Diagnostic test (medical)

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A diagnostic test is, as its name implies, a medical test or series of tests designed to examine a patient's signs or symptoms (what hurts, or what otherwise seems abnormal to the patient) in order to allow a medical practitioner to give a diagnosis (a conclusion) about what is wrong drawn an analysis of the patient's test results. This is the first step in deciding how to treat the ailment or disease.

Some diagnostic tests may be similar to screening tests, however they differ from the latter in that screening tests are designed to discover abnormality before any symptoms are manifested; diagnostic tests take place after the patient has noticed symptoms of abnormality, illness or disease.

Interpreting diagnostic tests

See also Bayes Theorem, sensitivity and specificity, and likelihood ratio.

Interpreting and comparing studies of diagnostic tests is difficult. Although standards exists for meta-analysis of randomized controlled trials and cohort studies[1], no guidelines exist for studies of diagnostic test accuracy. In addition, the Cochrane Collaboration has not finished their handbook of meta-analyses of diagnostic test accuracy[2] although the Cochrane Diagnostic Test Accuracy Working Group has published recommendations.[3].

Methods to compare diagnostic tests
Method Advantages Disadvantages
Simple accuracy Easy to understand Varies with prevalence of disease
Gain in Certainty[4]
(Sensitivity plus specificity)
Easy to understand
Stable with prevalence of disease
Youden's J index[5]
(Sensitivity plus specificity minus 1)
Easy to understand
Stable with prevalence of disease
Area under the receiver operating characteristic curve (AROC)[6] and variations[3] Stable with prevalence of disease
Can interpret multi-level and continuous outcome tests
Hard to understand
Diagnostic odds ratio (DOR)[7] Stable with prevalence of disease
Can be included in multivariable analyses
Hard to understand
Underestimates heterogeneity[8]
Net reclassification index[9]
Change in sensitivity plus change in specificity
Stable with prevalence of disease
Can interpret multi-level tests
Hard to understand

Non-specific benefit of tests

Medical tests can have value when results are abnormal by explaining to a patient the cause of their symptoms[10]. 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 [11].

If a normal test result is expected, understanding the meaning of a normal test in advance of learning the test results may reduce the rates of subsequent symptoms.[12][13]

Harms of tests


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[14] or anxiety[15]. This may be similar to the effects of labeling.[16]


The benefits must be weighed against the costs of resulting unnecessary follow-up and possibly even unnecessary treatment of incidental findings.[17]

Allowable costs of common tests according to the U.S. Centers for Medicare & Medicaid Services are available.[18]


For more information, see: Overdiagnosis.

Other harms

Tests that seem harmless individually, may be harmful when repeated multiple times in a patient. For example in radiology, it is estimated that computed tomography may be contributing to cancer.[19]

About 7% of abnormal results are not communicated to patients. Physicians without a complete an electronic health record tend to provide worse follow-up on abnormal diagnostic tests.[20]

Strategies to reduce unnecessary diagnostic testing

A systematic review has found that multiple interventions are needed to best improve test ordering.[21]

Improve availability of prior results

Sometimes testing is redundant.[22] Having the results of prior tests available may reduce the need for repeating tests.[23] A randomized controlled trial has shown reduction i ordering of redundant tests.[24]

Delay testing

Randomized controlled trials show benefit of immediate versus delayed testing in patients without possible emergent conditions.[14][17] The benefit may be in part due to successful empirical treatment.

Establish an alternative diagnoses

Studies show that the chance of thromboembolism is less in patients who have have alternative explanations for their symptoms.[25][26]

Patients with chronic abdominal symptoms are less likely to have underlying organic disease if they meet criteria for irritable bowel.[27][28][29]

Among patients referred for endoscopy, psychiatric diagnoses are associated with normal endoscopies.[30]

Patients with new headaches are less likely to have significant underlying pathology if their headaches meet criteria for being a migraine headache according to a systematic review by the Rational Clinical Examination.[31] The systematic review found two relevant studies:[31]

  • Among 69 patients over 40 years old with new migraines, no patients had definite significant intracranial pathology (4 patients had evidence of prior infarctions).[32]
  • Among 100 adults with new, non-specific headaches, approximately 40% had underlying pathology.[33]

Recognize futility of testing when disease prevalence is extremely low

Using Bayes Theorem may allow recognition that there are some settings where testing can be considered futile. Two conditions are necessary to establish futility:

  1. Being able to estimate the post-test probability of disease by having all the necessary information to do this: sensitivity and specificity and prevalence of disease.
  2. Evidence-based analysis of what post-test probability of disease is considered futile. For example, in the screening of HIV, decision analysis calculates that screening should occur whenever prevalence is approximately 0.2%.[34] However, this type of analysis is not available for many diseases and, when is available, usually includes value judgments about futility and cost that may not be universally accepted judgments.

Examples where thresholds are established or implied to justify testing or treatment include:

  • HIV screening - the threshold is very low.[34] Screening is recommended even if the prevalence is as low as 0.2%.[34]
  • Influenza treatment - the threshold is higher as the stakes are lower. For elderly patients, treatment should be initiated if probability of disease is 13% or more.[35] while for younger patients the threshold is 30%.[36][35]

In the absence of specific analysis, another approach to determining the appropriate threshold is to use precedent. For example, in potentially lethal diseases such as pulmonary embolism[37], acute coronary syndrome[38], and pneumonia[39][40], in the best of health care settings 2-4% of patients have their diagnosis missed.Therefore, the precedent would be that whenever a serious disease is estimated to have more than a 2%-4% prevalence, the disease should be sought.

A randomized controlled trial showed a small reduction in test ordering when a computer displayed very low probabilities that a test would be abnormal.[41]

Response to empiric treatment

Although this strategy seems sensible, there are reports of misleading responses by serious diseases to empiric treatment for chest pain[42][43] and headache[44][45]

These responses may be due to non-specific actions of the drugs used, or may be due to placebo effect.

Research studies of the accuracy of diagnostic tests

Poorly designed studies may overestimate the accuracy of a diagnostic test.[46]

The Quality Assessment of Diagnostic Accuracy Assessment (QUADAS) is an 14 item scale for assessing the quality of a diagnostic test that is used by the Cochrane Collaboration.[47][48]

Context bias

Diagnostic tests that are interpreted subjectively may be influence by the prevalence of disease.[49]

Incorporation bias or reference standard review bias

Incorporation bias or reference standard review bias occurs when interpretation of the final diagnosis is affected by knowledge of the diagnostic test.[50][47]

Spectrum bias

Spectrum bias may occur when a study includes patients with know disease and separately identified healthy subjects without subjects with intermediate probability of disease.[50][51] When two distinct groups are compared in this way, the study in effect becomes a case control study which has been shown to overestimate diagnostic test accuracy.[46]

Multivariable adjustments may counteract spectrum bias.[52]

Test review bias

Test review bias occurs when interpretation of a subjective test is done with additional knowledge of the patient. Interpreters of subjective tests should be blinded to other information about the patient. Multivariable adjustments may counteract test review bias.[52]

Verification bias

Verification bias can inflate the accuracy of test results in a study.[53]

Publication bias

Publication bias may inflate the reported accuracies of diagnostic tests.[54] Publication bias may be more of a problem in diagnostic test research than in randomized controlled trials because studies of diagnostic tests can be secondary analyses of databases and do not have to be registered prior to publication.[55]

For the detection of publication bias in meta-analysis of diagnostic tests, the effective sample size funnel plot and associated regression test of asymmetry may be used.[56]The

Standards for the conduct and reporting of studies of diagnostic tests

Standards are available (http://www.stard-statement.org/).[57][58][59]

The STARD statement is encouraged by 38% or clinical medical journals that published diagnostic tests.[60] STARD was more often encouraged by general and internal medicine scientific journals (46%) than in specialty scientific journals(35%).

The Quality Assessment of Diagnostic Accuracy Assessment (QUADAS) tool can help assess quality of studies of diagnostic tests.[47] The QUADAS-2 revision is available.[61]

Recommendations are available for reading studies of diagnostic test accuracy.[62][63]


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