High sensitivity Troponin-T for prediction of adverse events in patients with COVID-19

Abstract

Background

High sensitivity cardiac troponin-T (hs-TnT) has been associated with mortality in patients hospitalized with COVID-19. We aimed to determine if hs-TnT levels and their timing are independent predictors of adverse events in these patients.

Design

Retrospective chart review was performed for all patients hospitalized at our institution between 23 March 2020 and 13 April 2020 who were found to be COVID-19-positive. Clinical, demographic, and laboratory variables including initial and peak hs-TnT were recorded. Univariable and multivariable analyses were completed for a primary composite endpoint of in-hospital death, intubation, need for critical care, or cardiac arrest.

Results

In the 276 patients analysed, initial hs-TnT above the median (≥17 ng/L) was associated with increased length of stay, need for vasoactive medications, and death, along with the composite endpoint (OR 3.92, p < 0.001). Multivariable analysis demonstrated that elevated initial hs-TnT was independently associated with the primary endpoint (OR 2.92, p = 0.01). Late-peaking hs-TnT (OR 2.19 for each additional day until peak, p < 0.001) was also independently associated with the composite endpoint.

Conclusions

In patients hospitalized with COVID-19, hs-TnT identifies patients at high risk for adverse in-hospital events, and trends of hs-TnT over time, particularly during the first day, provide additional prognostic information.

Keywords:

• Troponin
• COVID-19
• biomarker
• risk stratification
• cardiac outcomes

Disclosure statement

The authors report no conflicts of interest.

Author contributions

Dr. Alenghat had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Singh, Nguyen, Alenghat. Acquisition, analysis or interpretation of data: All authors. Drafting of the manuscript: Singh, Alenghat. Critical revision of the manuscripts for important intellectual content: All authors. Statistical analysis: Singh, Besser, and Alenghat. Supervision: Alenghat.