Twerenbold, Raphael

Abstract Background High-sensitivity cardiac troponin (hs-cTn) assays are recommended for the diagnosis of acute myocardial infarction. Here, we characterize the analytical performance of a next-generation hs-cTn assay, Elecsys® Troponin T hs Gen 6 (Roche Diagnostics International). Methods Surplus lithium-heparin plasma or serum samples from patients or healthy volunteers were run on Cobas® e 801, e 402, and Pro analyzers. Limits of blank (LoB), limits of detection (LoD), and limits of quantitation (LoQ) were determined according to CLSI EP17-A2, with target values of 1.0 and 1.5 ng/L for LoB/LoD and 3.0 ng/L (10% CV) and 1.5 ng/L (20% CV) for LoQ, respectively. Precision was measured, per CLSI EP17-A2, using 3 QC samples (approximately 4, 30, and 220 ng/L), 12 native samples, and 3 reagent lots. Linearity, per CLSI EP06-Ed2, was determined by diluting samples with cardiac troponin T (cTnT) concentration above the measuring range with a low/blank sample. Interference (per Glick) with endogenous and assay components at 5 cTnT concentrations was assessed. Results Measured values for LoB, LoD, and LoQ at 10% and 20% CV were 0.1 to 0.7 ng/L, 0.3 to 1.4 ng/L, 1.0 to 2.9 ng/L, and 0.4 to 1.2 ng/L, respectively. Repeatability CVs were 1.0 to 5.8% for mean cTnT concentrations of 2.6 to 9230 ng/L in lithium-heparin plasma. High precision was shown across lots, and linearity was observed across the measuring range (1.5 to 9500 ng/L, all Pearson's r = 1.00). No interferences were observed, specified up to ≤1000 mg/dL hemoglobin, ≤50 mg/dL [≤855 µmol/L] icterus/bilirubin, and ≤1200 ng/mL biotin. Conclusions The analytical performance characterization of the assay demonstrated high sensitivity, high precision at the low end and across the measuring range, and resistance to interference.

Abstract Background Measurement of cardiac troponin (cTn) using high-sensitivity assays is recommended for the diagnosis of myocardial infarction. We determined sex-specific and uniform 99th percentile upper reference limits (URLs) using the new Elecsys® Troponin T high-sensitivity Gen 6 assay in a global, healthy reference range cohort. Methods Lithium-heparin plasma and serum samples were prospectively collected from apparently healthy individuals aged ≥20 years across 34 global sites in the United States, Europe, China, and Japan. cTnT was measured using the Troponin T high-sensitivity Gen 6 assay on the Cobas® e 801 analyzer. Exclusion criteria were defined according to the 2022 IFCC guidance. Uniform and sex-specific 99th percentile URLs and nonparametric 95% CIs were determined in plasma and serum separately and combined. Results The final study population comprised 4147 participants (52.5% female) with a median (25th–75th percentiles) age of 48.0 (33.0–59.0) years; 45.8%, 47.9%, 5.2%, and 1.1% were White, Asian, Black, and other/unknown, respectively. For sample matrices combined (n = 8294), 81.0% and 99.2% of cTnT values were above the limit of detection in females and males, respectively. Sex-specific 99th percentile URLs (95% CI) were 18 (16–23) ng/L for females and 32 (28–35) ng/L for males; the uniform 99th percentile URL was 27 (24–31) ng/L. URLs were comparable in plasma and serum samples. Conclusions This study determined sex-specific and uniform 99th percentile URLs for the Troponin T high-sensitivity Gen 6 assay that were comparable irrespective of the matrix used in a large, global, healthy reference population.

Background: Variations in cardiac troponin concentrations by age, sex, and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. Methods: A machine learning algorithm (myocardial-ischemic-injury-index [MI 3 ]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3013 patients and tested on 7998 patients with suspected myocardial infarction. MI 3 uses gradient boosting to compute a value (0–100) reflecting an individual’s likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value, specificity and positive predictive value for that individual. Assessment was by calibration and area under the receiver operating characteristic curve. Secondary analysis evaluated example MI 3 thresholds from the training set that identified patients as low risk (99% sensitivity) and high risk (75% positive predictive value), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology rule-out pathways. Results: Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI 3 was well calibrated with a very high area under the receiver operating characteristic curve of 0.963 [0.956–0.971] in the test set and similar performance in early and late presenters. Example MI 3 thresholds identifying low- and high-risk patients in the training set were 1.6 and 49.7, respectively. In the test set, MI 3 values were <1.6 in 69.5% with a negative predictive value of 99.7% (99.5–99.8%) and sensitivity of 97.8% (96.7–98.7%), and were ≥49.7 in 10.6% with a positive predictive value of 71.8% (68.9–75.0%) and specificity of 96.7% (96.3–97.1%). Using these thresholds, MI 3 performed better than the European Society of Cardiology 0/3-hour pathway (sensitivity, 82.5% [74.5–88.8%]; specificity, 92.2% [90.7–93.5%]) and the 99th percentile at any time point (sensitivity, 89.6% [87.4–91.6%]); specificity, 89.3% [88.6–90.0%]). Conclusions: Using machine learning, MI 3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low- and high-risk patients who may benefit from earlier clinical decisions. Clinical Trial Registration: URL: https://www.anzctr.org.au . Unique identifier: ACTRN12616001441404.