Improving the Prediction of Mortality and the Need for Life-Saving Interventions in Trauma Patients Using Standard Vital Signs With Heart-Rate Variability and Complexity

Abstract Background Triage trauma scores are utilised to determine patient disposition, interventions and prognostication in the care of trauma patients. Heart rate variability (HRV) and heart rate complexity (HRC) reflect the autonomic nervous system and are derived from electrocardiogram (ECG) analysis. In this study, we aimed to develop a model incorporating HRV and HRC, to predict the need for life-saving interventions (LSI) in trauma patients, within 24 h of emergency department presentation. Methods We included adult trauma patients (≥ 18 years of age) presenting at the emergency department of Singapore General Hospital between October 2014 and October 2015. We excluded patients who had non-sinus rhythms and larger proportions of artefacts and/or ectopics in ECG analysis. We obtained patient demographics, laboratory results, vital signs and outcomes from electronic health records. We conducted univariate and multivariate analyses for predictive model building. Results Two hundred and twenty-five patients met inclusion criteria, in which 49 patients required LSIs. The LSI group had a higher proportion of deaths (10, 20.41% vs 1, 0.57%, p < 0.001). In the LSI group, the mean of detrended fluctuation analysis (DFA)-α1 (1.24 vs 1.12, p = 0.045) and the median of DFA-α2 (1.09 vs 1.00, p = 0.027) were significantly higher. Multivariate stepwise logistic regression analysis determined that a lower Glasgow Coma Scale, a higher DFA-α1 and higher DFA-α2 were independent predictors of requiring LSIs. The area under the curve (AUC) for our model (0.75, 95% confidence interval, 0.66–0.83) was higher than other scoring systems and selected vital signs. Conclusions An HRV/HRC model outperforms other triage trauma scores and selected vital signs in predicting the need for LSIs but needs to be validated in larger patient populations.

Download Full-text

Abstract 339: A Novel Continuous Heart Rate Variability Based Analytic to Predict Clinical Hemodynamic Instability Requiring Intervention

Circulation ◽

10.1161/circ.138.suppl_2.339 ◽

2018 ◽

Vol 138 (Suppl_2) ◽

Author(s):

Ashwin Belle ◽

Kevin R Ward ◽

Bryce Benson ◽

Mark Salamango ◽

Fadi Islim

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Heart Rate Variability ◽

Lead Time ◽

Vital Signs ◽

Hemodynamic Instability ◽

Lead Times ◽

Compensatory Mechanisms ◽

Predictive Values ◽

Life Saving

Background: Sudden in-hospital hemodynamic instability (HI) due to cardiovascular and/or cardiorespiratory distress is a common occurrence. Causes can include hemorrhage, sepsis, pneumonia, heart failure, and others. Due to the body’s compensatory mechanisms, heart and respiratory rate, and blood pressure can be late indicators of HI. When detected late or left unrecognized HI can lead to complications and even death. Heart rate variability (HRV) has been demonstrated to reflect the status of the autonomic nervous system with changes in HRV linked to HI. However, to date, HRV has not demonstrated sufficient accuracies in adults to warrant widespread adoption. We have developed a novel nonlinear single lead ECG HRV analytic based on signal processing and machine learning features specific to HI physiology. Objective: Validate the capability, accuracy, and lead times of the HRV analytic to predict HI in hospitalized patients who were subjects of Rapid Response Team (RRT) activations. Methods: We retrospectively analyzed 4483 hours of ECG data from 22 RRT patient cases (16 male, 6 female) using the previously developed HRV analytic. A multi-clinician review adjudicated the occurrence of HI and need for intervention. The prediction lead time prior to the RRT call was calculated for these cases. Results: Of the 22 RRT cases, 13 calls were due to HI requiring a range of life-saving interventions, and 9 RRT cases for reasons not associated with HI and requiring no life-saving interventions. The analytic correctly distinguished between HI vs. non-HI RRT calls with 100% accuracy thus displaying 100% positive and negative predictive values. In the HI cases, the analytic detected HI with a median lead time of 7.7 hours prior to the RRT call with a range of 14 minutes to 38.4 hours. Conclusion: In this RRT cohort, a novel HRV analytic developed through machine learning based on HI physiology demonstrated its potential to forecast HI well before vital signs and other factors led to RRT activation resulting in life-saving interventions. These substantial lead times, as well as the ability to distinguish HI from non-HI RRT calls, may lead to the ability to reduce HI and improve outcomes while reducing unnecessary RRT activations.

Download Full-text

Utility of Vital Signs, Heart Rate Variability and Complexity, and Machine Learning for Identifying the Need for Lifesaving Interventions in Trauma Patients

Shock ◽

10.1097/shk.0000000000000186 ◽

2014 ◽

Vol 42 (2) ◽

pp. 108-114 ◽

Cited By ~ 23

Author(s):

Nehemiah T. Liu ◽

John B. Holcomb ◽

Charles E. Wade ◽

Mark I. Darrah ◽

Jose Salinas

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Heart Rate Variability ◽

Vital Signs ◽

Trauma Patients

Download Full-text

Adrenal Insufficiency, Heart Rate Variability, and Complex Biologic Systems: A Study of 1,871 Critically III Trauma Patients

Yearbook of Critical Care Medicine ◽

10.1016/s0734-3299(08)70603-8 ◽

2008 ◽

Vol 2008 ◽

pp. 250-252

Author(s):

D.J. Dries

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Adrenal Insufficiency ◽

Trauma Patients

Download Full-text

Integrating heart rate variability, vital signs, electrocardiogram, and troponin to triage chest pain patients in the ED

The American Journal of Emergency Medicine ◽

10.1016/j.ajem.2017.07.054 ◽

2018 ◽

Vol 36 (2) ◽

pp. 185-192 ◽

Cited By ~ 2

Author(s):

Jeffrey Tadashi Sakamoto ◽

Nan Liu ◽

Zhi Xiong Koh ◽

Dagang Guo ◽

Micah Liam Arthur Heldeweg ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Chest Pain ◽

Vital Signs ◽

Pain Patients

Download Full-text

Clinical Applications of Heart Rate Variability in the Triage and Assessment of Traumatically Injured Patients

Anesthesiology Research and Practice ◽

10.1155/2011/416590 ◽

2011 ◽

Vol 2011 ◽

pp. 1-8 ◽

Cited By ~ 18

Author(s):

Mark L. Ryan ◽

Chad M. Thorson ◽

Christian A. Otero ◽

Thai Vu ◽

Kenneth G. Proctor

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Continuous Monitoring ◽

Trauma Patients ◽

Clinical Implementation ◽

Patient Assessment ◽

Prehospital Triage ◽

Key Issues ◽

Medical Disciplines ◽

Injured Patients

Heart rate variability (HRV) is a method of physiologic assessment which uses fluctuations in the RR intervals to evaluate modulation of the heart rate by the autonomic nervous system (ANS). Decreased variability has been studied as a marker of increased pathology and a predictor of morbidity and mortality in multiple medical disciplines. HRV is potentially useful in trauma as a tool for prehospital triage, initial patient assessment, and continuous monitoring of critically injured patients. However, several technical limitations and a lack of standardized values have inhibited its clinical implementation in trauma. The purpose of this paper is to describe the three analytical methods (time domain, frequency domain, and entropy) and specific clinical populations that have been evaluated in trauma patients and to identify key issues regarding HRV that must be explored if it is to be widely adopted for the assessment of trauma patients.

Download Full-text