Risk stratification in patients with acute chest pain using three high-sensitivity cardiac troponin assays

Philip Haaf1, Tobias Reichlin, Raphael Twerenbold

  • 1Department of Cardiology, University Hospital Basel, Petersgraben 4, CH-4031 Basel, Switzerland.

Insights

High-sensitivity cardiac Troponin T (hs-cTnT) offers superior 2-year mortality prediction compared to hs-cTnI assays in acute chest pain patients. Early hs-cTn changes do not enhance risk stratification beyond initial values.

Area of Science:

  • Cardiology
  • Biomarkers
  • Clinical Diagnostics

Background:

  • High-sensitivity cardiac troponin (hs-cTn) assays are crucial for diagnosing acute myocardial infarction.
  • The prognostic value of different hs-cTn assays and early changes in predicting mortality remains unclear.

Purpose of the Study:

  • To compare the prognostic accuracy of novel hs-cTn assays (hs-cTnT and two hs-cTnI) versus a conventional assay.
  • To determine if early changes in hs-cTn levels improve mortality prediction.

Main Methods:

  • A prospective, international multicenter study involving 1117 patients with acute chest pain.
  • Simultaneous measurement of cardiac troponin using three hs-cTn assays and one conventional assay.
  • Two-year follow-up for mortality, with analysis of prognostic accuracy (AUC) and impact of troponin changes.

Main Results:

  • High-sensitivity cardiac Troponin T (hs-cTnT) demonstrated the highest 2-year prognostic accuracy (AUC 0.78), outperforming hs-cTnI assays and conventional cTnT.
  • Initial hs-cTn concentrations, particularly hs-cTnT, were significant predictors of mortality across various subgroups.
  • Changes in hs-cTn levels within 6 hours did not improve prognostic accuracy beyond presentation values.

Conclusions:

  • High-sensitivity cardiac Troponin T (hs-cTnT) provides more accurate long-term mortality prediction than hs-cTnI assays.
  • Early hs-cTn changes do not offer additional risk stratification benefit beyond initial measurements in patients with acute chest pain.
Abstract

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