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.

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.

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 343: Prediction of Lifesaving Interventions Using Changes in Vital Signs from Prehospital Scene to Emergency Department

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Jorge H Mena Munoz ◽  
Ashley Petersen ◽  
Francis X Guyette
Keyword(s):  
Emergency Department ◽  
Decision Trees ◽  
Recursive Partitioning ◽  
Vital Signs ◽  
Large Change ◽  
Predictive Performance ◽  
Classification Performance ◽  
Secondary Outcome ◽  
Classification Error ◽  
Trauma Patients

Objective: We investigate whether changes in vital signs between the prehospital scene and emergency department (ED) can be used to develop triage tools to predict the need for life-saving interventions (LSI) and survival in trauma patients. Methods: We analyzed a prospective cohort with any prehospital systolic blood pressure (SBP) ≤ 90 mmHg or Glasgow Coma Scale ≤ 8 who were admitted to an ED at 11 sites of the Resuscitation Outcomes Consortium. The primary outcome was the need for in-hospital LSI (e.g. invasive airway management, invasive bleeding control, blood transfusion, craniotomy, cardiopulmonary resuscitation). Secondary outcome was survival to hospital discharge. Changes in heart rate (HR), SBP, shock index (SI), and respiratory rate (RR) from first prehospital assessment to first ED assessment were considered as predictors in addition to sex, age, mechanism of injury, trauma center level, duration of transport, type of transport, and prehospital fluid volume. Decision trees for each outcome were developed using binary recursive partitioning with predictive performance measured using sensitivity, specificity, and classification error. Results: 5625 subjects were included in our analysis with 49% in need of LSI and 21% dying prior to discharge. Patients needing an LSI tended to either: (1) have an increasing SI (delta ≥ 0.22), (2) have a decreasing SI (delta < 0.22) and >500 mL prehospital fluids, or (3) have a decreasing SI (delta < 0.22), ≤500 mL prehospital fluids, and large change in RR (delta ≥ 9.5 or delta < -7.5). Those surviving to discharge tended to either: (1) have a decreasing SI (delta < 0.57) and a HR that did not decrease greatly (delta > -47) or (2) have an increase in SI (0.57 ≤ delta < 1) and a declining RR (delta < 5). LSI tree had a sensitivity of 58.7% and specificity of 63.3%. Survival tree had sensitivity of 96.2% and specificity of 21.3%. Conclusion: Though the decision trees were constructed with the best data in terms of initial triage and early secondary triage, the classification performance was limited. This highlights the difficulties of developing vital sign based triage tools to predict the need for LSI and survival.

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