Apple, Fred S.

Background: The observed incidence of type 2 myocardial infarction (T2MI) is expected to increase with the implementation of increasingly sensitive cTn assays. However, it remains to be determined how to diagnose, risk-stratify, and treat patients with T2MI. We aimed to discriminate and risk-stratify T2MI using biomarkers. Methods: Patients presenting to the emergency department with chest pain, enrolled in the CHOPIN study (Copeptin Helps in the early detection Of Patients with acute myocardial INfarction), were retrospectively analyzed. Two cardiologists adjudicated type 1 MI (T1MI) and T2MI. The prognostic ability of several biomarkers alone or in combination to discriminate T2MI from T1MI was investigated using receiver operating characteristic curve analysis. The biomarkers analyzed were cTnI, copeptin, MR-proANP (midregional proatrial natriuretic peptide), CT-proET1 (C-terminal proendothelin-1), MR-proADM (midregional proadrenomedullin), and procalcitonin. The prognostic utility of these biomarkers for all-cause mortality and major adverse cardiovascular event (a composite of acute myocardial infarction, unstable angina pectoris, reinfarction, heart failure, and stroke) at 180-day follow-up was also investigated. Results: Among the 2071 patients, T1MI and T2MI were adjudicated in 94 and 176 patients, respectively. Patients with T1MI had higher levels of baseline cTnI, whereas those with T2MI had higher baseline levels of MR-proANP, CT-proET1, MR-proADM, and procalcitonin. The area under the receiver operating characteristic curve for the diagnosis of T2MI was higher for CT-proET1, MR-proADM, and MR-proANP (0.765, 0.750, and 0.733, respectively) than for cTnI (0.631). Combining all biomarkers resulted in a similar accuracy to a model using clinical variables and cTnI (0.854 versus 0.884, P =0.294). Addition of biomarkers to the clinical model yielded the highest area under the receiver operating characteristic curve (0.917). Other biomarkers, but not cTnI, were associated with mortality and major adverse cardiovascular event at 180 days among all patients, with no interaction between the diagnosis of T1MI or T2MI. Conclusions: Assessment of biomarkers reflecting pathophysiologic processes occurring with T2MI might help differentiate it from T1MI. All biomarkers measured, except cTnI, were significant predictors of prognosis, regardless of the type of myocardial infarction.

Abstract Background Concerns exist regarding how the 99th percentile upper reference limit (URL) of cardiac troponin (cTn) is determined and whether it should be derived from normal healthy individuals. Content The 99th percentile URL of cTn is an important criterion to standardize the diagnosis of myocardial infarction (MI) for clinical, research, and regulatory purposes. Statistical heterogeneity in its calculation exists but recommendations have been proposed. Some negativity has resulted from the fact that with some high-sensitivity (hs) cTn assays, a greater number of increases above the 99th percentile are observed when transitioning from a contemporary assay. Increases reflect acute or chronic myocardial injury and provide valuable diagnostic and prognostic information. The etiology of increases can sometimes be difficult to determine, making a specific treatment approach challenging. For those reasons, some advocate higher cutoff concentrations. This approach can contribute to missed diagnoses. Contrary to claims, neither clinical or laboratory guidelines have shifted away from the 99th percentile. To support the diagnosis of acute MI, the 99th percentile URL remains the best-established approach given the absence of cTn assay standardization. Importantly, risk stratification algorithms using hs-cTn assays predict the possibility of MI diagnoses established using the 99th percentile. Summary The 99th percentile of cTn remains the best-established criterion for the diagnosis of acute MI. While not perfect, it is analytically and clinically evidence-based. Until there are robust data to suggest some other approach, staying with the 99th percentile, a threshold that has served the field well for the past 20 years, appears prudent.

Abstract Background How to select healthy reference subjects in deriving 99th percentiles for cardiac troponin assays still needs to be clarified. To assist with global implementation of high sensitivity (hs)-cardiac troponin (cTn) I and hs-cTnT assays in clinical practice, we determined overall and sex-specific 99th percentiles in 9 hs-cTnI and 3 hs-cTnT assays using a universal sample bank (USB). Methods The Universal Sample Bank (USB) comprised healthy subjects, 426 men and 417 women, screened using a health questionnaire. Hemoglobin A1c (>URL 6.5%), NT-proBNP (>URL 125 ng/L) and eGFR (<60 mL/min), were used as surrogate biomarker exclusion criteria along with statin use. 99th percentiles were determined by nonparametric, Harrell--Davis bootstrap, and robust methods. Results Subjects were ages 19 to 91 years, Caucasian 58%, African American 27%, Pacific Islander/Asian 11%, other 4%, Hispanic 8%, and non-Hispanic 92%. The overall and sex-specific 99th percentiles for all assays, before and after exclusions (n = 694), were influenced by the statistical method used, with substantial differences noted between and within both hs-cTnI and hs-cTnT assays. Men had higher 99th percentiles (ng/L) than women. The Roche cTnT and Beckman and Abbott cTnI assays (after exclusions) did not measure cTn values at ≥ the limit of detection in ≥50% women. Conclusions Our findings have important clinical implications in that sex-specific 99th percentiles varied according to the statistical method and hs-cTn assay used, not all assays provided a high enough percentage of measurable concentrations in women to qualify as a hs-assay, and the surrogate exclusion criteria used to define normality tended to lower the 99th percentiles.

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.