Nonetheless, sensitivity and specificity can vary greatly depending on the spectrum of the disease in the studied group. Neither sensitivity nor specificity are not influenced by the disease prevalence, meaning that results from one study could easily be transferred to some other setting with a different prevalence of the disease in the population. Therefore, we can postulate that specificity relates to the aspect of diagnostic accuracy that describes the test ability to recognise subjects without the disease, i.e. In other words, specificity represents the probability of a negative test result in a subject without the disease (T-|B-). It is defined as a proportion of subjects without the disease with negative test result in total of subjects without disease (TN/TN+FP). Specificity is a measure of a diagnostic test accuracy, complementary to sensitivity. Hence, it relates to the potential of a test to recognise subjects with the disease. Actually, sensitivity is defined as the probability of getting a positive test result in subjects with the disease (T+|B+). Sensitivity is expressed in percentage and defines the proportion of true positive subjects with the disease in a total group of subjects with the disease (TP/TP+FN). Therefore, the cut-off divides the population of examined subjects with and without disease in four subgroups considering parameter values of interest: Those values are false negative values (FN). However, some subjects with the disease can have them too. On the other hand, values below the cut-off are mainly found in subjects without disease. Such elevated values of certain parameter of interest are called false positive values (FP). Values above the cut-off are not always indicative of a disease since subjects without disease can also sometimes have elevated values. Unfortunately, such perfect test does not exist in real life and therefore diagnostic procedures can make only partial distinction between subjects with and without disease. Values of a perfect test which are above the cut-off are always indicating the disease, while the values below the cut-off are always excluding the disease. This brief review outlines some basic definitions and characteristics of the measures of diagnostic accuracy.Ī perfect diagnostic procedure has the potential to completely discriminate subjects with and without disease. Such efforts could make a substantial difference in the quality of reporting of studies of diagnostic accuracy and serve to provide the best possible evidence to the best for the patient care. STARD statement should be included into the Instructions to authors by scientific journals and authors should be encouraged to use the checklist whenever reporting their studies on diagnostic accuracy. STARD initiative was a very important step toward the improvement the quality of reporting of studies of diagnostic accuracy. Studies not meeting strict methodological standards usually over- or under-estimate the indicators of test performance as well as they limit the applicability of the results of the study. Furthermore, measures of diagnostic accuracy are extremely sensitive to the design of the study. Measures of diagnostic accuracy are not fixed indicators of a test performance, some are very sensitive to the disease prevalence, while others to the spectrum and definition of the disease. Different measures of diagnostic accuracy relate to the different aspects of diagnostic procedure: while some measures are used to assess the discriminative property of the test, others are used to assess its predictive ability. This discriminative potential can be quantified by the measures of diagnostic accuracy such as sensitivity and specificity, predictive values, likelihood ratios, the area under the ROC curve, Youden's index and diagnostic odds ratio.

Diagnostic accuracy relates to the ability of a test to discriminate between the target condition and health.

Synonym tidiness