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Critical Appraisal

Pre- and Post-test Probability & Odds

EM FINAL EXAMS Critical Appraisal · Diagnostics Pre- and Post-test Probability & Odds The chance of disease before a test, updated by the result into the chance after it. Definition The pre-test probability is the chance of disease before testing — roughly the prevalence in your population tempered by clinical judgement. Apply the test result’s […]

EM FINAL EXAMS Critical Appraisal · Diagnostics

Pre- and Post-test Probability & Odds

The chance of disease before a test, updated by the result into the chance after it.

Definition

The pre-test probability is the chance of disease before testing — roughly the prevalence in your population tempered by clinical judgement. Apply the test result’s likelihood ratio to update it to the post-test probability (Bayesian reasoning). Probability and odds interconvert — odds = p ÷ (1 − p) — and formally post-test odds = pre-test odds × LR. The Fagan nomogram does this graphically.

The picture
Draw a line: pre-test → through LR → read post-test Pre-test % LR Post-test % 0.1 1 5 10 20 40 60 90 99 1000 100 10 1 0.1 0.01 0.001 99 90 70 50 20 5 1 0.1 20% LR 10 ~70%

Pre-test 20% → through LR+ 10 → post-test ≈ 70%

What it shows

The same positive result moves different starting points to different finishing points. Here a 20% pre-test probability, pivoted through an LR+ of 10, lands near 70%. Anchor a lower pre-test value and the same LR lands far lower — the result has not changed; the starting odds have.

How to read it

Fix the pre-test probability on the left axis, pivot the straight line through the result’s LR in the middle, and read the post-test probability on the right. Behind the picture is pure arithmetic: convert the pre-test probability to odds, multiply by the LR, convert the resulting post-test odds back to a probability.

Why it matters

Test results are never interpreted in a vacuum — only against where the patient started. Quantifying the pre-test probability and updating it is the formal version of “clinical gestalt”, and it is the reason the same result rules disease in for one patient yet barely shifts another.

Key
  • odds = p ÷ (1 − p) · p = odds ÷ (1 + odds)
  • post-test odds = pre-test odds × LR
  • Pre-test probability ≈ prevalence / clinical judgement
Pitfall
Pitfall Ignoring the pre-test probability. The same positive result shifts a low-risk and a high-risk patient to very different post-test probabilities — a “positive” test in someone with near-zero prior is far more likely a false positive than true disease.
emfinalexams.com · FRCEM / MRCEM revision
EM trial in the wild

Wells score + D-dimer for PE — the same D-dimer is interpreted against the pre-test probability the score sets. In a “PE-unlikely” patient (Wells ≤4, low pre-test probability) a negative D-dimer drops the post-test probability of PE below roughly 2% and safely rules it out; in a “PE-likely” patient the identical negative D-dimer leaves too much residual risk, so CTPA is still required (Wells et al., Ann Intern Med 2001; Christopher Study, JAMA 2006). One result, two destinations: D-dimer only rules out PE when the pre-test probability is already low — apply it at a high pre-test probability and a negative result is falsely reassuring.

Examiner traps
  • Base-rate neglect — judging a result without anchoring the pre-test probability.
  • Confusing odds with probability (they only coincide when both are small).
  • Using a test outside the pre-test probability range it was validated in.
Quick check

The same positive result in a low-risk and a high-risk patient — same post-test probability?
Answer: No — it depends on the pre-test probability. The LR shifts the odds by the same factor, but from different starting points it lands at very different post-test probabilities.

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