Point-of-Care Troponin Testing during Ambulance Transport to Detect Acute Myocardial Infarction

Stopyra, Jason P.; Snavely, Anna C.; Scheidler, James; Smith, Lane M.; Nelson, R. Darrell; Winslow, James E.; Pomper, Gregory J.; Ashburn, Nicklaus P.; Hendley, Nella; Riley, Robert F.; Koehler, Lauren E.; Miller, Chadwick D.; Mahler, Simon A.

doi:10.1080/10903127.2020.1721740

Cited by 28 publications

(21 citation statements)

References 23 publications

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“…On contrast, our algorithm has been shown to improve the net gain classification performance of patients with ACS (rule in), plus it can aid decisions at the prehospital setting. A recent study has shown that point-of-care troponin assays has a sensitivity of only 27% when used during ambulance transport 39 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

et al. 2020

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Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

et al. 2020

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“…These findings show that prehospital troponin testing has a sensitivity ranging from 26.5% to 91.0% and NPV up to 94.9% for the diagnosis of AMI. [6][7][8][9][10][11][12] One of the most challenging tasks for paramedics in the prehospital setting is the diagnosis of AMI. Treatment for ACS or "rule out" could be initiated in the prehospital setting based on the ECG, the patient history of risk factors, blood pressure, and heart rate.…”

Section: Discussionmentioning

confidence: 99%

“…The studies included in the systematic review (Table 1) were conducted in four different countries: three in Denmark, [6][7][8] two in Canada, 9,10 one in Italy, 11 and one in United State of America. 12 As shown in Table 2, the POC assay characteristics for each individual study included sensitivity, specificity, NPV, and positive predictive value (PPV).…”

Section: Study Characteristics and Methodological Quality Analysismentioning

confidence: 99%

Diagnostic Performance of Prehospital Point-of-Care Troponin Tests to Rule Out Acute Myocardial Infarction: A Systematic Review

Alghamdi

Alotaibi

Alharbi

et al. 2020

Prehosp. Disaster med.

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Abstract Introduction: Chest pain is one of the most common reasons for 999 calls and transfers to the emergency department (ED). In these patients, acute myocardial infarction (AMI) is often the diagnosis that clinicians are seeking to exclude. However, only a minority of those patients have AMI, causing a substantial financial burden to health services. Cardiac troponin (cTn) is the reference standard biomarker for the diagnosis of AMI. Several commercially available point-of-care (POC) cTn assays are portable and could feasibly be used in an ambulance. The aim of this paper is to systematically review existing evidence for the use of POC cTn assays in the prehospital setting to rule out AMI. Methods: A systematic search was conducted on EMBASE, MEDLINE, and CINAHL Plus databases, reference lists, and relevant grey literature, including combinations of the relevant terms. Papers published in English language since the year 2000 were eligible for inclusion. A narrative synthesis of the evidence was then undertaken. Results: The initial search and cross-referencing revealed a total of 350 papers, of which 243 were excluded. Seven papers were included in the systematic literature review. Conclusion: Current evidence does not support the use of POC troponin assays to exclude AMI due to issues with diagnostic accuracy and insufficient high-quality evidence.

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“…1 2 Due to limited diagnostic tests, the AMI diagnosis is challenging in the prehospital emergency setting, [3][4][5] and the value of prehospital risk stratification with point-ofcare troponins with or without risk assessment scores has received increased attention during the last decade. [6][7][8] Still, there is no prehospital strategy that safely excludes AMI outside of hospitals. 5 8 9 The introduction of high-sensitivity assays for cardiac troponins opened for rapid diagnostic pathways in hospitals, [10][11][12] and the diagnostic utility of the 2015 European Society of Cardiology (ESC) 0/1-hour algorithm for high-sensitivity cardiac troponin T (hs-cTnT) 2 has been confirmed in large validation studies from hospital emergency departments (EDs).…”

Section: Introductionmentioning

confidence: 99%

Pre-hospital One-Hour Troponin in a Low-Prevalence Population of Acute Coronary Syndrome: OUT-ACS study

et al. 2020

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ObjectiveThe European Society of Cardiology 0/1-hour algorithm for high-sensitivity cardiac troponin T (hs-cTnT) has demonstrated high rule-out safety in large hospital validation cohorts. We aimed to validate the algorithm in a primary care setting, where patients have a lower pretest probability for acute coronary syndrome.MethodsThis prospective, observational, diagnostic study included patients with acute non-specific chest pain admitted to a primary care emergency clinic in Oslo, Norway, from November 2016 to October 2018. hs-cTnT was measured after 0, 1 and 4 hours. The primary outcome measure was the diagnostic performance of the 0/1-hour algorithm, the 90-day incidence of AMI or all-cause death the secondary.ResultsAmong 1711 included patients, 61 (3.6%) were diagnosed with AMI. By applying the algorithm, 1311 (76.6%) patients were assigned to the rule-out group. The negative predictive value was 99.9% (95% CI 99.5% to 100.0%), the sensitivity and specificity 98.4% (91.2–100.0) and 79.4% (77.4–81.3), respectively. Sixty-six (3.9%) patients were triaged towards rule-in, where 45 were diagnosed with AMI. The corresponding positive predictive value was 68.2% (58.3–76.7), sensitivity 73.8% (60.9–84.2), and specificity 98.7% (98.1–99.2). Among 334 (19.5%) patients assigned to the observation group in need of further tests, 15 patients had an AMI. The following 90 days, five new patients experienced an AMI and nine patients died, with a low incidence in the rule-out group (0.3%).ConclusionThe 0/1-hour algorithm for hs-cTnT seems safe, efficient and applicable for an accelerated assessment of patients with non-specific chest pain in a primary care emergency setting.Trial registration numberNCT02983123.

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Point-of-Care Troponin Testing during Ambulance Transport to Detect Acute Myocardial Infarction

Cited by 28 publications

References 23 publications

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

Diagnostic Performance of Prehospital Point-of-Care Troponin Tests to Rule Out Acute Myocardial Infarction: A Systematic Review

Pre-hospital One-Hour Troponin in a Low-Prevalence Population of Acute Coronary Syndrome: OUT-ACS study

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