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

Critical Care ◽  
2016 ◽  
Vol 20 (1) ◽  
Author(s):  
Micah Liam Arthur Heldeweg ◽  
Nan Liu ◽  
Zhi Xiong Koh ◽  
Stephanie Fook-Chong ◽  
Weng Kit Lye ◽  
...  
Author(s):  
Jeremy Zhenwen Pong ◽  
Stephanie Fook-Chong ◽  
Zhi Xiong Koh ◽  
Mas’uud Ibnu Samsudin ◽  
Takashi Tagami ◽  
...  

The emergency department (ED) serves as the first point of hospital contact for many septic patients, where risk-stratification would be invaluable. We devised a combination model incorporating demographic, clinical, and heart rate variability (HRV) parameters, alongside individual variables of the Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation II (APACHE II), and Mortality in Emergency Department Sepsis (MEDS) scores for mortality risk-stratification. ED patients fulfilling systemic inflammatory response syndrome criteria were recruited. National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), quick SOFA (qSOFA), SOFA, APACHE II, and MEDS scores were calculated. For the prediction of 30-day in-hospital mortality, combination model performed with an area under the receiver operating characteristic curve of 0.91 (95% confidence interval (CI): 0.88–0.95), outperforming NEWS (0.70, 95% CI: 0.63–0.77), MEWS (0.61, 95% CI 0.53–0.69), qSOFA (0.70, 95% CI 0.63–0.77), SOFA (0.74, 95% CI: 0.67–0.80), APACHE II (0.76, 95% CI: 0.69–0.82), and MEDS scores (0.86, 95% CI: 0.81–0.90). The combination model had an optimal sensitivity and specificity of 91.4% (95% CI: 81.6–96.5%) and 77.9% (95% CI: 72.6–82.4%), respectively. A combination model incorporating clinical, HRV, and disease severity score variables showed superior predictive ability for the mortality risk-stratification of septic patients presenting at the ED.


2018 ◽  
Vol 54 (3) ◽  
pp. 273-280 ◽  
Author(s):  
Jeffrey Tadashi Sakamoto ◽  
Nan Liu ◽  
Zhi Xiong Koh ◽  
Dagang Guo ◽  
Micah Liam Arthur Heldeweg ◽  
...  

2012 ◽  
Vol 21 (6) ◽  
pp. 727-738 ◽  
Author(s):  
David M Leistner ◽  
Jens Klotsche ◽  
Sylvia Palm ◽  
Lars Pieper ◽  
Günter K Stalla ◽  
...  

2019 ◽  
Author(s):  
Nan Liu ◽  
Dagang Guo ◽  
Zhi Xiong Koh ◽  
Andrew Fu Wah Ho ◽  
Feng Xie ◽  
...  

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.


2018 ◽  
Author(s):  
Chu En Ting ◽  
Nan Liu ◽  
Zhi Xiong Koh ◽  
Dagang Guo ◽  
Janson Cheng Ji NG ◽  
...  

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