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8 - Multiple tests and multivariable decision rules

Published online by Cambridge University Press:  04 August 2010

Thomas B. Newman  and
Michael A. Kohn
Thomas B. Newman
Affiliation:
University of California, San Francisco
Michael A. Kohn
Affiliation:
University of California, San Francisco
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Summary

Introduction

In Chapter 3, when we introduced dichotomous tests, the LR of a positive result [LR(+)], and the LR of a negative result [LR(−)], we also made a point of distinguishing between prevalence and prior probability. Recall that prior probability is the more general term. The prior probability is equal to the prevalence of the disease in the population only when we do not know anything else about the patient. This is often the case for screening tests applied to large populations without obtaining information on individuals that allows differentiation between them. Although we tend to focus on laboratory or imaging tests, any new information about the patient can be used to update the prior probability of disease from what is known about the prevalence of disease in the population. As soon as we obtain individual-level information by taking a history and doing a physical examination, we develop a different estimate for the prior probability than the prevalence of the disease.

In this chapter, we discuss combining multiple types of information – elements of history, findings on physical examination, laboratory results, or radiographic images. We cover (at least theoretically) how we might get from prevalence to prior probability based on the history and physical examination, and then to posterior probability based on additional information from diagnostic tests. We begin by reviewing the concept of test independence, and then we discuss how to deal with departures from independence, which are probably the rule rather than the exception.

Type
Chapter
Information
Evidence-Based Diagnosis , pp. 156 - 185
Publisher: Cambridge University Press
Print publication year: 2009

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References

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Fine, M. J., Auble, T. E, et al. (1997). “A prediction rule to identify low-risk patients with community-acquired pneumonia [see comments].” N Engl J Med 336(4): 243–50.CrossRefGoogle Scholar
Goldman, L., Cook, E. F, et al. (1988). “A computer protocol to predict myocardial infarction in emergency department patients with chest pain.” N Engl J Med 318(13): 797–803.CrossRefGoogle ScholarPubMed
Hoffman, J. R., Wolfson, A. B., et al. (1998). “Selective cervical spine radiography in blunt trauma: methodology of the National Emergency X-Radiography Utilization Study (NEXUS) [see comments].” Ann Emerg Med 32(4): 461–9.CrossRefGoogle Scholar
Hoffman, J. R., Mower, W. R., et al. (2000). “Validity of a set of clinical criteria to rule out injury to the cervical spine in patients with blunt trauma. National Emergency X-Radiography Utilization Study Group.” N Engl J Med 343(2): 94–9.CrossRefGoogle ScholarPubMed
Katz, M. H. (1999). Multivariable Analysis: A Practical Guide for Clinicians. Cambridge, Cambridge University Press.Google Scholar
Laupacis, A., Sekar, N., et al. (1997). “Clinical prediction rules. A review and suggested modifications of methodological standards.” JAMA 277(6): 488–94.CrossRefGoogle ScholarPubMed
Lee, T. H., Juarez, G., et al. (1991). “Ruling out acute myocardial infarction. A prospective multicenter validation of a 12-hour strategy for patients at low risk.” N Engl J Med 324(18): 1239–46.CrossRefGoogle ScholarPubMed
Leroy, S., Marc, E., et al. (2006). “Prediction of vesicoureteral reflux after a first febrile urinary tract infection in children: validation of a clinical decision rule.” Arch Dis Child 91(3): 241–4.CrossRefGoogle ScholarPubMed
Newman, T. B., Bernzweig, J. A., et al. (2002). “Urine testing and urinary tract infections in febrile infants seen in office settings: the Pediatric Research in Office Settings' Febrile Infant Study.” Arch Pediatr Adolesc Med 156(1): 44–54.CrossRefGoogle ScholarPubMed
Nicolaides, K. H. (2004). The 11–13+6 Weeks Scan. London, Fetal Medicine Foundation.Google Scholar
Oostenbrink, R., Heijden, A. J., et al. (2000). “Prediction of vesico-ureteric reflux in childhood urinary tract infection: a multivariate approach.” Acta Paediatr 89(7): 806–10.CrossRefGoogle ScholarPubMed
Pantell, R. H., Newman, T. B., et al. (2004). “Management and outcomes of care of fever in early infancy.” JAMA 291(10): 1203–12.CrossRefGoogle ScholarPubMed
Quinn, J. V., Stiell, I. G., et al. (2004). “Derivation of the San Francisco Syncope Rule to predict patients with short-term serious outcomes.” Ann Emerg Med 43(2): 224–32.CrossRefGoogle ScholarPubMed
Selker, H. P., Griffith, J. L., et al. (1991). “A tool for judging coronary care unit admission appropriateness, valid for both real-time and retrospective use. A time-insensitive predictive instrument (TIPI) for acute cardiac ischemia: a multicenter study.” Med Care 29(7): 610–27. [For corrected coefficients, see http://medg.lcs.mit.edu/cardiac/tipicoef.htm.]CrossRefGoogle ScholarPubMed
Selker, H. P., Beshansky, J. R., et al. (1998). “Use of the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia. A multicenter, controlled clinical trial.” Ann Intern Med 129(11): 845–55.CrossRefGoogle ScholarPubMed
Snijders, R. J., Sundberg, K., et al. (1999). “Maternal age- and gestation-specific risk for trisomy 21.” Ultrasound Obstet Gynecol 13(3): 167–70.CrossRefGoogle ScholarPubMed
Stiell, I. G., McKnight, R. D., et al. (1994). “Implementation of the Ottawa ankle rules.” JAMA 271(11): 827–32.CrossRefGoogle ScholarPubMed
Wasson, J. H., Sox, H. C, et al. (1985). “Clinical prediction rules. Applications and methodological standards.” N Engl J Med 313(13): 793–9.CrossRefGoogle ScholarPubMed

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