UCSF

Importance

Levels of high-sensitivity cardiac troponin T (hs-cTnT) predict secondary cardiovascular events in patients with stable coronary heart disease.

Objectives

To determine the association of hs-cTnT levels with structural and functional measures of heart disease and the extent to which these measures explain the relationship between hs-cTnT and secondary events.

Design

We measured serum concentrations of hs-cTnT and performed exercise treadmill testing with stress echocardiography in a prospective cohort study of outpatients with coronary heart disease who were enrolled from September 11, 2000, through December 20, 2002, and followed up for a median of 8.2 years.

Setting

Twelve outpatient clinics in the San Francisco Bay Area.

Participants

Nine hundred eighty-four patients with stable coronary heart disease.

Main outcomes and measures

Cardiovascular events (myocardial infarction, heart failure, or cardiovascular death), determined by review of medical records and death certificates.

Results

Of 984 participants, 794 (80.7%) had detectable hs-cTnT levels. At baseline, higher hs-cTnT levels were associated with greater inducible ischemia and worse left ventricular ejection fraction, left atrial function, diastolic function, left ventricular mass, and treadmill exercise capacity. During follow-up, 317 participants (32.2%) experienced a cardiovascular event. After adjustment for clinical risk factors, baseline cardiac structure and function, and other biomarkers (N-terminal portion of the prohormone of brain-type natriuretic peptide and C-reactive protein levels), each doubling in hs-cTnT level remained associated with a 37% higher rate of cardiovascular events (hazard ratio, 1.37 [95% CI, 1.14-1.65]; P = .001).

Conclusions and relevance

In outpatients with stable coronary heart disease, higher hs-cTnT levels were associated with multiple abnormalities of cardiac structure and function but remained independently predictive of secondary events. These findings suggest that hs-cTnT levels may detect an element of risk that is not captured by existing measures of cardiac disease severity.