This was a meta-analysis including 22 studies (n=12089) in adult patients presenting with blunt trauma, evaluating the accuracy of the FAST examination for the detection of intra-abdominal injury. All included studies had at least 1 reference standard including abdominal computed tomography, diagnostic peritoneal lavage, laparotomy, autopsy, and/or clinical course. 5 of the studies excluded patients with hemodynamic instability. When these studies were looked at alone, the positive LR increased to 82.
Note: accuracy of ultrasound is operator-dependent. Reported LRs may not be reproducible by an inexperienced sonographer.
Published in collaboration with The POCUS Atlas
Author Matthew Riscinti, MD
Published/Updated September 13, 2018
What are Likelihood Ratios?
LR, pretest probability and posttest (or posterior) probability are daunting terms that describe simple concepts that we all intuitively understand.
Let's start with pretest probability: that's just a fancy term for my initial impression, before we perform whatever test it is that we're going to use.
For example, a patient with prior stents comes in sweating and clutching his chest in agony, I have a pretty high suspicion that he's having an MI – let's say, 60%. That is my pretest probability.
He immediately gets an ECG (known here as the "test") showing an obvious STEMI.
Now, I know there are some STEMI mimics, so I'm not quite 100%, but based on my experience I'm 99.5% sure that he's having an MI right now. This is my posttest probability - the new impression I have that the patient has the disease after we did our test.
And likelihood ration? That's just the name for the statistical tool that converted the pretest probability to the posttest probability - it's just a mathematical description of the strength of that test.
Using an online calculator, that means the LR+ that got me from 60% to 99.5% is 145, which is about as high an LR you can get (and the actual LR for an emergency physician who thinks an ECG shows an obvious STEMI).
(Thank you to Seth Trueger, MD for this explanation!)