ArticleNeurocritical care2020
Heart Rate Variability as a Biomarker of Neurocardiogenic Injury After Subarachnoid Hemorrhage.
Article in Neurocritical care, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Heart rate variability as a predictor of stroke course, functional outcome, and medical complications: A systematic review.Frontiers in physiology · 2023Pooled it
- Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies.BioMed research international · 2022Pooled it
- Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives.Sovremennye tekhnologii v meditsine · 2021Pooled it
- Trial
- Association of Troponin T levels and functional outcome 3 months after subarachnoid hemorrhage.Scientific reports · 2021Trial
- Associations Between Delayed Cerebral Ischemia in Spontaneous Subarachnoid Hemorrhage and Dysfunction of Autonomic Cardiovascular Modulation Compared to Transcranial Doppler Ultrasound Findings.Diagnostics (Basel, Switzerland) · 2026Article
- The effect of transcutaneous auricular vagus nerve stimulation on cardiovascular function in subarachnoid hemorrhage patients: a safety study.medRxiv : the preprint server for health sciences · 2024Article
- Future of neurocritical care: Integrating neurophysics, multimodal monitoring, and machine learning.World journal of critical care medicine · 2024Review
- Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study.Critical care (London, England) · 2024Article
- Development of a predictive nomogram for 28-day mortality risk in non-traumatic or post-traumatic subarachnoid hemorrhage patients.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
- Aberrant brain-heart coupling is associated with the severity of post cardiac arrest brain injury.Annals of clinical and translational neurology · 2024Article
- Heart Rate Variability for Outcome Prediction in Intracerebral and Subarachnoid Hemorrhage: A Systematic Review.Journal of clinical medicine · 2023Review
- Heart rate variability and adrenal size provide clues to sudden cardiac death in hospitalized COVID-19 patients.Journal of critical care · 2022Article
- Autonomic Nervous System Activity during Refractory Rise in Intracranial Pressure.Journal of neurotrauma · 2021Article
- Continuous heart rate variability and electroencephalography monitoring in severe acute brain injury: a preliminary study.Acute and critical care · 2021Article
- Dynamic change of heart rate in the acute phase and clinical outcomes after intracerebral hemorrhage: a cohort study.Journal of intensive care · 2021Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
backgroundThe objective of this study was to examine whether heart rate variability (HRV) measures can be used to detect neurocardiogenic injury (NCI).
methodsThree hundred and twenty-six consecutive admissions with aneurysmal subarachnoid hemorrhage (SAH) met criteria for the study. Of 326 subjects, 56 (17.2%) developed NCI which we defined by wall motion abnormality with ventricular dysfunction on transthoracic echocardiogram or cardiac troponin-I > 0.3 ng/mL without electrocardiogram evidence of coronary artery insufficiency. HRV measures (in time and frequency domains, as well as nonlinear technique of detrended fluctuation analysis) were calculated over the first 48 h. We applied longitudinal multilevel linear regression to characterize the relationship of HRV measures with NCI and examine between-group differences at baseline and over time.
resultsThere was decreased vagal activity in NCI subjects with a between-group difference in low/high frequency ratio (β 3.42, SE 0.92, p = 0.0002), with sympathovagal balance in favor of sympathetic nervous activity. All time-domain measures were decreased in SAH subjects with NCI. An ensemble machine learning approach translated these measures into a classification tool that demonstrated good discrimination using the area under the receiver operating characteristic curve (AUROC 0.82), the area under precision recall curve (AUPRC 0.75), and a correct classification rate of 0.81.
conclusionsHRV measures are significantly associated with our label of NCI and a machine learning approach using features derived from HRV measures can classify SAH patients that develop NCI.
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