ITEM 18.  Report distribution of severity of disease (define criteria) in those with the target condition; other diagnoses in participants without the target condition.

 

 

Demographic and clinical features of the study population can affect measures of diagnostic accuracy. This variability is known as spectrum bias.[1] The spectrum effect includes the severity of the target condition, demographic features, and co morbidity. All of these elements have caused variability in measures of test accuracy, but most notable examples involved differences in the severity of the target condition.[2-7]
Many target conditions are not pure dichotomous states but cover a continuum, ranging from minute pathological changes to advanced clinical disease. Test sensitivity is often higher in studies with a higher proportion of patients with more advanced stages of the target condition.[1] On the other hand, in the presence of co morbid conditions, false-positive or false-negative test results may occur more often.[8-10]
Accordingly, it is important to describe the severity of disease in the study group.

 

 

References

1. Ransohoff DF, Feinstein AR. Problems of spectrum and bias in evaluating the efficacy of diagnostic tests. N Engl J Med 1978; 299:926-30.
2. Fletcher RH. Carcinoembryonic antigen. Ann Intern Med 1986; 104:66-73.
3. Lachs MS, Nachamkin I, Edelstein PH, Goldman J, Feinstein AR, Schwartz JS. Spectrum bias in the evaluation of diagnostic tests: lessons from the rapid dipstick test for urinary tract infection. Ann Intern Med 1992; 117:135-40.
4. O'Connor PW, Tansay CM, Detsky AS, Mushlin AI, Kucharczyk W. The effect of spectrum bias on the utility of magnetic resonance imaging and evoked potentials in the diagnosis of suspected multiple sclerosis. Neurology 1996; 47:140-4.
5. Moons KG, van Es GA, Deckers JW, Habbema JD, Grobbee DE. Limitations of sensitivity, specificity, likelihood ratio, and bayes' theorem in assessing diagnostic probabilities: a clinical example. Epidemiology 1997; 8:12-7.
6. Hlatky MA, Pryor DB, Harrell FE, Jr., Califf RM, Mark DB, Rosati RA. Factors affecting sensitivity and specificity of exercise electrocardiography. Multivariable analysis. Am J Med 1984; 77:64-71.
7. Harris JM, Jr. The hazards of bedside Bayes. JAMA 1981; 246:2602-5.
8. Stein PD, Gottschalk A, Henry JW, Shivkumar K. Stratification of patients according to prior cardiopulmonary disease and probability assessment based on the number of mismatched segmental equivalent perfusion defects. Approaches to strengthen the diagnostic value of ventilation/perfusion lung scans in acute pulmonary embolism. Chest 1993; 104:1461-7.
9. Knottnerus JA, Knipschild PG, Sturmans F. Symptoms and selection bias: the influence of selection towards specialist care on the relationship between symptoms and diagnoses. Theor Med 1989; 10:67-81.
10. Philbrick JT, Horwitz RI, Feinstein AR, Langou RA, Chandler JP. The limited spectrum of patients studied in exercise test research. Analyzing the tip of the iceberg. JAMA 1982; 248:2467-70.