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

Shock ◽  
2014 ◽  
Vol 42 (2) ◽  
pp. 108-114 ◽  
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

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

Circulation ◽  
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.

scholarly journals Development of a heart rate variability and complexity model in predicting the need for life-saving interventions amongst trauma patients

2019 ◽  
Vol 7 ◽  
Author(s):  
Aravin Kumar ◽  
Nan Liu ◽  
Zhi Xiong Koh ◽  
Jayne Jie Yi Chiang ◽  
Yuda Soh ◽  
...  
Keyword(s):  
Emergency Department ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vital Signs ◽  
Fluctuation Analysis ◽  
Trauma Patients ◽  
Ecg Analysis ◽  
Patient Disposition ◽  
Life Saving ◽  
Trauma Scores

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.

scholarly journals Trends in Heart-Rate Variability Signal Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Syem Ishaque ◽  
Naimul Khan ◽  
Sri Krishnan
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Well Being ◽  
Good Health ◽  
Machine Learning Techniques ◽  
Video Gaming ◽  
Clinical Events ◽  
Learning Techniques

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

scholarly journals Preoperative Heart Rate Variability During Sleep Predicts Vagus Nerve Stimulation Outcome Better in Patients With Drug-Resistant Epilepsy

2021 ◽  
Vol 12 ◽  
Author(s):  
Xi Fang ◽  
Hong-Yun Liu ◽  
Zhi-Yan Wang ◽  
Zhao Yang ◽  
Tung-Yang Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Outcome Prediction ◽  
Healthy Controls ◽  
Drug Resistant ◽  
Sleep State ◽  
Drug Resistant Epilepsy

Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices.Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model.Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1).Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

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

2018 ◽  
Vol 36 (2) ◽  
pp. 185-192 ◽  
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
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