Relationships between renal function variations and relative changes in cardiac troponin T concentrations based on...

Area of Science:

• Cardiology
• Nephrology
• Biostatistics

Background:

• High-sensitive cardiac troponin T (hs-cTnT) is a key biomarker for myocardial injury.
• The interplay between hs-cTnT and renal function markers like creatinine (cre) and estimated glomerular filtration rate (eGFR) is recognized, but the precise nature of their variations requires further investigation.
• Understanding these relationships is crucial for accurate clinical interpretation.

Purpose of the Study:

• To model the relationship between relative changes in hs-cTnT (Δhs-cTnT) and variations in creatinine (Δcre) or estimated glomerular filtration rate (ΔeGFR).
• To quantify the impact of renal function variations on the interpretation of Δhs-cTnT.
• To utilize a quantile generalized additive model (qgam) for robust statistical analysis.

Main Methods:

• Analysis of concomitant plasma Δhs-cTnT and Δcre data from a large cohort (n=106567) of patients aged 18-100 years.
• Data collected over a 5.8-year period with time variation (Δtime) between 3 hours and 7 days.
• Quantile generalized additive models (qgam) were employed to fit relationships between Δhs-cTnT and covariates including Δcre, ΔeGFR, age, Δtime, and hour of blood sampling (HSB).

Main Results:

• Δhs-cTnT showed a significant positive, nonlinear association with Δcre (e.g., +55% for +100% Δcre).
• Δhs-cTnT exhibited a negative association with ΔeGFR (e.g., -20% for +100% ΔeGFR).
• Adjusting Δhs-cTnT interpretations for renal function resulted in statistically significant but minor diagnostic discrepancies (<2%) for myocardial injury compared to unadjusted interpretations.

Conclusions:

• Considering renal function variations when interpreting relative changes in cardiac troponin T has a minimal impact on the diagnosis rate of myocardial injury.
• The primary driver for Δhs-cTnT variations is Δcre, with secondary influences from age, Δtime, and HSB.
• Statistical modeling confirms the need to acknowledge renal status but highlights its limited effect on diagnostic outcomes in this context.