scholarly journals Predictive values of heart rate variability, deceleration and acceleration capacity of heart rate in post‐infarction patients with LVEF ≥35%

2020 ◽  
Vol 25 (6) ◽  
Author(s):  
Xiang Liu ◽  
Li Xiang ◽  
Guangming Tong
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Predictive Values ◽  
Acceleration Capacity

scholarly journals Sensitivity, specificity and predictive values of linear and nonlinear indices of heart rate variability in stable angina patients

2012 ◽  
Vol 5 (1) ◽  
pp. 31 ◽  
Author(s):  
Flávio Pivatelli ◽  
Marcio dos Santos ◽  
Gislaine Fernandes ◽  
Marcio Gatti ◽  
Luiz de Abreu ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Stable Angina ◽  
Predictive Values ◽  
Linear And Nonlinear ◽  
Sensitivity Specificity

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.

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