# [JC SERIES] Episode 4: Oslerphiles Rejoice: We're Gonna Talk DIAGNOSIS

Don't tune out yet - this is actually pretty important stuff!

**Diagnostic PICO questions**

Really, with diagnostic foreground questions we are asking: *does a particular piece of subjective or objective data (i.e. test, physical exam finding, imaging) have value in ruling IN or ruling OUT a condition?* And if so, HOW valuable is it?

JAMA RCE: https://jamanetwork.com/collections/6257/the-rational-clinical-examination

Two main approaches to diagnosis:

**Pattern recognition**- System 1 processing
- Fast, efficient and instinctive
- E.g. recognition of dermatomal rash of herpes zoster

**Probabilistic diagnostic reasoning**- System 2 processing
- Slow, analytic
- E.g. determining whether a patient with acute onset shortness of breath has pulmonary embolism vs heart failure vs pneumonia, etc

**Probabilistic Diagnostic Reasoning**

**Pre-test probability:**what is the probability that my patient has disease x prior to any workup?**Test threshold:**the pre-test probability threshold necessary for you to order a given diagnostic test to rule in or rule out disease x**Post-test probability:**what is the probability that my patient has disease x after my workup?**Treatment threshold:**the post-test (or pre-test) probability threshold necessary for you to initiate treatment for disease x

**Check out the helpful graphic to the left.**

**Diagnosis: the literature**

Sometimes we need to move to the literature to ask **HOW **useful is a given test is.

Ideally, our diagnostic tests will move us FROM our pre-test probability TO:

An extremely high post test probability (i.e. RULE IN)

OR

An extremely low post-test probability (i.e. RULE OUT)

**Study Format & Risk of Bias**

There really isn’t any ONE study design used for diagnostic tests. They are often observational, but sometimes are studied with RCTs or systematic reviews as well.

There are a number of biases we need to be on the lookout for:

**Spectrum Bias** - when a study of a diagnostic test compares florid cases of the disease with asymptomatic, healthy volunteers- Not helpful because this isn’t representative of our patient population
- This same thing happened with the carcinoembryonic antigen (CEA) testing or the urine dipstick for diagnosis of UTI

**Partial verification bias -**when a study of a diagnostic test doesn’t expose all patients to the reference or gold standard test.- Some people may be more likely to send a positive stress test for left heart catheterization, but may not send a negative stress test for LHC. This may lead to overestimation of the utility of stress testing for coronary artery disease.

**Test result blinding**- you want to make sure those who are interpreting results of a given diagnostic test are blind to the gold standard – or else it may influence how hard they look for a given condition, skewing your results

**STATS IN 60 SECONDS OR LESS (SISSOL) TOPICS**

### Type I and Type II Errors

**Type I-**false positive**Type II-**false negative

### Sensitivity and Specificity

**Sensitivity-**true positive rate- Aka power of detection
- A test that is 99% sensitive for a given condition will have a very LOW rate of false positives
- Great for ruling IN a condition

**Specificity**-true negative rate- A test that is 99% specific is going to have a very LOW rate of false negatives
- Great for ruling OUT a condition

### Likelihood ratios

- We calculate two types
- Positive likelihood ratio (for positive test results)
- Negative likelihood ratio (for negative test results)

- Used to evaluate two things:
- The utility of a particular diagnostic test
- How likely is it that my patient has disease x

- Generated from sensitivity and specificity
- You want a likelihood ratio of:
- 10 (to rule in)
- 0.1 (to rule out)

*Calculation of likelihood ratios:*

### How to convert pre- to post-test probabilities

- Check out MedCalc or the Diagnosis App (no affiliation)
- Or use the Fagan nomogram (below)

**Questions We Should All be Asking Ourselves**

- Will this change my management?
- Was the population used to study a given diagnostic test generalizable to my patient population?
- Will the patient be better off as a result of the test?