Performance of heart rate adjusted heart rate variability for risk stratification of sudden cardiac death

Yan, Su-Peng; Song, Xiaoyan; Wei, Liang; Gong, Yushun; Hu, Houyuan; Li, Yongqin

doi:10.1186/s12872-023-03184-0

Cited by 2 publications

(1 citation statement)

References 46 publications

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“…The study examined the relationship between HRV and HR values both in the long and short term. Yan et al demonstrated that most HRV values decreased exponentially with an HR increase, especially in patients with a high risk of sudden cardiac death [3].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Heart Rate Variability Monitoring on Preventing Severe Cardiovascular Events

et al. 2023

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The increase in the incidence of cardiovascular diseases worldwide raises concerns about the urgent need to increase definite measures for the self-determination of different parameters, especially those defining cardiac function. Heart rate variability (HRV) is a non-invasive method used to evaluate autonomic nervous system modulation on the cardiac sinus node, thus describing the oscillations between consecutive electrocardiogram R-R intervals. These fluctuations are undetectable except when using specialized devices, with ECG Holter monitoring considered the gold standard. HRV is considered an independent biomarker for measuring cardiovascular risk and for screening the occurrence of both acute and chronic heart diseases. Also, it can be an important predictive factor of frailty or neurocognitive disorders, like anxiety and depression. An increased HRV is correlated with rest, exercise, and good recovery, while a decreased HRV is an effect of stress or illness. Until now, ECG Holter monitoring has been considered the gold standard for determining HRV, but the recent decade has led to an accelerated development of technology using numerous devices that were created specifically for the pre-hospital self-monitoring of health statuses. The new generation of devices is based on the use of photoplethysmography, which involves the determination of blood changes at the level of blood vessels. These devices provide additional information about heart rate (HR), blood pressure (BP), peripheral oxygen saturation (SpO2), step counting, physical activity, and sleep monitoring. The most common devices that have this technique are smartwatches (used on a large scale) and chest strap monitors. Therefore, the use of technology and the self-monitoring of heart rate and heart rate variability can be an important first step in screening cardiovascular pathology and reducing the pressure on medical services in a hospital. The use of telemedicine can be an alternative, especially among elderly patients who are associated with walking disorders, frailty, or neurocognitive disorders.

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Section: Introductionmentioning

confidence: 99%

The Impact of Heart Rate Variability Monitoring on Preventing Severe Cardiovascular Events

et al. 2023

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Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU

Lee,

Yang,

Ryu

et al. 2023

npj Digit. Med.

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Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients. A light gradient boosting machine (LGBM) algorithm was used to develop the proposed model for predicting in-hospital cardiac arrest within 0.5–24 h. The LGBM model using 33 HRV measures achieved an area under the receiver operating characteristic curve of 0.881 (95% CI: 0.875–0.887) and an area under the precision-recall curve of 0.104 (95% CI: 0.093–0.116). The most important feature was the baseline width of the triangular interpolation of the RR interval histogram. As our model uses only ECG data, it can be easily applied in clinical practice.

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Performance of heart rate adjusted heart rate variability for risk stratification of sudden cardiac death

Cited by 2 publications

References 46 publications

The Impact of Heart Rate Variability Monitoring on Preventing Severe Cardiovascular Events

The Impact of Heart Rate Variability Monitoring on Preventing Severe Cardiovascular Events

Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU

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