Advertisement

Journal of General Internal Medicine

, Volume 11, Issue 1, pp 16–22 | Cite as

The inability of physicians to predict the outcome of in-hospital resuscitation

  • Mark H. Ebell
  • George R. Bergus
  • Lawrence Warbasse
  • Roger Bloomer
Original Articles

Abstract

OBJECTIVE: To measure the accuracy, reliability, and discrimination of physicians’ predictions of the outcome of in-hospital cardiopulmonary resuscitation (CPR), using a large series of detailed clinical vignettes of patients with known outcomes.

DESIGN: Faculty and resident physicians at three university-affiliated generalist training programs were given one-page summaries of admission data for patients who later underwent in-hospital CPR. These summaries included all pre-arrest variables known to be related to the outcome of CPR. Physicians were asked to estimate the probability that patients would survive the resuscitation long enough to be stabilized, and the probability of survival to discharge.

SETTING: Patient cases were derived from a consecutive series of patients undergoing CPR at two urban teaching hospitals in Detroit, Michigan.

PARTICIPANTS: Faculty members and residents at a university-based department of internal medicine and two university-based departments of family medicine were surveyed.

INTERVENTIONS: Accuracy of the physician predictions was assessed by comparing the mean predicted probability of survival with the percentage of patients who actually survived. The reliability of probability estimates of survival was evaluated by assessing the numerical proximity of the estimates to the actual outcome of the resuscitative effort. The ability to discriminate between survivors and nonsurvivors was measured by comparing the mean predicted probability of survival for those patients who survived CPR with that for those who did not, and by stratifying physician predictions and measuring the area under a receiver operating characteristic (ROC) curve.

MEASUREMENTS AND MAIN RESULTS: Physicians (n=51) made a total of 713 estimates, and showed poor accuracy, reliability, and discrimination in predicting the outcome of in-hospital CPR. The mean predicted probability of survival to discharge did not differ between patients who actually survived to discharge and those who did not (29.5% vs 26.4%,z=0.35,p=.73). Similarly, the mean predicted probabilities of surviving resuscitation were the same for patients who actually survived long enough to be stabilized and those who did not (37.8% vs 39.9%,z=0.55,p=.58). Accounting for type of physician and institution by analysis of variance did not change this finding. The area under the ROC curve for the prediction of arrest survival was 0.476, which is not significantly different from 0.5, and is consistent with an ability to discriminate between survivors and nonsurvivors that is no better than random choice.

CONCLUSIONS: Physicians were no better at identifying patients who. would survive resuscitation than would be expected by chance alone. Further work is needed to establish which variables are used by physicians in the decision-making process, and to design educational interventions that will make physicians more accurate prognosticators.

Key words

Resuscitation prediction receiver operating characteristic curve cardiopulmonary resuscitation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Murphy DJ, Burrows D, Santilli S, et al. The influence of the probability of survival on patients’ preferences regarding cardiopulmonary resuscitation. N Engl J Med. 1994;330:545–9.PubMedCrossRefGoogle Scholar
  2. 2.
    George AL, Folk BP, Crecelius PL, Campbell WB. Pre-arrest morbidity and other correlates of survival after in-hospital cardiopulmonary arrest. Am J Med. 1989;87:28–34.PubMedCrossRefGoogle Scholar
  3. 3.
    Marwick TH, Case CC, Siskind V, Woodhouse SP. Prediction of survival from resuscitation: a prognostic index derived from multivariate logistic model analysis. Resuscitation. 1991;22:129–37.PubMedCrossRefGoogle Scholar
  4. 4.
    Ebell MH. Pre-arrest predictors of survival following in-hospital cardiopulmonary resuscitation: a meta-analysis. J Fam Pract. 1992;34:551–8.PubMedGoogle Scholar
  5. 5.
    Hilden J, Habbema JDF, Bjerregaard B. The measurement of performance in probabilistic diagnosis. Trustworthiness of the exact values of the diagnostic probabilities. Methods Inf Med. 1978;227–37.Google Scholar
  6. 6.
    Redelmeier DA, Block DA, Hickam DH. Assessing predictive accuracy: how to compare Brier scores. J Clin Epidemiol. 1991;44:1141–6.PubMedCrossRefGoogle Scholar
  7. 7.
    Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148:839–43.PubMedGoogle Scholar
  8. 8.
    Woolson RF. Statistical Methods for the Analysis of Biomedical Data. New York: John Wiley and Sons, 1987.Google Scholar
  9. 9.
    Hartley RM, Charlton JR. Jarman B, Harris CM. Case history questionnaires in the study of doctors’ use of resources: are they measuring what we want? Med Care. 1985;23:1163–70.PubMedCrossRefGoogle Scholar
  10. 10.
    Morrell DC, Roland MO. Analysis of referral behaviour: responses to simulated case histories may not reflect real clinical behaviour. Br J Gen Pract. 1990;40:182–5.PubMedGoogle Scholar
  11. 11.
    Howie JGR. Further observations on diagnosis and management of general practice respiratory illness using simulated patient consultations. BMJ. 1974;276:540–3.Google Scholar
  12. 12.
    Chaput de Saintonge DM, Hathaway NR. Antibiotic use in otitis media: patient simulations as an aid to audit. BMJ. 1981;283:883–4.PubMedCrossRefGoogle Scholar
  13. 13.
    Rethans JJ, van Boven CP. Simulated patients in general practice: a different look at the consultation. BMJ. 1987;294:809–12.PubMedGoogle Scholar
  14. 14.
    Bobbio M, Detrano R, Shandling AH, et al. Clinical assessment of the probability of coronary artery disease: judgemental bias from personal knowledge. Med Decis Making. 1992;12:197–203.PubMedCrossRefGoogle Scholar
  15. 15.
    Sandvik H. Criterion validity of responses to patient vignettes: an analysis based on management of female urinary incontinence. Fam Med. 1995;27:388–92.PubMedGoogle Scholar
  16. 16.
    Green LA, Yates JF. Influence of pseudodiagnostic information on the evaluation of ischemic cardiac disease. Ann Emerg Med. 1995;25:241–7.CrossRefGoogle Scholar
  17. 17.
    Rozenbaum EA, Shenkman L. Predicting outcome of in-hospital cardiopulmonary resuscitation. Crit Care Med. 1988;16:583–6.PubMedCrossRefGoogle Scholar
  18. 18.
    Arena FP. Perlin M, Turner AD. Initial experience with a “code-no code” resuscitation system in cancer patients. Crit Care Med. 1980;8:733–5.PubMedCrossRefGoogle Scholar
  19. 19.
    Tortolani AJ, Risucci DA, Rosati RJ, Dixon R. In-hospital cardiopulmonary resuscitation: patient, arrest and resuscitation factors associated with survival. Resuscitation. 1990;20:115–28.PubMedCrossRefGoogle Scholar
  20. 20.
    Keatinge RM. Exclusion from resuscitation. J R Soc Med. 1989;82:402–5.PubMedGoogle Scholar
  21. 21.
    Bedell SE, Delbanco TL, Cook EF, Epstein FH. Survival after cardiopulmonary resuscitation in the hospital. N Engl J Med. 1983;309:569–75.PubMedCrossRefGoogle Scholar
  22. 22.
    Murphy DJ, Murray AM, Robinson BE, Campion EW. Outcomes of cardiopulmonary resuscitation in the elderly. Ann Intern Med. 1989;111:199–205.PubMedGoogle Scholar
  23. 23.
    Schneider AP, Nelson DJ, Brown DD. In-hospital cardiopulmonary resuscitation: a 30-year review. J Am Board Fam Pract. 1993;6:91–101.PubMedGoogle Scholar

Copyright information

© Society of General Internal Medicine 1996

Authors and Affiliations

  • Mark H. Ebell
    • 1
  • George R. Bergus
    • 4
  • Lawrence Warbasse
    • 2
  • Roger Bloomer
    • 3
  1. 1.Department of Family MedicineWayne State UniversityDetroit
  2. 2.Department of Internal MedicineWayne State UniversityDetroit
  3. 3.School of MedicineWayne State UniversityDetroit
  4. 4.the Department of Family MedicineUniversity of IowaUSA

Personalised recommendations