What White Blood Cell Count Should Prompt Antibiotic Treatment in a Febrile Child? Tutorial on the Importance of Disease Likelihood to the Interpretation of Diagnostic Tests

Most diagnostic tests are not dichotomous (negative or positive) but, rather, have a range of possible results (very negative to very positive). If the pretest probability of disease is high, the test result that prompts treatment should be any value that is even mildly positive. If the pretest probability of disease is low, the test result needed to justify treatment should be very positive. Simple decision rules that fix the cutpoint separating positive from negative test results do not take into account the individual patient’s pretest probability of disease. Allowing the cutpoint to change with the pretest probability of disease increases the value of the test. This is primarily an issue when the pretest probability of disease varies widely between patients and depends on characteristics that are not measured by the test. It remains an issue for decision rules based on multiple test results if these rules fail to account for important determinants of patient-specific risk. This tutorial demonstrates how the value of a diagnostic test depends on the ability to vary the cutpoint, using as an example the white blood cell count in febrile children at risk for bacteremia.

[1]  Frans J. Th. Wackers,et al.  Use of the initial electrocardiogram to predict in-hospital complications of acute myocardial infarction. , 1985, The New England journal of medicine.

[2]  J Hilden,et al.  Regret graphs, diagnostic uncertainty and Youden's Index. , 1996, Statistics in medicine.

[3]  M. Fine,et al.  A prediction rule to identify low-risk patients with community-acquired pneumonia. , 1997, The New England journal of medicine.

[4]  A. Laupacis,et al.  Clinical prediction rules. A review and suggested modifications of methodological standards. , 1997, JAMA.

[5]  D. Schriger,et al.  Practice guideline for the management of infants and children 0 to 36 months of age with fever without source. Agency for Health Care Policy and Research. , 1993, Annals of emergency medicine.

[6]  M. Harper,et al.  Risk of bacteremia for febrile young children in the post-Haemophilus influenzae type b era. , 1998, Archives of pediatrics & adolescent medicine.

[7]  J Hilden Prevalence-free utility-respecting summary indices of diagnostic power do not exist. , 2000, Statistics in medicine.

[8]  P Doubilet,et al.  A Mathematical Approach to Interpretation and Selection of Diagnostic Tests , 1983, Medical decision making : an international journal of the Society for Medical Decision Making.

[9]  E F Cook,et al.  A computer protocol to predict myocardial infarction in emergency department patients with chest pain. , 1988, The New England journal of medicine.

[10]  Use of the initial electrocardiogram to predict in-hospital complications of acute myocardial infarction. , 1986, The New England journal of medicine.

[11]  R B D'Agostino,et al.  A predictive instrument to improve coronary-care-unit admission practices in acute ischemic heart disease. A prospective multicenter clinical trial. , 1984, The New England journal of medicine.

[12]  B. Psaty,et al.  Predictors of myocardial infarction in emergency room patients , 1985, Critical care medicine.

[13]  Inger,et al.  A prediction rule to identify low-risk patients with community-acquired pneumonia. , 1997, The New England journal of medicine.

[14]  H. Sox,et al.  Clinical prediction rules. Applications and methodological standards. , 1985, The New England journal of medicine.

[15]  G. Chapman,et al.  [Medical decision making]. , 1976, Lakartidningen.