Pre test Probability and Bayesian Thinking

What is Bayesian Thinking?

It reflects the statistical model of conditional probabilities (likelihood of an event or outcome based on the occurrence of a previous event or outcome) and applies it to how we work inside medicine when forming diagnoses and use our tests. In a statistical model this is calculated from multiplying the probability of the event by the probability of the subsequent or conditional event

That means every piece of information we gather changes the probability that our hypothesis or diagnosis (when applying the principles clinically) is correct. This maybe a test result or it maybe part of your history or examination.

-   The probability of a hypothesis being true is dependent  on 2 conditions:

1.       How reasonable is it based on what we already know?

2.       How well does it fit with the new evidence

Bayes’ Theorem allows us to update our beliefs and convictions based on new pieces of information. Ie if we are trying to work out the probability of someone having cancer we can assume the percentage of the population that has cancer. However extra evidence such as smoking, family Hx and change our perception (hence the probability).

This can be applied to most of A+E (undifferentiated patients with a wide differential on the presenting complaint). As we gather our history each extra part changes with probability/perception of what is most likely the problem. Our investigations then add to this with their own sensitivity.

In the rest of this blog we will look at how this applies to the use of common ED tests like Troponin and D-dimers

This link gives more information on it an an excellent explaination

https://www.upgrad.com/blog/what-is-bayesian-thinking-introduction-and-theorem/