Boeddinghaus, Jasper

Abstract Aims Type 2 myocardial infarction due to myocardial oxygen supply–demand imbalance is associated with poor outcomes. There are no guidelines to inform care for these patients. The consensus on the assessment and management of type 2 myocardial infarction is gained. Methods and results An international e-Delphi study including experts in type 2 myocardial infarction identified through systematic review was conducted. Participants were asked to describe their approach to (i) definition and diagnosis, (ii) risk stratification, (iii) assessment of coronary artery disease and cardiac function, (iv) specialty management, (v) treatment and secondary prevention, and (vi) communication and rehabilitation. Statements generated in round one were circulated, with consensus defined a priori as ≥70% agreement on a 5-point Likert scale. Where no consensus was reached, statements were amended and recirculated for a final round. The response rate was 56% (38/68), 54% (37/68), and 72% (49/68) in the first, second, and third rounds, respectively. Following the first round, 67 unique statements were generated across six domains. Overall, consensus was achieved on 64% (43/67) of statements. Consensus was achieved for 42% (5/12) of statements on the diagnosis of type 2 myocardial infarction, 75% (3/4) on risk stratification, 50% (9/18) on the assessment of coronary artery disease and cardiac function, 60% (6/10), on specialty management, 100% (9/9) on treatment and secondary prevention, and 79% (11/15) on communication and rehabilitation. Conclusion Consensus was obtained across a number of domains for the assessment and management of patients with type 2 myocardial infarction. However, there was limited agreement amongst experts on the diagnostic criteria, which may benefit from refinement.

Abstract New technologies enabling access to high-sensitivity cardiac troponin (hs-cTn) assays at the point of care (POC) are available for routine use. POC technology can accelerate cardiac troponin testing within the hospital setting and support testing in other healthcare environments. Pre-analytical and analytical issues unique to POC testing are discussed. The opportunities and the evidence needed to support the routine use of hs-cTn POC assays in clinical care are outlined. Based on recent developments, the potential impact of hs-cTn at the POC in multiple clinical settings is described, and a roadmap of the steps required for successful implementation is provided.

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.