Validation of NICE diagnostic guidance for rule out of myocardial infarction using high-sensitivity troponin tests

W A Parsonage1, C Mueller2, J H Greenslade1

  • 1Royal Brisbane & Women's Hospital, Herston, Australia.

Insights

The National Institute for Health and Care Excellence (NICE) algorithms for high-sensitivity troponin (hsTn) assays can identify low-risk acute myocardial infarction (AMI) patients but require further risk stratification due to significant major adverse cardiac events (MACE).

Area of Science:

  • Cardiology
  • Emergency Medicine
  • Diagnostic Assay Validation

Background:

  • Chest pain is a common emergency department presentation.
  • Accurate and rapid diagnosis of acute myocardial infarction (AMI) is critical.
  • High-sensitivity troponin (hsTn) assays are increasingly used for AMI rule-out.

Purpose of the Study:

  • To validate the National Institute for Health and Care Excellence (NICE) recommended algorithms for hsTn assays.
  • To assess the performance of NICE algorithms in identifying patients with low probability of AMI.
  • To evaluate the rate of major adverse cardiac events (MACE) within 30 days for patients ruled out by these algorithms.

Main Methods:

  • International post hoc analysis of three prospective, observational studies.
  • Inclusion of adult patients presenting with chest pain to tertiary hospital emergency departments.
  • Primary endpoint: cardiac death or AMI within 24 hours; Secondary endpoint: MACE at 30 days.

Main Results:

  • hsTnI algorithm ruled out 80.1% of patients, with 2.0% missed MI. hsTnT algorithm ruled out 53.1%, with 0.4% missed MI.
  • A significant proportion of patients had troponin levels below the limit of detection/blank.
  • 30-day MACE rates were 10.7% (hsTnI) and 8.5% (hsTnT) for 'ruled out' patients.

Conclusions:

  • NICE algorithms can identify low-probability AMI patients within 2 hours.
  • Algorithm performance did not fully align with NICE diagnostic guidance predictions.
  • The observed MACE rates necessitate using these algorithms as part of a broader risk stratification model.
Abstract

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