Systematic Review of Clinical Decision Support Systems for Prehospital Acute Coronary Syndrome Identification

Knoery, Charles; Heaton, Janet; Polson, Rob; Bond, Raymond; Iftikhar, Aleeha; Rjoob, Khaled; McGilligan, Victoria; Peace, Aaron; Leslie, Stephen J

doi:10.1097/hpc.0000000000000217

Cited by 17 publications

(12 citation statements)

References 50 publications

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“…Several studies have investigated the prediction of ACS. Integrated components of patient history, vital signs, 12-lead electrocardiograms (ECG), and cardiac enzymes were studied to increase the accuracy of diagnosis in prehospital management 2 . Prehospital 12-lead ECG is recommended for early diagnosis in patients with suspected ST-segment elevation myocardial infarction (STEMI) 3 ; however, costs and lack of training of 12-lead ECG limit its widespread use 4 , 5 .…”

Section: Introductionmentioning

confidence: 99%

Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study

Takeda

Oami

Hayashi

et al. 2022

Sci Rep

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Rapid and precise prehospital recognition of acute coronary syndrome (ACS) is key to improving clinical outcomes. The aim of this study was to investigate a predictive power for predicting ACS using the machine learning-based prehospital algorithm. We conducted a multicenter observational prospective study that included 10 participating facilities in an urban area of Japan. The data from consecutive adult patients, identified by emergency medical service personnel with suspected ACS, were analyzed. In this study, we used nested cross-validation to evaluate the predictive performance of the model. The primary outcomes were binary classification models for ACS prediction based on the nine machine learning algorithms. The voting classifier model for ACS using 43 features had the highest area under the receiver operating curve (AUC) (0.861 [95% CI 0.775–0.832]) in the test score. After validating the accuracy of the model using the external cohort, we repeated the analysis with a limited number of selected features. The performance of the algorithms using 17 features remained high AUC (voting classifier, 0.864 [95% CI 0.830–0.898], support vector machine (radial basis function), 0.864 [95% CI 0.829–0.887]) in the test score. We found that the machine learning-based prehospital algorithms showed a high predictive power for predicting ACS.

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Section: Introductionmentioning

confidence: 99%

Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study

Takeda

Oami

Hayashi

et al. 2022

Sci Rep

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“…While several studies have demonstrated the e cacy and feasibility of risk strati cation for ACS with combined modalities such as 12-lead ECG and biomarkers in the emergency department [11][12][13] and prehospital setting [2], there are few reports predicting the onset of ACS according to vital signs, ECG monitoring, and symptoms obtained by EMS personnel. A prehospital stroke scale with physical examination has been [17] designed to be accessible and applicable for EMS personnel initially triaging patients with limited information, but the conventional scoring system for suspected ACS requires 12-lead ECG and cardiac troponin in addition to medical history [12].…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have investigated the prediction of ACS. Integrated components of patient history, vital signs, 12-lead electrocardiograms (ECG), and cardiac enzymes were studied to increase the accuracy of diagnosis in prehospital management [2]. Prehospital 12-lead ECG is recommended for early diagnosis in patients with suspected ST-segment elevation myocardial infarction (STEMI) [3]; however, costs and lack of training of 12-lead ECG limit its widespread use [4,5].…”

Section: Introductionmentioning

confidence: 99%

Prehospital Diagnostic Algorithm for Acute Coronary Syndrome Using Machine Learning: A Prospective Observational Study

Takeda

Oami

Hayashi

et al. 2022

Preprint

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Background Rapid and precise prehospital recognition of acute coronary syndrome (ACS) is key to improving clinical outcomes. We tested the hypothesis that the machine learning-based prehospital algorithm shows a high predictive power for predicting ACS. Methods We conducted a multicenter observational prospective study that included 10 participating facilities in an urban area of Japan. The data from consecutive adult patients, identified by emergency medical service (EMS) personnel with suspected ACS, were analyzed. The primary outcomes were binary classification models for ACS prediction based on eXtreme Gradient Boosting (XGBoost). Secondary outcomes were classification models for subcategories of ACS, including acute myocardial infarction (AMI) and ST-segment elevation myocardial infarction (STEMI). We evaluated the model performance based on the area under the receiver operating curve (AUC). Results Of the 555 enrolled patients, 388 (70%) were randomly assigned to a training cohort, and 167 (30%) were placed in a test cohort. The XGBoost model for ACS using 43 features performed well (AUC 0.879 [95% CI 0.815–0.935]) in the test cohort. We repeated the analysis with a limited number of selected features, and the performance of the XGBoost model using 17 features remained high (AUC 0.883 [95% CI 0.820–0.939]) in the test cohort. The XGBoost model for ACS using 17 features had a higher AUC than the other four common prediction models: logistic regression, random forest, a linear support vector machine (SVM), and a radial basis function (RBF) kernel SVM. The XGBoost algorithm for prediction of AMI and STEMI using the same 17 features also resulted in high AUC scores of 0.857 [95% CI 0.788–0.919] and 0.871 [95% CI 0.819–0.925] in the test cohort, respectively. Conclusions We found that the machine learning-based prehospital algorithms showed a high predictive power for predicting ACS.

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“… 22 Demographic features of occlusive MI, along with ECG analysis and potentially biomarkers could combine into a clinical decision support system to help guide clinicians to identify occlusive MI. 23 However, as revealed by latent class analysis, there is no reliable distinguishing features between occlusive and non‐occlusive MI. Before novel innovations are available, serial ECGs provide a potential insight into the dynamic nature of coronary artery occlusion and the need for emergency reperfusion therapy if showing indicative changes such as evolving ST elevation.…”

Section: Discussionmentioning

confidence: 99%

Using latent class analysis to identify clinical features of patients with occlusive myocardial infarction: Preangiogram prediction remains difficult

Knoery

McEwan

Manktelow

et al. 2022

Clinical Cardiology

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Background: Treatment decisions in myocardial infarction (MI) are currently stratified by ST elevation (ST-elevation myocardial infarction [STEMI]) or lack of ST elevation (non-ST elevation myocardial infarction [NSTEMI]) on the electrocardiogram.This arose from the assumption that ST elevation indicated acute coronary artery occlusion (OMI). However, one-quarter of all NSTEMI cases are an OMI, and have a higher mortality. The purpose of this study was to identify features that could help identify OMI.Methods: Prospectively collected data from patients undergoing percutaneous coronary intervention (PCI) was analyzed. Data included presentation characteristics, comorbidities, treatments, and outcomes. Latent class analysis was undertaken, to determine patterns of presentation and history associated with OMI.Results: A total of 1412 patients underwent PCI for acute MI, and 263 were diagnosed as OMI. Compared to nonocclusive MI, OMI patients are more likely to have fewer comorbidities but no difference in cerebrovascular disease and increased acute mortality (4.2% vs. 1.1%; p < .001). Of OMI, 29.5% had delays to their treatment such as immediate reperfusion therapy. With latent class analysis, while clusters of similar patients are observed in the data set, the data available did not usefully identify patients with OMI compared to non-OMI. Conclusion:Features between OMI and STEMI are broadly very similar. However, there was no difference in age and risk of cerebrovascular disease in the OMI/non-OMI group. There are no reliable characteristics therefore for identifying OMI versus non-OMI. Delays to treatment also suggest that OMI patients are still missing out on

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Systematic Review of Clinical Decision Support Systems for Prehospital Acute Coronary Syndrome Identification

Abstract: Supplemental Digital Content is available in the text.

Cited by 17 publications

References 50 publications

Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study

Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study

Prehospital Diagnostic Algorithm for Acute Coronary Syndrome Using Machine Learning: A Prospective Observational Study

Using latent class analysis to identify clinical features of patients with occlusive myocardial infarction: Preangiogram prediction remains difficult

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