Rising and Falling High-Sensitivity Cardiac Troponin in Diagnostic Algorithms for Patients With Suspected Myocardial
Paul M Haller1,2, Nils A Sörensen1,2, Tau S Hartikainen1
1Department of Cardiology University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf Hamburg Germany.
Insights
Falling troponin patterns (FPs) have a lower positive predictive value for myocardial infarction (MI) diagnosis than rising troponin patterns (RPs). Patients with FPs face the highest risk for death or recurrent MI.
Area of Science:
- Cardiology
- Biomarkers
- Diagnostic Algorithms
Background:
- High-sensitivity cardiac troponin (hs-cTn) assays are crucial for diagnosing myocardial infarction (MI) without ST elevation.
- Current diagnostic algorithms often treat falling (FP) and rising (RP) troponin patterns similarly.
- The differential performance of these patterns in diagnostic protocols requires investigation.
Purpose of the Study:
- To compare the diagnostic performance of European Society of Cardiology (ESC) algorithms for FPs versus RPs in suspected MI.
- To evaluate the prognostic implications of FP and RP in patients undergoing hs-cTn testing.
Main Methods:
- Pooled analysis of two prospective cohorts of patients with suspected MI.
- Stratification of patients into stable, FP, and RP groups based on serial hs-cTnI and hs-cTnT measurements.
- Application of ESC 0/1-hour and 0/3-hour diagnostic algorithms to assess positive predictive values for MI rule-in.
Main Results:
- The positive predictive value for ruling in MI was significantly lower for FPs compared to RPs across both 0/1-hour and 0/3-hour algorithms.
- A larger proportion of patients with FPs fell into the 'observe' zone, indicating diagnostic uncertainty.
- Patients with FPs exhibited the highest adjusted risk for death or MI compared to stable troponin levels or RPs.
Conclusions:
- ESC diagnostic algorithms demonstrate significantly lower positive predictive value for MI rule-in in patients with FPs compared to RPs.
- Falling troponin patterns are associated with the highest risk of adverse outcomes, including death or incident MI.
- Clinical management strategies may need to differentiate between FP and RP for optimal patient care.
Abstract:
Background High-sensitivity cardiac troponin (hs-cTn)-based diagnostic algorithms are recommended for the management of patients with suspected myocardial infarction (MI) without ST elevation. Although mirroring different phases of myocardial injury, falling and rising troponin patterns (FPs and RPs, respectively) are equally considered by most algorithms. We aimed to compare the performance of diagnostic protocols for RPs and FPs, separately. Methods and Results We pooled 2 prospective cohorts of patients with suspected MI and stratified patients to stable, FP, and RP during serial sampling separately for hs-cTnI and hs-cTnT and applied the European Society of Cardiology 0/1- and 0/3-hour algorithms comparing the positive predictive values to rule in MI. Overall, 3523 patients were included in the hs-cTnI study population. The positive predictive value for patients with an FP was significantly reduced compared with patients with an RP (0/1-hour: FP, 53.3% [95% CI, 45.0-61.4] versus RP, 76.9 [95% CI, 71.6-81.7]; 0/3-hour: FP, 56.9% [95% CI, 42.2-70.7] versus RP, 78.1% [95% CI, 74.0-81.8]). The proportion of patients in the observe zone was larger in the FP using 0/1-hour (31.3% versus 55.8%) and 0/3-hour (14.6% versus 38.6%) algorithms. Alternative cutoffs did not improve algorithm performances. Compared with stable hs-cTn, the risk for death or MI was highest in those with an FP (adjusted hazard ratio [HR], hs-cTnI 2.3 [95% CI, 1.7-3.2]; RP adjusted HR, hs-cTnI 1.8 [95% CI, 1.4-2.4]). Findings were similar for hs-cTnT tested in 3647 patients overall. Conclusions The positive predictive value to rule in MI by the European Society of Cardiology 0/1- and 0/3-hour algorithms is significantly lower in patients with FP than RP. These are at highest risk for incident death or MI. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifiers: NCT02355457, NCT03227159.
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