Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score

Abstract

IntroductionA key aim of triage is to identify those with high risk of cardiac arrest, as they require intensive monitoring, resuscitation facilities, and early intervention. We aim to validate a novel machine learning (ML) score incorporating heart rate variability (HRV) for triage of critically ill patients presenting to the emergency department by comparing the area under the curve, sensitivity and specificity with the modified early warning score (MEWS).MethodsWe conducted a prospective observational study of critically ill patients (Patient Acuity Category Scale 1 and 2) in an emergency department of a tertiary hospital. At presentation, HRV parameters generated from a 5-minute electrocardiogram recording are incorporated with age and vital signs to generate the ML score for each patient. The patients are then followed up for outcomes of cardiac arrest or death.ResultsFrom June 2006 to June 2008 we enrolled 925 patients. The area under the receiver operating characteristic curve (AUROC) for ML scores in predicting cardiac arrest within 72 hours is 0.781, compared with 0.680 for MEWS (difference in AUROC: 0.101, 95% confidence interval: 0.006 to 0.197). As for in-hospital death, the area under the curve for ML score is 0.741, compared with 0.693 for MEWS (difference in AUROC: 0.048, 95% confidence interval: -0.023 to 0.119). A cutoff ML score ≥ 60 predicted cardiac arrest with a sensitivity of 84.1%, specificity of 72.3% and negative predictive value of 98.8%. A cutoff MEWS ≥ 3 predicted cardiac arrest with a sensitivity of 74.4%, specificity of 54.2% and negative predictive value of 97.8%.ConclusionWe found ML scores to be more accurate than the MEWS in predicting cardiac arrest within 72 hours. There is potential to develop bedside devices for risk stratification based on cardiac arrest prediction.

Autonomic Nervous System Activity as Risk Predictor in the Medical Emergency Department

“…However, only very few studies tested the clinical usefulness of HRV as clinical tool for risk prediction in a medical ED (33). DC differs from standard measures of HRV in several aspects.…”

Heart rate variability predicts 30-day all-cause mortality in intensive care units

“…1,6,11 This may explain differing performances with respect to predictive ability over other frequency domain parameters in the intensive care setting. 3 HRV is increasingly gaining popularity as a predictor of outcome in a variety of clinical environments, including trauma patients, 12 critically ill emergency department 4 patients, 13 septic patients on admission to ED, 14 and the haemodynamically stable trauma patient. 15 It has also been used to predict outcomes such as hypotension during obstetric spinal hypotension.…”

Risk Scoring for Prediction of Acute Cardiac Complications from Imbalanced Clinical Data

“…ECG signals were sampled at a rate of 125 Hz and the processing of raw data to obtain the HRV parameters was done using the LABVIEW (Version 8.6, National Instruments, Austin, TX, USA) interface embedded with MATLAB (R2009a, The MathWorks, Natick, MA, USA) scripts. A detailed description of data acquisition and signal processing can be found in our previous works [15], [19], in which a threshold-plus-derivative method was used to detect the QRS complexes, and all ectopic and nonsinus beats were excluded in accordance with the guidelines outlined by the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology [20]. In this study, a total of 16 time domain and frequency domain HRV parameters were derived, which are elaborated in Table I. The 12-lead ECG was also measured at the ED with PageWriter TC Series Cardiograph (Philips, Amsterdam, Netherlands) and parameters were either automatically derived by the device or manually calculated by a doctor.…”