive care units of Göttingen University Hospital, Germany, were collected according to the original score criteria. Information necessary for the computation of SAPS II and the vital status on hospital discharge was recorded. To customize the original SAPS II in our cohort, the database was randomly divided into two subgroups. Logistic regression analysis with physiological values as explanatory variables was used. A bootstrap procedure completed the process. Furthermore, physicians were asked to indicate their prognostic judgement concerning the patients’ hospital mortality.

Results: Discrimination analysis showed the following areas under receiver operating characteristic curves: physicians’ prognoses 0.84 (confidence interval (CI): 0.79–89), SAPS II 0.75 (CI: 0.69–0.80) and customized SAPS 0.72 (CI: 0.66–0.78). The physician's forecast was significantly better, while the customized and the original SAPS were not substantially different as regards their accuracy.

Conclusions: Prognoses made by physicians are superior to objective models. This may result from more extensive knowledge and other kinds of information available to clinicians. A clinician's action also depends on his/her prognosis at the beginning of the treatment, giving raise to a possible correlation between medical outcome and the clinician's prognosis. Our findings indicate that physicians do not limit their prognosis to the objective factors at their disposal, but indicate that they base their decisions on experience and individual observations.

Type
Original Article
Copyright
2004 European Society of Anaesthesiology

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References

Detsky AS, Stricker SC, Mulley AG, Thibault GE. Prognosis, survival, and the expenditure of hospital resources for patients in an intensive-care unit. New Engl J Med 1981; 305: 667672.Google Scholar
Wong DT, Gomez M, McGuire GP, Kavanagh B. Utilization of intensive care unit days in a Canadian medical–surgical intensive care. Crit Care Med 1999; 27: 13191324.Google Scholar
Teres D, Rapoport J, Lemeshow S, Kim S, Akhras K. Effects of severity of illness on resource use by survivors and nonsurvivors of severe sepsis at intensive care unit admission. Crit Care Med 2002; 30: 24132419.Google Scholar
Moreno R. Severity of illness. In: Sibbald WJ, Bion JF, eds. Evaluating Critical Care. Berlin, Germany: Springer, 2001: 5168.
Karfonta T. Decision support systems in the intensive care unit: nurses’ and physicians’ experiences. PhD thesis, University of Wisconsin-Milwaukee, USA, 1999.
Schuster D. Predicting outcome after ICU admission. Chest 1992; 102: 18611870.Google Scholar
McNelis J, Marini C, Kalimi R. A comparison of predictive outcomes of APACHE II and SAPS II in a surgical intensive care unit. Am J Med Qual 2001; 16: 161165.Google Scholar
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. Crit Care Med 1995; 23: 13271335.Google Scholar
Moreno R, Reis Miranda D, Fidler V. Evaluation of two outcome prediction models on an independent database. Crit Care Med 1998; 26: 5061.Google Scholar
Timsit JF, Fosse JP, Troché G, et al. Accuracy of a composite score using daily SAPS II scores for predicting hospital mortality in ICU patients hospitalized for more than 72 h. Intensive Care Med 2001; 27: 10121021.Google Scholar
Suistomaa M, Niskanen M, Kari A, Hynynen M, Takala J. Customized prediction models based on APACHE II and SAPS II scores in patients with prolonged length of stay in the ICU. Intensive Care Med 2002; 28: 479485.Google Scholar
Le Gall J, 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: 644650.Google Scholar
Katzman McClish D, Powell S. How well can physicians estimate mortality in a medical intensive care unit? Med Decis Making 1989; 9: 125132.Google Scholar
Schuster HP, Hesse M, Tröster S. Prognoseeinschätzung durch Ärzte in der Intensivmedizin. Intensivmed 1992; 29: 5560.Google Scholar
Marks RJ, Simons RS, Blizzard RA, Browne DR. Predicting outcome in intensive care units – a comparison of APACHE II with subjective assessments. Intensive Care Med 1991; 17: 159163.Google Scholar
LeGall JR, Lemeshow S, Saulnier F. A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA 1993; 270: 29572963.Google Scholar
Efron B, Tibshirani R. An Introduction to the Bootstrap. New York, USA: Chapman & Hall, 1993.
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143: 2936.Google Scholar
Moreno R, Morais P. Outcome prediction in intensive care: results of a prospective, multicenter, Portuguese study. Intensive Care Med 1997; 23: 177186.Google Scholar
Murphy-Filkins RL, Teres D, Lemeshow S, Hosmer DW. Effect of changing patient mix on the performance of an intensive care unit severity of illness model: how to distinguish a general from a special intensive care unit. Crit Care Med 1996; 24: 19681973.Google Scholar
SUPPORT Group. The SUPPORT prognostic model objective estimates of survival for seriously ill hospitalized adults. Ann Intern Med 1995; 122: 191203.
Perkins HS, Jonsen AR, Epstein WV. Providers as predictors: using outcome predictions in intensive care. Crit Care Med 1986; 14: 105111.Google Scholar