Citations

Introduction Clinical assessment in emergency departments (EDs) for possible acute myocardial infarction (AMI) requires at least one cardiac troponin (cTn) blood test. The turn-around time from blood draw to posting results in the clinical portal for central laboratory analysers is ~1–2 hours. New generation, high-sensitivity, point-of-care cardiac troponin I (POC-cTnI) assays use whole blood on a bedside (or near bedside) analyser that provides a rapid (8 min) result. This may expedite clinical decision-making and reduce length of stay. Our purpose is to determine if utilisation of a POC-cTnI testing reduces ED length of stay. We also aim to establish an optimised implementation process for the amended clinical pathway. Methods and analysis This quality improvement initiative has a pragmatic multihospital stepped-wedge cross-sectional cluster randomised design. Consecutive patients presenting to the ED with symptoms suggestive of possible AMI and having a cTn test will be included. Clusters (comprising one or two hospitals each) will change from their usual-care pathway to an amended pathway using POC-cTnI—the ‘intervention’. The dates of change will be randomised. Changes occur at 1 month intervals, with a minimum 2 month ‘run-in’ period. The intervention pathway will use a POC-cTnI measurement as an alternate to the laboratory-based cTn measurement. Clinical decision-making steps and logic will otherwise remain unchanged. The POC-cTnI is the Siemens (Erlangen Germany) Atellica VTLi high-sensitivity cTnI assay. The primary outcome is ED length of stay. The safety outcome is cardiac death or AMI within 30 days for patients discharged directly from the ED. Ethics and dissemination Ethics approval has been granted by the New Zealand Southern Health and Disability Ethics Committee, reference 21/STH/9. Results will be published in a peer-reviewed journal. Lay and academic presentations will be made. Māori-specific results will be disseminated to Māori stakeholders. Trial registration number ACTRN12619001189112.

Abstract The International Federation of Clinical Chemistry Committee on Clinical Applications of Cardiac Biomarkers (IFCC C-CB) provides educational documents to facilitate the interpretation and use of cardiac biomarkers in clinical laboratories and practice. Our aim is to improve the understanding of certain key analytical and clinical aspects of cardiac biomarkers and how these may interplay. Measurements of cardiac troponin (cTn) have a prominent place in the clinical work-up of patients with suspected acute coronary syndrome. It is therefore important that clinical laboratories know how to recognize and assess analytical issues. Two emerging analytical issues resulting in falsely high cTn concentrations, often several fold higher than the upper reference limit (URL), are antibody-mediated assay interference due to long-lived cTn-antibody complexes, called macrotroponin, and crosslinking antibodies that are frequently referred to as heterophilic antibodies. We provide an overview of antibody-mediated cTn assay interference and provide recommendations on how to confirm the interference and interpret the results.

Abstract The coronavirus disease 2019 (COVID-19) pandemic has increased awareness that severe acute respiratory distress syndrome coronavirus-2 (SARS-CoV-2) may have profound effects on the cardiovascular system. COVID-19 often affects patients with pre-existing cardiac disease, and may trigger acute respiratory distress syndrome (ARDS), venous thromboembolism (VTE), acute myocardial infarction (AMI), and acute heart failure (AHF). However, as COVID-19 is primarily a respiratory infectious disease, there remain substantial uncertainty and controversy whether and how cardiovascular biomarkers should be used in patients with suspected COVID-19. To help clinicians understand the possible value as well as the most appropriate interpretation of cardiovascular biomarkers in COVID-19, it is important to highlight that recent findings regarding the prognostic role of cardiovascular biomarkers in patients hospitalized with COVID-19 are similar to those obtained in studies for pneumonia and ARDS in general. Cardiovascular biomarkers reflecting pathophysiological processes involved in COVID-19/pneumonia and its complications have a role evaluating disease severity, cardiac involvement, and risk of death in COVID-19 as well as in pneumonias caused by other pathogens. First, cardiomyocyte injury, as quantified by cardiac troponin concentrations, and haemodynamic cardiac stress, as quantified by natriuretic peptide concentrations, may occur in COVID-19 as in other pneumonias. The level of those biomarkers correlates with disease severity and mortality. Interpretation of cardiac troponin and natriuretic peptide concentrations as quantitative variables may aid in risk stratification in COVID-19/pneumonia and also will ensure that these biomarkers maintain high diagnostic accuracy for AMI and AHF. Second, activated coagulation as quantified by D-dimers seems more prominent in COVID-19 as in other pneumonias. Due to the central role of endothelitis and VTE in COVID-19, serial measurements of D-dimers may help physicians in the selection of patients for VTE imaging and the intensification of the level of anticoagulation from prophylactic to slightly higher or even therapeutic doses.

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.