High-Sensitivity Troponin I Measurement in a Large Contemporary Cohort: Implications for Clinical Care
Daniel Esau1,2, Peter Nord3, Beth L Abramson4
1Royal Jubilee Hospital, Victoria, British Columbia, Canada.
Insights
High-sensitivity troponin I (hsTnI) levels are influenced by traditional cardiovascular risk factors but also by unexplained variables. This suggests hsTnI offers additional, valuable information for risk stratification beyond existing methods.
Area of Science:
- Cardiology
- Biomarker Research
- Risk Stratification
Background:
- Current cardiovascular risk stratification methods often lack accuracy.
- High-sensitivity troponin I (hsTnI) shows promise for improving risk assessment.
- Factors influencing hsTnI levels require further investigation.
Purpose of the Study:
- To determine the prevalence of elevated hsTnI in a large Canadian cohort.
- To analyze the impact of comorbidities on hsTnI concentration.
- To explore predictors of hsTnI in an outpatient setting.
Main Methods:
- Analysis of a large dataset (41,602 visits) of routine hsTnI measurements in outpatients.
- hsTnI remeasured in 28% of patients to assess stability.
- Extraction of laboratory data, blood pressure, and anthropometric measures from electronic medical records.
Main Results:
- hsTnI levels were predicted by male sex, higher HDL-C, higher Hgb A1c, decreasing eGFR, and increasing systolic blood pressure.
- Higher non-HDL-C and statin use were associated with lower hsTnI.
- Remesured hsTnI did not alter risk category in 92.7% of patients; model fit was poor (R-squared = 0.0091).
Conclusions:
- Traditional cardiovascular risk factors partially predict hsTnI levels.
- A substantial portion of hsTnI variance remains unexplained by traditional factors.
- hsTnI provides information beyond conventional risk stratification, supporting its role as a valuable biomarker.
Abstract:
Introduction: Contemporary methods of cardiovascular (CV) risk stratification are frequently inaccurate. Biomarkers such as high-sensitivity troponin I (hsTnI) have the potential to improve risk stratification. However, uncertainties exist regarding factors that determine hsTnI concentration. Our aim was to investigate the prevalence of elevated hsTnI in a large contemporary Canadian cohort and describe the effect of comorbidities on hsTnI concentration.
Methods:
We report a large dataset of 41,602 visits in which hsTnI was measured routinely in ambulatory outpatients. hsTnI was remeasured in 28% of patients, with a mean time between measurements of 387 days (IQR 364-441). Low-, medium-, and high-risk categories were created based on hsTnI cutoffs for each sex. Laboratory data, blood pressure, and anthropomorphic measures were extracted from the electronic medical record.
Results:
Remeasurement of hsTnI did not change risk category in 92.7% of cases. Male sex, higher HDL-C, higher Hgb A1c, decreasing eGFR, and increasing systolic blood pressure were significant predictors of increased hsTnI. High non-HDL-C and the use of statins were associated with lower hsTnI. The inverse relationship between hsTnI and non-HDL-C was partially corrected when the confounding effect of statin therapy was considered. Model fit was poor (adjusted R-squared = 0.0091).
Conclusion:
Traditional CV risk factors were predictors of serum hsTnI levels; however, a significant amount of the variance in hsTnI cannot be explained by these factors alone. This suggests that hsTnI adds additional information that is not provided by traditional risk stratification methods and supports ongoing study of hsTnI as a biomarker for CV risk stratification.
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