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Clinical Prediction Rules

EM FINAL EXAMS Critical Appraisal · Prediction Clinical Prediction Rules Tools that combine several clinical variables to estimate the probability of a diagnosis or outcome and guide a decision. Definition A clinical prediction rule (CPR) combines findings from history, examination and simple tests into a score that estimates the probability of a diagnosis or outcome […]

EM FINAL EXAMS Critical Appraisal · Prediction

Clinical Prediction Rules

Tools that combine several clinical variables to estimate the probability of a diagnosis or outcome and guide a decision.

Definition

A clinical prediction rule (CPR) combines findings from history, examination and simple tests into a score that estimates the probability of a diagnosis or outcome — e.g. Wells, the Ottawa rules, HEART, PERC, CURB-65. A rule must be derived, then externally validated, and ideally impact-analysed, before clinical use. Appraise its discrimination (AUC) and its calibration.

The picture
useless rule (AUC 0.5) 0 0.5 1 0 0.5 1 chosen cut-off (triage threshold) AUC = 0.85 ↖ top-left = better 1 − Specificity (false positive rate) Sensitivity (true positive rate) moving the cut-off trades sensitivity ↔ specificity

A good rule discriminates → AUC 0.85; the gold dot is the score cut-off you actually triage on

What it shows

How well a rule separates patients who will have the outcome from those who won’t, across every possible score cut-off. The further the curve bows to the top-left, the better the discrimination (a higher AUC); the dashed diagonal is a coin toss. The gold dot is the single cut-off the rule recommends for action — for example the HEART ≤3 “low-risk” threshold.

How to read it

Read the whole curve (AUC) for overall discrimination, then read the chosen cut-off for the sensitivity and specificity you’d actually get when you apply the rule. But discrimination is only half the story: also ask whether the rule was externally validated in patients like yours, and whether its predicted risks are calibrated — a curve drawn from derivation data flatters the rule.

Why it matters

CPRs standardise decisions and can safely cut investigation and admission — but only within the population and setting where they were validated. A rule is a decision aid, not a replacement for judgement: it estimates probability, it does not examine the patient in front of you.

Key
  • Lifecycle: derive → validate (external) → impact study
  • Appraise both discrimination (AUC) and calibration
  • A derivation-only rule overfits and overperforms
Pitfall
Pitfall Using a rule outside its validated population or setting, or before external validation. Derivation-only rules overfit their own data and overperform; applied to a different case-mix they can miss the very patients they were meant to catch.
emfinalexams.com · FRCEM / MRCEM revision
EM trial in the wild

HEART score — the model often held up as doing it properly. After derivation (Six et al., Neth Heart J 2008) it was prospectively externally validated by Backus et al. in 2,440 ED chest-pain patients across 10 hospitals (Int J Cardiol 2013): c-statistic 0.83 for 6-week MACE, beating TIMI (0.75) and GRACE (0.70), with a HEART ≤3 “low-risk” group running ~1.7% MACE. It was then taken further with impact studies (e.g. the HEART Pathway / HEART-Impact RCTs). A rule earns ED use through this whole chain — derive, externally validate, then impact-analyse — not on a single impressive derivation AUC.

Examiner traps
  • Adopting a rule after derivation only, with no external validation.
  • Applying a rule to a different population or spectrum than it was validated in.
  • Treating the rule as a replacement for clinical judgement rather than a decision aid.
Quick check

A newly-derived rule reports excellent performance in its development sample — can you adopt it straight into practice?
Answer: No — it needs external validation in a separate population (and ideally an impact study) first. Derivation performance is optimistic because the rule was fitted to that exact data.

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