A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain.
Risk stratification models can be employed at the emergency department (ED) to evaluate patient prognosis and guide choice of treatment. We derived and validated a new cardiovascular risk stratification model comprising vital signs, heart rate variability (HRV) parameters, and demographic and electrocardiogram (ECG) variables.



We conducted a single-center, observational cohort study of patients presenting to the ED with chest pain. All patients above 21 years of age and in sinus rhythm were eligible. ECGs were collected and evaluated for 12-lead ECG abnormalities. Routine monitoring ECG data were processed to obtain HRV parameters. Vital signs and demographic data were obtained from electronic medical records. Thirty-day major adverse cardiac events (MACE) were the primary endpoint, including death, acute myocardial infarction, and revascularization. Candidate variables were identified using univariate analysis; the model for the final risk score was derived by multivariable logistic regression. We compared the performance of the new model with that of the thrombolysis in myocardial infarct (TIMI) score using receiver operating characteristic (ROC) analysis.



In total, 763 patients were included in this study; 254 (33 %) met the primary endpoint, the mean age was 60 (σ = 13) years, and the majority was male (65 %). Nineteen candidate predictors were entered into the multivariable model for backward variable elimination. The final model contained 10 clinical variables, including age, gender, heart rate, three HRV parameters (average R-to-R interval (RR), triangular interpolation of normal-to-normal (NN) intervals, and high-frequency power), and four 12-lead ECG variables (ST elevation, ST depression, Q wave, and QT prolongation). Our proposed model outperformed the TIMI score for prediction of MACE (area under the ROC curve 0.780 versus 0.653). At the cutoff score of 9 (range 0-37), our model had sensitivity of 0.709 (95 % CI 0.653, 0.765), specificity of 0.674 (95 % CI 0.633, 0.715), positive predictive value of 0.520 (95 % CI 0.468, 0.573), and negative predictive value of 0.823 (95 % CI 0.786, 0.859).



A non-invasive and objective ECG- and HRV-based risk stratification tool performed well against the TIMI score, but future research warrants use of an external validation cohort.
University of Groningen, Groningen, Netherlands.
Micah Liam Arthur
ML
Heldeweg
Department of Emergency Medicine, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore. liu.nan@sgh.com.sg.|Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore. liu.nan@sgh.com.sg.
Nan
N
Liu
Department of Emergency Medicine, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore.
Zhi Xiong
ZX
Koh
Department of Clinical Research, Singapore General Hospital, Singapore, Singapore.
Stephanie
S
Fook-Chong
Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
Weng Kit
WK
Lye
Department of Internal Medicine, University Medical Centre Groningen, Groningen, Netherlands.
Mark
M
Harms
Department of Emergency Medicine, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore.|Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
Marcus Eng Hock
ME
Ong
D000368:Aged; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000401:mortality; D015331:Cohort Studies; D003661:Decision Support Techniques; D004562:Electrocardiography / Q000379:methods*; D004636:Emergency Service, Hospital / Q000458:organization & administration; D005260:Female; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D063868:Patient Outcome Assessment; D011379:Prognosis; D011446:Prospective Studies; D018570:Risk Assessment / Q000379:methods* / Q000592:standards; D012846:Singapore
12-Lead ECG; Chest pain; Emergency department; Heart rate variability; Risk stratification