Mueller, Christian

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.

Background: Copeptin in combination with troponin has been shown to have incremental value for the early rule-out of myocardial infarction, but its performance in Black patients specifically has never been examined. In light of a potential for wider use, data on copeptin in different relevant cohorts are needed. This is the first study to determine whether copeptin is equally effective at ruling out myocardial infarction in Black and Caucasian races. Methods: This analysis of the CHOPIN trial included 792 Black and 1075 Caucasian patients who presented to the emergency department with chest pain and had troponin-I and copeptin levels drawn. Results: One hundred and forty-nine patients were diagnosed with myocardial infarction (54 Black and 95 Caucasian). The negative predictive value of copeptin at a cut-off of 14 pmol/l (as in the CHOPIN study) for myocardial infarction was higher in Blacks (98.0%, 95% confidence interval (CI) 96.2–99.1%) than Caucasians (94.1%, 95% CI 92.1–95.7%). The sensitivity at 14 pmol/l was higher in Blacks (83.3%, 95% CI 70.7–92.1%) than Caucasians (53.7%, 95% CI 43.2–64.0%). After controlling for age, hypertension, heart failure, chronic kidney disease and body mass index in a logistic regression model, the interaction term had a P value of 0.03. A cut-off of 6 pmol/l showed similar sensitivity in Caucasians as 14 pmol/l in Blacks. Conclusions: This is the first study to identify a difference in the performance of copeptin to rule out myocardial infarction between Blacks and Caucasians, with increased negative predictive value and sensitivity in the Black population at a cut-off of 14 pmol/l. This also holds true for non-ST-segment elevation myocardial infarction and, although numbers were small, similar trends exist in the normal troponin population. This may have significant implications for early rule-out strategies using copeptin.

Abstract There is an unmet need for effective treatment strategies to reduce morbidity and mortality in patients with heart failure with preserved ejection fraction (HFpEF). Until recently, attention in patients with HFpEF was almost exclusively focused on the left side. However, it is now increasingly recognized that right heart dysfunction is common and contributes importantly to poor prognosis in HFpEF. More insights into the development of right heart dysfunction in HFpEF may aid to our knowledge about this complex disease and may eventually lead to better treatments to improve outcomes in these patients. In this position paper from the Heart Failure Association of the European Society of Cardiology, the Committee on Heart Failure with Preserved Ejection Fraction reviews the prevalence, diagnosis, and pathophysiology of right heart dysfunction and failure in patients with HFpEF. Finally, potential treatment strategies, important knowledge gaps and future directions regarding the right side in HFpEF are discussed.

Copeptin is currently understood as a quantitative marker of endogenous stress. It rises rapidly in multiple acute disorders including acute myocardial infarction. As a single variable, it has only modest diagnostic accuracy for acute myocardial infarction. However, the use of copeptin within a dual-marker strategy together with conventional cardiac troponin increases the diagnostic accuracy and particularly the negative predictive value of cardiac troponin alone for acute myocardial infarction. The rapid rule-out of acute myocardial infarction is the only application in acute cardiac care mature enough to merit consideration for routine clinical care. However, the dual-marker approach seems to provide only very small incremental value when used in combination with sensitive or high-sensitivity cardiac troponin assays. This review aims to update and educate regarding the potential and the procedural details, as well as the caveats and challenges of using copeptin in clinical practice.

AbstractObjectivesChest pain is a common complaint to emergency departments (EDs) and clinical risk factors are used to predict which patients are at risk for worse outcomes and mortality. The goal was to assess the novel biomarker midregional proadrenomedullin (MR‐proADM) in prediction of mortality and major adverse cardiac events (MACE).MethodsThis was a subanalysis of the CHOPIN study, a 16‐center prospective trial that enrolled 2,071 patients presenting with chest pain within 6 hours of onset. The primary endpoint was 6‐month all‐cause mortality and the secondary endpoint was 30‐day and 6‐month MACE: ED visits or hospitalization for acute myocardial infarction, unstable angina, reinfarction, revascularization, and heart failure.ResultsMR‐proADM performed similarly to troponin (cTnI; c‐statistic = 0.845 and 0.794, respectively) for mortality prediction in all subjects and had similar results in those with noncardiac diagnoses. MR‐proADM concentrations were stratified by decile, and the cohort in the top decile had a 9.8% 6‐month mortality risk versus 0.9% risk for those in the bottom nine deciles (p < 0.0001). MR‐proADM, history of coronary artery disease (CAD), and hypertension were predictors of short‐term MACE, while history of CAD, hypertension, cTnI, and MR‐proADM were predictors of long‐term MACE.ConclusionsIn patients with chest pain, MR‐proADM predicts mortality and MACE in all‐comers with chest pain and has similar prediction in those with a noncardiac diagnosis. This exploratory analysis is primarily hypotheses‐generating and future prospective studies to identify its utility in risk stratification should be considered.