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Emerg Med Australas. 2012 Apr;24(2):151-8. doi: 10.1111/j.1742-6723.2011.01530.x. Epub 2012 Jan 19.

Cardiac troponin I does not independently predict mortality in critically ill patients with severe sepsis.

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1
Department of Intensive Care Medicine, Frankston Hospital, Melbourne, VIC 3199, Australia. travindranath@hotmail.com

Abstract

OBJECTIVE:

Patients with sepsis often have elevated cardiac troponin I even in the absence of coronary artery disease. The prognostic value of cardiac troponins in critically ill patients with sepsis remains debatable. Our objective was to evaluate the prognostic value of cardiac troponin I in critically ill patients with severe sepsis.

METHODS:

In this retrospective study, we included patients with severe sepsis who had troponin assayed within 12 h of admission to intensive care over a 6 year period. Patients who had myocardial infarction at intensive care admission in the setting of sepsis were excluded. Included patients were classified into two groups based on their serum troponin I levels: low troponin group (troponin ≤ 0.1 µg/L) and elevated troponin group (troponin > 0.1 µg/L). The primary outcome of interest was hospital mortality. The secondary outcome measures included intensive care mortality, intensive care and hospital length of stay.

RESULTS:

A total of 382 patients were admitted to intensive care with sepsis. Of these, 293 patients were included in analyses. There was a statistically significant difference in hospital (15% vs 36.1%; P < 0.01) and intensive care (11% vs 25%; P < 0.01) mortality, but not in intensive care and hospital duration of stay. Logistic regression analysis revealed temperature, simplified acute physiology score II and serum lactate to be independent predictors of hospital mortality. Cardiac troponin I was not an independent predictor of hospital mortality.

CONCLUSION:

Critically ill patients with severe sepsis who had elevated troponin had increased hospital and intensive care mortality. However, cardiac troponin I did not independently predict hospital mortality.

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