Every assessment is followed by the clinical diagnosis without which targeted treatment is not possible. In many cases in ordinary clinical practice, the diagnosis is simple and certain. For example, a tooth can be missing or present clinically. The diagnosis "missing tooth" is therefore obvious.
However, a clear distinction between healthy ("tooth is present") and diseased ("tooth is missing") is not possible in every case. For instance, it is more difficult to answer the question of whether a patient is suffering from periodontitis when there is an attachment loss of 4 mm on a single tooth.
The effects of wrong diagnoses can have serious consequences for the patient. Either healthy patients are treated or ill patients are not treated.
To make a secure diagnosis, the greatest possible number of findings is collected to obtain a broad basis for coming to a decision. These findings must be assessed depending on whether they are normal or abnormal.
Whether something is abnormal or "unusual" often depends on statistically defined thresholds. Another possibility is to add clinical observations that suggest that the risk of the disease increases above a specific threshold (e.g., pocket probing depth > 3 mm).
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Principles of diagnostic tests
Different diagnostic tests are used to increase the probability of making a valid diagnosis.
In dentistry, the diagnosis of periodontal disease is made by a combination of clinical and radiological findings such as bleeding on probing, pocket probing depth, attachment loss and bone resorption. Because periodontitis is a chronic infectious disease, other microbiological tests can be used to identify pathogenic bacterial species. In addition, immunological or biochemical tests can be performed to measure how the body reacts to the disease.
To be able to judge the validity of such test methods, it is important to understand fundamental principles of diagnostic tests.
When a diagnostic test for a disease or condition gives a positive result, this result can be right (true positive) or wrong (false positive). If the result of the test is negative, this result can be either right (true negative) or wrong (false negative).
The ability of a test to provide a correct result is described with the terms sensitivity and specificity. Sensitivity and specificity are an attempt to quantify the diagnostic ability of a test (Altman and Bland 1994, p. 1552).
The percentage of test-positive persons among all the persons of a sample with the disease, i.e., the probability of identifying the patients as ill with a diagnostic test. High sensitivity is desirable if a disease is to be excluded with a high degree of certainty (Deutsches Netzwerk Evidenzbasierte Medizin e.V.).
The percentage of test-negative persons among all the persons of a sample without the disease, i.e., the probability of correctly identifying patients without disease with a diagnostic test. High specificity is desirable if a disease is to be confirmed with a high degree of certainty (Deutsches Netzwerk Evidenzbasierte Medizin e.V.).
In diagnostics, predictive values are positive (PPV) and negative (NPV). Both values depend on the one hand on the sensitivity and specificity of the diagnostic procedure and on the other hand on the prevalence of the disease in the investigated group (Deutsches Netzwerk Evidenzbasierte Medizin e.V.).
Positive predictive value
Percentage of persons with a positive test result in whom the sought disease is actually present. This value depends on the prevalence of the disease in the investigated group (Deutsches Netzwerk Evidenzbasierte Medizin e.V.).
Negative predictive value
Percentage of persons with a negative test result in whom the sought disease is actually not present. This value depends on the prevalence of the disease in the investigated group (Deutsches Netzwerk Evidenzbasierte Medizin e.V.).
The two-by-two table
The two-by-two table helps to determine the sensitivity and specificity of a clinical test. Moreover, the predictive values can be calculated from it. A two-by-two table consists of 2x2 fields that are filled with the test results as absolute frequencies. These absolute frequencies result from a cross-classification of two binary features, X and Y. A binary feature is a variable with only two parameters (for example, disease yes/no, treatment yes/no, success yes/no) (Bender 2001, p. 116)
. Stated simply, a test result is classed as positive when persons who have the disease according to the test really have the disease (e.g., as detected by the gold standard) and when persons who do not have the disease according to the test really do not have the disease. These values are also obtained for test persons in whom the test result is negative. This results in the following table:
||A (true positive)
||B (false positive)
||C (false negative)
||D (true negative)
The following values can be read from the two-by-two table: sensitivity: A / (A+C)
specificity: D / (B+D), positive predictive value: A / (A+B), negative predictive value D / (C+D).
- Altman, D. G.; Bland, J. M. (1994): Diagnostic tests. 1: Sensitivity and specificity. In: BMJ (Clinical research ed.) 308 (6943), S. 1552
- Bender, Ralf (2001): Interpretation von Effizienzmaßen der Vierfeldertafel für Diagnostik und Behandlung. In: Medizinische Klinik 96 (2), S. 116–121. DOI: 10.1007/PL00002179
- Deutsches Netzwerk Evidenzbasierte Medizin e.V. (Hg.): Glossar zur Evidenzbasierten Medizin. Online verfügbar unter >>>