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Purpose of investigation

Revision as of 12:16, 11 January 2024 by Nikolas (talk | contribs)

When one orders a test (laboratory, imaging, or otherwise), it's important to have a predetermined purpose for the test and not just ordering tests willy-nilly. Generally, investigations can be used for:

  • Screening (healthy or not healthy)
  • Lab diagnosis
  • Follow-up/monitoring
    • Monitoring progression/response to treatment
  • Evaluation of prognosis
    • Information regarding the likely outcome of the disease

Features of investigations

Any single test or investigation has a certain features which are important to know about, like sensitivity.

True and false positive and negative

When researching a certain test's characteristics and usefulness in diagnosing a certain disorder, one would perform the test on both healthy people and people with the disorder. A good test will be positive in most people with the disorder and negative in most people without the disorder. However, because no test is perfect, the test will be negative in some people with the disorder, and it will be positive in some people without the disorder.

After performing the test on a number of subjects, each subject will be put into one category:

  • True positives (TP) - those who have the disorder and tested positive with the test
  • False positives (FP) - those without the disorder but who tested positive anyway
  • True negatives (TN) - those without the disorder and who tested negative
  • False negatives (FN) - those with the disorder but who tested negative anyway

The best tests have as few false positives and false negatives as possible.

Sensitivity

For a given test and illness, sensitivity refers to the proportion of sick people who are tested that produce a positive test result. In terms of the above, in a certain population, sensitivity refers to the ratio of how many people are True Positives of those who have the disorder (True Positives + False Negatives).

A perfectly sensitive test is 100% sensitive, meaning that 100% of tested sick people will test positive, meaning that no sick people will test negative. Few tests are 100% sensitive, and in real-life, most test which are regarded as highly sensitive have sensitivities around 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 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.

Specificity

For a given test and illness, specificity refers to the proportion of healthy people who are tested that produce a negative test result. In terms of the above, in a certain population, specificity refers to the ratio of how many people are True Negatives of those who do not have the disorder (True Negatives + False Positives).

A perfectly specific test is 100% specific, meaning that 100% of tested healthy people will test negative, meaning that no healthy people will test positive. Few tests are 100% specific, and in real-life, most test which are regarded as highly specific have specificities around 95%.

An example of a highly specific test is measuring anti-tissue glutaminase antibodies in suspected coeliac disease, which has a specificity of 95%.

An example of a test with low specificity is measuring PSA in suspected prostate cancer, which has a specificity of 20%.

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

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, 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. Ideally, a test should increase the post-test probability to be much higher than the pre-test probability.

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 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 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, 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

For a given test and illness, the negative predictive value (NPV) of a test refers to the probability that a patient does not have the illness if they have tested negative. Like positive predictive value, the negative predictive value is important as it tells us the probability that the patient does not have the disease if they test negative.

In a certain population, negative predictive value refers to the ratio of how many people are True Negatives of those who tested negative (True Negatives + False Negatives).

In many cases, tests with high sensitivity have high negative predictive value as well. For example, unless the pre-test probability is high, a negative D-dimer has a close to 100% negative predictive value for venous thromboembolism. As such, patients with a low or medium pre-test probability for VTE who test negative for D-dimer have VTE ruled out.

As with PPV, the negative predictive value of a test depends not only on the test's characteristics but also the pre-test probability of the disorder. However, in contrast to PPV, NPV decreases as the prevalence increases. As such, even if the test is really accurate and has a high specificity and sensitivity, the test may have a low negative predictive value regardless if the prevalence is high (the disease is common).

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 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:

  • A frail elderly patient living in an institution (like a nursing home) has a positive faecal occult blood test. The physician considers to refer them to colonoscopy, but stops to consider: how will a colonoscopy be of value to the patient? In their current state, even if the colonoscopy would show colorectal cancer, the patient would not be a candidate for any anticancer surgery or chemotherapy, so even if a diagnosis is made, nothing will change for the patient (except the stress of knowing they have cancer). In addition, colonoscopy is an invasive investigation which requires strict patient preparation, which can be difficult for a frail elderly to perform or even survive.
    • If they have colorectal cancer and it grows and eventually causes intestinal obstruction, colonoscopy may be indicated for stenting the bowel, in which case the colonoscopy would have value for the patient as palliative therapy
    • One can also argue that even performing a faecal occult blood test in this case has no value for the patient, because the next step after a positive test would be a colonoscopy, a procedure which would not be of value to the patient anyway
  • A young male has had back pain for a few days. The pain is not severe, and there are no red flags for cauda equina syndrome. He wants an MRI to know what's going on, but the physician stops to consider: how will an MRI be of value to the patient? The patient may or may not have a herniated disc, but even if they do, a herniated disk without red flags isn't an indication for surgery anyway. As such, whether the MRI shows a herniated disc or not, the management will be the same (no surgery, only physical therapy and pain relief), and the MRI is therefore of no value to the patient (and it is resource-intensive)
    • On the other hand, if the patient has debilitating pain or there are red flags present, he may have cauda equina syndrome, in which case surgery is indicated. In this case, MRI has value: it determines whether they need surgery or not
  • A middle aged woman has symptoms of an upper respiratory tract infection. After taking the anamnesis and physical examination, you're certain that it's a viral infection. You reflexively want to order a CRP or leukocyte count, but you stop to consider: will the laboratory investigation be of value to the patient? You know that viral URTIs only cause mildly elevated inflammatory parametres, so that's likely what you'll find anyway. And even if the CRP is higher than you expect, you're certain enough that this is not a bacterial infection, so you won't be administering antibiotics anyway. So even after making the investigation, you'll most likely not be changing your management of this patient; managing their symptoms and encouraging rest, without antibiotics
    • On the other hand, if the patient has symptoms which make it difficult to distinguish between viral and bacterial infection clinically, a laboratory investingation is merited, as it provides additional information which can aid in the diagnosis and therefore the treatment in this case
    • Consider that many laboratories can analyse a pharyngeal swab for specific airway viruses. Would making such an investigation in this case change the management of the patient? In most cases no, as there is no specific treatment for most airway viruses anyway.

Screening

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:

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.

Early diagnosis may not always improve prognosis

It is reasonable to assume that early diagnosis always improves the prognosis, but that is not always the case. In many cases, the cancer would develop so slowly that the person would never have known of it, or possibly only developed mild symptoms. However, cancer diagnosis almost always leads to aggressive treatment, which has its own effects on quality of life.

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.[1]

Diagnosis

When an investigation is ordered for diagnosis, one should already have a list of differential diagnoses before ordering the test, and the test should be able to narrow down the list of differential diagnoses. It's important to consider the test's specificity, sensitivity, positive predictive value, and negative predictive value in this. There is no reason to perform an investigation if it doesn't help narrow doen the number of differential diagnoses.

Using test characteristics wisely

Because positive and negative predictive values depend on the prevalence of the disorder in the population, it's important to keep the prevalence of the disorder in mind.

For example, a negative D-dimer has a high negative predictive value for VTE. However, negative predictive value decreases with increasing prevalence. If the patient has a high pre-test probability, often determined by their Wells score (a scoring system used to determine the pre-test probability for DVT/PE), the patient no longer has the same pre-test probability as the general population (which is low). The

Follow-up and monitoring

Follow-up and monitoring of a disorder is also a common use of laboratory and imaging investigations. Examples include:

  • CRP or leukocyte count following antibiotic prescription to evaluate the treatment response
  • Yearly brain MRI following removal of a meningeoma, to try and catch a recurrence early, before it causes symptoms

However, it's important to keep in mind the requirement of value for the patient. In the first scenario, there is value as a lack of normalisation of inflammatory parametres in a patient treated with antibiotic for a bacterial infection may be a sign that the antibiotic is ineffective, which may require administration of a different antibiotic. In the second scenario, there is also value, as the earlier one can catch recurrence of a meningeoma, the better the prognosis after treatment. However, if the patient is in a condition where they will not receive treatment anyway (for example, if they are terminal), monitoring is not of value to the patient.

Evaluation of prognosis

In some cases, performing an investigation even though it won't change patient management can be useful if it provides information on patient prognosis. This is most common in case of patients with cancer.

External resources

References

  1. ↑ https://www.nejm.org/doi/full/10.1056/NEJMp1409841