Heart rate n-variability (HRnV) and its application to risk stratification of chest pain patients in the emergency department

Abstract Background Chest pain is among the most common presenting complaints in the emergency department (ED). Swift and accurate risk stratification of chest pain patients in the ED may improve patient outcomes and reduce unnecessary costs. Traditional logistic regression with stepwise variable selection has been used to build risk prediction models for ED chest pain patients. In this study, we aimed to investigate if machine learning dimensionality reduction methods can improve performance in deriving risk stratification models. Methods A retrospective analysis was conducted on the data of patients > 20 years old who presented to the ED of Singapore General Hospital with chest pain between September 2010 and July 2015. Variables used included demographics, medical history, laboratory findings, heart rate variability (HRV), and heart rate n-variability (HRnV) parameters calculated from five to six-minute electrocardiograms (ECGs). The primary outcome was 30-day major adverse cardiac events (MACE), which included death, acute myocardial infarction, and revascularization within 30 days of ED presentation. We used eight machine learning dimensionality reduction methods and logistic regression to create different prediction models. We further excluded cardiac troponin from candidate variables and derived a separate set of models to evaluate the performance of models without using laboratory tests. Receiver operating characteristic (ROC) and calibration analysis was used to compare model performance. Results Seven hundred ninety-five patients were included in the analysis, of which 247 (31%) met the primary outcome of 30-day MACE. Patients with MACE were older and more likely to be male. All eight dimensionality reduction methods achieved comparable performance with the traditional stepwise variable selection; The multidimensional scaling algorithm performed the best with an area under the curve of 0.901. All prediction models generated in this study outperformed several existing clinical scores in ROC analysis. Conclusions Dimensionality reduction models showed marginal value in improving the prediction of 30-day MACE for ED chest pain patients. Moreover, they are black box models, making them difficult to explain and interpret in clinical practice.

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Risk stratification of chest pain patients by point-of-care cardiac troponin T and myoglobin measured in the emergency department

Clinica Chimica Acta ◽

10.1016/j.cca.2005.07.035 ◽

2006 ◽

Vol 365 (1-2) ◽

pp. 93-97 ◽

Cited By ~ 13

Author(s):

Jordi Ordóñez-Llanos ◽

Miquel Santaló-Bel ◽

Javier Mercé-Muntañola ◽

Paul O. Collinson ◽

David Gaze ◽

...

Keyword(s):

Emergency Department ◽

Chest Pain ◽

Risk Stratification ◽

Cardiac Troponin ◽

Troponin T ◽

Point Of Care ◽

Cardiac Troponin T ◽

Pain Patients

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Risk stratification of chest pain patients in the emergency department by a nurse utilizing a point of care protocol

European Journal of Emergency Medicine ◽

10.1097/mej.0b013e3282aa4045 ◽

2008 ◽

Vol 15 (1) ◽

pp. 9-15 ◽

Cited By ~ 17

Author(s):

Andrew J. Hamilton ◽

Leslie A. Swales ◽

Johanne Neill ◽

John C. Murphy ◽

Karen M. Darragh ◽

...

Keyword(s):

Emergency Department ◽

Chest Pain ◽

Risk Stratification ◽

Point Of Care ◽

Care Protocol ◽

Pain Patients

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The Impact of B-Type Natriuretic Peptide in Addition to Troponin I, Creatine Kinase-MB, and Myoglobin on the Risk Stratification of Emergency Department Chest Pain Patients With Potential Acute Coronary Syndrome

Annals of Emergency Medicine ◽

10.1016/j.annemergmed.2006.08.024 ◽

2007 ◽

Vol 49 (2) ◽

pp. 153-163 ◽

Cited By ~ 16

Author(s):

Aaron M. Brown ◽

Keara L. Sease ◽

Jennifer L. Robey ◽

Frances S. Shofer ◽

Judd E. Hollander

Keyword(s):

Emergency Department ◽

Acute Coronary Syndrome ◽

Creatine Kinase ◽

Chest Pain ◽

Risk Stratification ◽

Troponin I ◽

Coronary Syndrome ◽

Creatine Kinase Mb ◽

The Impact ◽

Pain Patients

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A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain

Critical Care ◽

10.1186/s13054-016-1367-5 ◽

2016 ◽

Vol 20 (1) ◽

Cited By ~ 13

Author(s):

Micah Liam Arthur Heldeweg ◽

Nan Liu ◽

Zhi Xiong Koh ◽

Stephanie Fook-Chong ◽

Weng Kit Lye ◽

...

Keyword(s):

Emergency Department ◽

Heart Rate ◽

Heart Rate Variability ◽

Cardiovascular Risk ◽

Chest Pain ◽

Risk Stratification ◽

Cardiovascular Risk Stratification ◽

Risk Stratification Model

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Heart Rate n-Variability (HRnV) and Its Application to Risk Stratification of Chest Pain Patients in the Emergency Department

10.1101/738989 ◽

2019 ◽

Author(s):

Nan Liu ◽

Dagang Guo ◽

Zhi Xiong Koh ◽

Andrew Fu Wah Ho ◽

Feng Xie ◽

...

Keyword(s):

Risk Factors ◽

Emergency Department ◽

Heart Rate ◽

Logistic Regression ◽

Chest Pain ◽

Risk Stratification ◽

Roc Analysis ◽

Individual Risk ◽

Stepwise Logistic Regression ◽

Clinical Scores

AbstractBackgroundChest 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) for 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 and had a higher proportion of male patients. Twenty-one conventional HRV and 115 HRnV parameters were calculated. In univariable analysis, eleven HRV parameters and 48 HRnV parameters were significantly associated with 30-day MACE. The multivariable stepwise logistic regression identified 16 predictors that were strongly associated with the 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 at the ED.

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