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Purpose of investigation: Difference between revisions
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An example of a highly sensitive test is measuring [[D-dimer]] in suspected [[venous thromboembolism]], which has a sensitivity of 95%. | An example of a highly sensitive test is measuring [[D-dimer]] in suspected [[venous thromboembolism]], which has a sensitivity of 95%. | ||
An example of a test which is | An example of a test which is less sensitive is [[chest radiography]] in suspected [[lung cancer]], which has a sensitivity of approximately 90%. | ||
The sensitivity of a test is not affected by the prevalence of the disease in the tested population. | The sensitivity of a test is not affected by the prevalence of the disease in the tested population. | ||
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The specificity of a test is not affected by the prevalence of the disease in the tested population. | The specificity of a test is not affected by the prevalence of the disease in the tested population. | ||
Unfortunately, when designing a test, there is most commonly a tradeoff between sensitivity and specificity. One cannot design a test to be perfectly sensitive, as that would produce many false positives, as such giving a low specificity, and vice-versa. | |||
=== Pre-test and post-test probability === | === Pre-test and post-test probability === | ||
The pre-test probability refers to the probability that a patient with a certain symptom or clinical finding has a certain condition ''before'' performing a test or investigation. | The pre-test probability refers to the probability that a patient with a certain symptom or clinical finding has a certain condition ''before'' performing a test or investigation. For an asymptomatic person, the pre-test probability is equal to the prevalence of the disease in the general population. For a symptomatic person, the pre-test probability is equal to the prevalence in a population of people with the condition ''and'' the previously mentioned symptom. As such, if the patient has symptoms or clinical findings, they have a higher probability of having the disease than the general population, and so the pre-test probability is higher than the prevalence. | ||
As an example, the prevalence of [[urinary tract infection]] in the general population is 11%, meaning that, if you pick a random person in the world, there is an 11% chance that they have an UTI. However, among all people with typical urinary tract infection symptoms, approximately 80% of them have urinary tract infection. As such, if a person has typical urinary tract infection symptoms, the pre-test probability of them having UTI is 80%. | As an example, the prevalence of [[urinary tract infection]] in the general population is 11%, meaning that, if you pick a random person in the world, there is an 11% chance that they have an UTI, regardless of whether they have symptoms. However, among all people with typical urinary tract infection symptoms, approximately 80% of them have urinary tract infection. As such, if a person has typical urinary tract infection symptoms, the pre-test probability of them having UTI is 80%. | ||
Likewise, the post-test probability refers to the probability that a patient with a certain symptom or clinical finding has a certain condition ''after'' performing the test or investigation. | Likewise, the post-test probability refers to the probability that a patient with a certain symptom or clinical finding has a certain condition ''after'' performing the test or investigation. Ideally, a test should increase the post-test probability to be much higher than the pre-test probability. | ||
=== Positive predictive value === | === Positive predictive value === | ||
For a given test and illness, the positive predictive value (PPV) of a test refers to the probability that a patient has the illness if they have tested positive. Intuitively, it can be difficult to understand the difference between specificity and PPV, and I've given up trying to understand why. However, it's only important to know that the positive predictive value of a test is | For a given test and illness, the positive predictive value (PPV) of a test refers to the probability that a patient has the illness if they have tested positive. Intuitively, it can be difficult to understand the difference between specificity and PPV, and I've given up trying to understand why. However, it's only important to know that the positive predictive value of a test is perhaps more important for us than sensitivity, as it tells us more about the usefulness of a test than the test's sensitivity. | ||
In a certain population, positive predictive value refers to the ratio of how many people are True Positives of those who tested positive (True Positives + False Positives). | In a certain population, positive predictive value refers to the ratio of how many people are True Positives of those who tested positive (True Positives + False Positives). | ||
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In many cases, test with high specificity have high positive predictive value as well. I can't think of any specific examples. | In many cases, test with high specificity have high positive predictive value as well. I can't think of any specific examples. | ||
However, and this is important to know, the positive predictive value of a test depends not only on the test's characteristics but also the pre-test probability of the disorder, which in turn is equal to the prevalence of the disorder (if there are no symptoms). When the pre-test probability increases, the PPV increases as well. As such, even if the test is | However, and this is important to know, the positive predictive value of a test depends not only on the test's characteristics but also the pre-test probability of the disorder, which in turn is equal to the prevalence of the disorder (if there are no symptoms). When the pre-test probability increases, the PPV increases as well, and vice-versa. As such, even if the test is excellent and has a high specificity and sensitivity, the test may have a low positive predictive value regardless if the prevalence is low (the disease is rare). | ||
=== Negative predictive value === | === Negative predictive value === | ||
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== Value to the patient == | == Value to the patient == | ||
An investigation should have some form of value for the patient (except if done in a research setting or for epidemiological purpose, in which case the investigation has value for | An investigation should have some form of value for the patient (except if done in a research setting or for epidemiological purpose, in which case the investigation has value for researchers, and in turn, future patients). The value to the patient should also weigh up for any negative consequences of the investigation. | ||
Common examples to consider: | Common examples to consider: | ||
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== Screening == | == Screening == | ||
Screening refers to using an investigation to detect a disease which has not yet caused symptoms, so-called subclinical disease. Examples include: | Screening refers to using an investigation to detect a disease which has not yet caused symptoms, so-called subclinical disease, with the aim of initiating management as early as possible, to improve the prognosis. Examples include: | ||
* Regular [[mammography]] or [[cervical cytology]] in women | * Regular [[mammography]] or [[cervical cytology]] in women | ||
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** Used to detect early [[colorectal cancer]] or bleeding [[Colonic polyps|colon polyps]] | ** Used to detect early [[colorectal cancer]] or bleeding [[Colonic polyps|colon polyps]] | ||
* Screening for [[inborn errors of metabolism]] and [[developmental dysplasia of the hip]] in newborns | * Screening for [[inborn errors of metabolism]] and [[developmental dysplasia of the hip]] in newborns | ||
* [[Non-invasive prenatal test]] (NIPT) for trisomies during pregnancy | |||
Screening is indicated if the disorder has a high mortality or morbidity, and there is treatment available. | |||
=== Screening tests === | |||
A screening test is most useful when it has high [[sensitivity]]. It would be great if a screening test also had high [[specificity]], but there is often a trade-off between sensitivity and specificity. Therefore, most screening tests have low specificity. As a result, screening tests produce few false negatives but many false positives, and many more false positives than true positives. | |||
Because many of those who have a positive screening test are false positives, positive screening test must always be confirmed by a more specific test. | |||
=== Advantages of screening === | |||
Screening may have many advantages. The main advantage, and the intention behind screening, is that treatment can be started at an earlier time, before symptoms even appear, which usually improves the prognosis considerably, and may allow for curative treatment, which may not have been an option if the disorder was not diagnosed at the asymptomatic stage. | |||
=== Disadvantages of screening === | |||
Unfortunately, screening has many disadvantages as well. | |||
=== | ==== Low specificity of tests causing worry and more testing ==== | ||
Because screening tests necessarily have low specificity, there will be many false positives. Testing positive on a screening test, especially for cancer, leads to considerable worry for a person. Considering that most people who test positive on a screening test is false positive, the positive predictive value is low as well, meaning that the chance of having the disorder when testing positive is low. This concept is very difficult for laypeople to understand. | |||
Because screening tests must be confirmed by a confirmatory test, screening for a disease always leads to more testing. In many cases, these tests are invasive, either entailing radiation exposure ([[CT]] scan) or complicated patient preparation and discomfort during the procedure ([[colonoscopy]]). In some cases, for example with NIPT testing, the confirmatory test ([[amniocentesis]] or [[chorionic villus sampling]]) is invasive and increase the risk of harm ([[abortion]] of the foetus in this case), which may lead to harm for people who are actually healthy. | |||
==== Uncertainty whether early diagnosis improves prognosis ==== | |||
It is reasonable to assume that early diagnosis always improves the prognosis, but that is not always the case. This is especially important for [[prostate cancer]], for example. Prostate cancer is relatively common in elderly, but research has shown that many who are treated for subclinical prostate cancer would never have developed symptoms of the cancer, and would rather have died peacefully, never knowing that they even had the cancer. Also important to consider that cancer treatment causes significant reduction in quality of life, which is especially unfortunate if the cancer would never have caused symptoms anyway. | |||
South Korea started screening for thyroid cancer in the late 20th century. Up until relatively recently, research showed that, while the incidince increased significantly (more cases of thyroid cancer were discovered), the mortality remained the same. In other words, screening detected more cases and lead to more people being treated for cancer, which is a burden to both the healthcare system and the individual, but screening could not demonstrate a reduction in mortality, which is arguably one of the most important goals of screening. This shows that screening can lead to significant overdiagnosis.<ref>Korea's Thyroid-Cancer “Epidemic” — Screening and Overdiagnosis | NEJM</ref> | |||
== Diagnosis == | == Diagnosis == | ||
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* [https://www.bmj.com/content/351/bmj.h5552 Explaining laboratory test results to patients: what the clinician needs to know] | * [https://www.bmj.com/content/351/bmj.h5552 Explaining laboratory test results to patients: what the clinician needs to know] | ||
== References == | |||
[[Category:Radiology]] | [[Category:Radiology]] | ||
[[Category:Laboratory Medicine]] | [[Category:Laboratory Medicine]] | ||
[[Category:Public Health]] | [[Category:Public Health]] | ||