Diagnostic test

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This article is about medical tests for diagnostics. For diagnostic tests in education, see Formative assessment.

In medicine, a diagnostic test is any kind of medical test performed to aid in the diagnosis or detection of disease. For example, such a test may be used to confirm that a person is free from disease, or to fully diagnose a disease, including to sub-classify it regarding severity and susceptibility to treatment. Companion diagnostics have also been developed to preselect patients for specific treatments based on their own biology, where such targeted therapy may hold promise in personalized treatment of diseases such as cancer.[1]

A drug test can be a specific medical test to ascertain the presence of a certain drug in the body (for example, in drug addicts).

Overview[edit]

Most diagnostic tests are conducted on the living; however, some of these tests can also be carried out on a dead person as part of an autopsy. Some of the diagnostic tests are parts of a simple physical examination which require only simple tools in the hands of a skilled practitioner, and can be performed in an office environment. Some other tests require elaborate equipment used by medical technologists or the use of a sterile operating theatre environment. Some tests require samples of tissue or body fluids to be sent off to a pathology lab for further analysis. Some simple chemical tests, such as urine pH, can be measured directly in the doctor's office.

The validity of diagnostic test results produced in each laboratory is entirely dependent on the measures employed before, during, and after each assay. Consistency in the production of good results requires an overall program that includes quality assurance, quality control, and quality assessment.

Medical tests can be classified into three categories:

Psychological effects[edit]

Medical tests can have value when results are abnormal by explaining to a patient the cause of their symptoms.[2][non-primary source needed][better source needed] In addition, normal test results can have value by reassuring patients that serious illness is not present and even reduce the rates of subsequent symptoms.[3] Understanding the meaning of a normal test in advance of learning the test results may also reduce the rates of subsequent symptoms.[4][non-primary source needed][better source needed]

Lack of adequate education about the meaning of test results (especially relevant to tests that may have incidental and unimportant findings) may cause an increase in symptoms.[5][non-primary source needed][better source needed] In addition, the possible benefits must be weighed against the costs of unnecessary tests and resulting unnecessary follow-up and possibly even unnecessary treatment of incidental findings.[6][non-primary source needed][better source needed]

Interpretation[edit]

Further information: Medical test

The aim of a diagnostic test is to have an answer whether a condition is present or not in the test target, or at least contributing in estimating a post-test probability of it.

Interpretation of diagnostic tests should always take sources of inaccuracy and imprecision into account. Sources of inaccuracy and imprecision of diagnostic tests may broadly be categorized as:

  • Physical sources within the diagnostic test taking itself
  • Interpretational sources of the resultant data in relation to the target condition. Such sources include conversion of continuous values to binary ones (creating artificially binary values), such as designating a blood test for prostate specific antigen as "positive" when having reached a certain cutoff value, which is generally less accurate than considering the value itself.

Proper evaluation of a diagnostic test involves the use of statistical analysis. In this context the test is referred to as a classification rule for a binary classifier. The test is then compared to a gold standard test to assess the quality. Common methods for evaluation involve the receiver operating characteristic, Matthews correlation coefficient or F1 score but no metric is without bias.[7][non-primary source needed]

Factors determining clinical utility[edit]

The clinical utility of a diagnostic test is its capacity to rule diagnosis in and/or out and to make a decision possible to adopt or to reject a therapeutic action. It[clarification needed] can be integrated into clinical prediction rules for specific diseases or outcomes.

The following are factors determining the utility of a diagnostic test:[citation needed]

  1. Association between test results and disease is a must.
  2. The pre-test probability of a disease
  3. The demand of the testee in regard to the post-test probability of disease according with the given test result in order to rule in or out the disease and to accept or reject a particular therapeutic action.

With regard to 1. In the case of no association the post-test probability of disease is independent of the positivity or negativity of the test result and is always equal to the pre-test probability. In other words, the test result does not change the degree of (un)certainty of presence or absence of the target disease: the test is useless. If association occurs the degree of post test probability of disease increases with positive test results and decreases with negative ones. Association increases the degree of certainty by which the hypothesis of the disease can be adopted given a positive test result and by which the hypothesis can be rejected given a negative test result.

With regard to 2. The pre-test probability of disease influences the post-test probability. Pre-test probability is the probability that a person suffers from a disease before the test is executed. A high pre-test probability will tend to allow (much) easier to confirm the hypothesis of the presence of the target disease, a low pre-test probability of disease will tend to allow (much) easier to accept the hypothesis of absence of the disease.

With regard to 3. It is the user of the test (or the testee) who determines which degree of certainty that is needed to decide to the presence or absence of the target disease and/or to take the adeaquate decisions in regard to therapy. Thus it is possible that someone is of opinion that the test result is useful while another thinks that the test (on its own or in the given combination of other test results and/or data) is useless.

Based on 2 and 3, an important conclusion can be deduced. It is not because the association between a test result and the presence or absence of the disease is weak that the test is necessarily useless since a high pre-test probability and/or a ‘low’ degree of demanded certainty by tester and/or testee can make a test with a weak relation (a modest likelihood ratio) to the target disease useful (allow to decide to the presence or absence of the target disease given a positive test result despite the weak relationship). An analogue reasoning can be made for ruling out the target disease.

On the other hand a test result on a test showing a strong association with the absence or presence of the target disease can be useless because of a ‘low’ pre-test probability and/or a too high demand for the degree of certainty. An analogue reasoning can be made for ruling out the diagnosis of the target disease.

Every test that shows an association between test results and the target disease is potentially useful. If it is not on its own thought to be useful then combination of it with other test results and/or data can potentially lead to a post-test probability that is thought to be high enough to rule the diagnosis in or low enough to rule the diagnosis out.

Tests can be useful to rule disease in or out or to rule both disease in (positive test result) and out (negative test result)

Post-test probability of disease[edit]

A formula for the calculation of the post-test probability of disease is given by:

NK = PR*LR/(PR*(LR-1)-1)

Wherein NK = post-test probability of disease and PR = pre-test probability of disease and LR = likelihood ratio.

Let PR = .1 and LR+ = 10 and our demand for certainty = 95% then the post-test probability equals 52.6% and this is far from sufficient to accept the hypothesis of the presence of the target disease. Otherwise let PR = 90% and LR+ = 3 then NK = 96.4% what suffices to accept the presence of the target disease since the degree of certainty thought to be sufficient was 95%. Although a LR+ = 10 points to a much greater association between a positive test result and the presence of the target disease than a LR+ = 3, an LR+ = 3 can suffice for ruling a disease in while it is possible that a LR+ = 10 does not suffice. If the demanded degree of certainty should have been as high as 97% then both pre-test probabilities and LR’s should not have been sufficient to rule the diagnosis in. In this example both the crucial role of the pre-test probability and the demand of the degree of certainty for the usefulness of a positive test result are illustrated.

See also[edit]

References[edit]

  1. ^ Winther H, Jorgensen JT (2010). "Drug-Diagnostic Co-Development in Cancer". Pharm Med 24 (6): 363–375. doi:10.1007/bf03256837. 
  2. ^ Ward B, Wu W, Richter J, Hackshaw B, Castell D (1987). "Long-term follow-up of symptomatic status of patients with noncardiac chest pain: is diagnosis of esophageal etiology helpful?". Am J Gastroenterol 82 (3): 215–8. PMID 3826028. 
  3. ^ Sox H, Margulies I, Sox C (1981). "Psychologically mediated effects of diagnostic tests". Ann Intern Med 95 (6): 680–5. doi:10.7326/0003-4819-95-6-680. PMID 7305144. 
  4. ^ Petrie K, Müller J, Schirmbeck F, Donkin L, Broadbent E, Ellis C, Gamble G, Rief W (2007). "Effect of providing information about normal test results on patients' reassurance: randomised controlled trial". BMJ 334 (7589): 352. doi:10.1136/bmj.39093.464190.55. PMC 1800996. PMID 17259186. 
  5. ^ Kendrick D, Fielding K, Bentley E, Kerslake R, Miller P, Pringle M (2001). "Radiography of the lumbar spine in primary care patients with low back pain: randomised controlled trial". BMJ 322 (7283): 400–5. doi:10.1136/bmj.322.7283.400. PMC 26570. PMID 11179160. 
  6. ^ Jarvik J, Hollingworth W, Martin B, Emerson S, Gray D, Overman S, Robinson D, Staiger T, Wessbecher F, Sullivan S, Kreuter W, Deyo R (2003). "Rapid magnetic resonance imaging vs radiographs for patients with low back pain: a randomized controlled trial". JAMA 289 (21): 2810–8. doi:10.1001/jama.289.21.2810. PMID 12783911. 
  7. ^ Powers, D.M.W. (February 27, 2011). "Evaluation: from precision, recall and f-measure to ROC, informedness, markedness & correlation". Journal of Machine Learning Technologies 2 (1): 37–63. 

Further reading[edit]