ArticleBMC cardiovascular disorders2020
Heart rate n-variability (HRnV) and its application to risk stratification of chest pain patients in the emergency department.
Article in BMC cardiovascular disorders, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 24 citations in OpenAlex.
- Machine learning-based early warning system for hemodynamic deterioration in cardiovascular ICU patients: a bidirectional cross-validation study.Frontiers in cardiovascular medicine · 2025Article
- Association between heart rate variability and ECG changes in on-duty prehospital physicians.Frontiers in physiology · 2025Article
- Predicting angiographic coronary artery disease using machine learning and high-frequency QRS.BMC medical informatics and decision making · 2024Article
- Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study.Critical care (London, England) · 2024Article
- Artificial intelligence and machine learning in prehospital emergency care: A scoping review.iScience · 2023Article
- Adding heart rate n-variability (HRnV) to clinical assessment potentially improves prediction of serious bacterial infections in young febrile infants at the emergency department: a prospective observational study.Annals of translational medicine · 2023Article
- Benchmarking emergency department prediction models with machine learning and public electronic health records.Scientific data · 2022Article
- A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department.BMC emergency medicine · 2021Article
- Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions.JAMA network open · 2021Article
- A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification.BMC medical informatics and decision making · 2021Article
- Utilizing machine learning dimensionality reduction for risk stratification of chest pain patients in the emergency department.BMC medical research methodology · 2021Article
- Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department.PloS one · 2021Article
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Authors and funding
12 authors at 8 institutions in 3 countries.
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Abstract
backgroundChest pain is one of the most common complaints among patients presenting to the emergency department (ED). Causes of chest pain can be benign or life threatening, making accurate risk stratification a critical issue in the ED. In addition to the use of established clinical scores, prior studies have attempted to create predictive models with heart rate variability (HRV). In this study, we proposed heart rate n-variability (HRnV), an alternative representation of beat-to-beat variation in electrocardiogram (ECG), and investigated its association with major adverse cardiac events (MACE) in ED patients with chest pain.
methodsWe conducted a retrospective analysis of data collected from the ED of a tertiary hospital in Singapore between September 2010 and July 2015. Patients > 20 years old who presented to the ED with chief complaint of chest pain were conveniently recruited. Five to six-minute single-lead ECGs, demographics, medical history, troponin, and other required variables were collected. We developed the HRnV-Calc software to calculate HRnV parameters. The primary outcome was 30-day MACE, which included all-cause death, acute myocardial infarction, and revascularization. Univariable and multivariable logistic regression analyses were conducted to investigate the association between individual risk factors and the outcome. Receiver operating characteristic (ROC) analysis was performed to compare the HRnV model (based on leave-one-out cross-validation) against other clinical scores in predicting 30-day MACE.
resultsA total of 795 patients were included in the analysis, of which 247 (31%) had MACE within 30 days. The MACE group was older, with a higher proportion being male patients. Twenty-one conventional HRV and 115 HRnV parameters were calculated. In univariable analysis, eleven HRV and 48 HRnV parameters were significantly associated with 30-day MACE. The multivariable stepwise logistic regression identified 16 predictors that were strongly associated with MACE outcome; these predictors consisted of one HRV, seven HRnV parameters, troponin, ST segment changes, and several other factors. The HRnV model outperformed several clinical scores in the ROC analysis.
conclusionsThe novel HRnV representation demonstrated its value of augmenting HRV and traditional risk factors in designing a robust risk stratification tool for patients with chest pain in the ED.
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