Heart Rate Variability during Periods of Low Blood Pressure as a Predictor of Short-Term Outcome in Preterms

Semenova, Oksana; Carra, Giorgia; Lightbody, Gordon; Boylan, Geraldine B.; Dempsey, Eugene M.

doi:10.1109/embc.2018.8513600

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…Most frequently, however, articles reported using some other non-linear parameters (n=48; 16.8%) (table 3) such as: DFA RMS1 and/or RMS2, [72][73][74][75][76][77][78][79][80][81][82][83] Shannon entropy, 28 36 43 44 46 84-87 other entropies, 25 28 48 84 86-89 deceleration/acceleration capacity 24 25 86 90-93 and numerous (n=26) other combinations of measures, indices, exponents and dimensions of various chaos, fractality, sample asymmetry, correlations and graphical analyses. 21 28 30 36 37 48 72 84-87 [93][94][95][96][97][98][99][100][101][102][103][104][105][106][107] Heart rate characteristics N=25 articles reported using HRC (table 3). Most of them (n=20) come from within the same group of researchers.…”

Section: Non-linear Hrvmentioning

confidence: 99%

Neonatal heart rate variability: a contemporary scoping review of analysis methods and clinical applications

et al. 2021

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BackgroundNeonatal heart rate variability (HRV) is widely used as a research tool. However, HRV calculation methods are highly variable making it difficult for comparisons between studies.ObjectivesTo describe the different types of investigations where neonatal HRV was used, study characteristics, and types of analyses performed.Eligibility criteriaHuman neonates ≤1 month of corrected age.Sources of evidenceA protocol and search strategy of the literature was developed in collaboration with the McGill University Health Center’s librarians and articles were obtained from searches in the Biosis, Cochrane, Embase, Medline and Web of Science databases published between 1 January 2000 and 1 July 2020.Charting methodsA single reviewer screened for eligibility and data were extracted from the included articles. Information collected included the study characteristics and population, type of HRV analysis used (time domain, frequency domain, non-linear, heart rate characteristics (HRC) parameters) and clinical applications (physiological and pathological conditions, responses to various stimuli and outcome prediction).ResultsOf the 286 articles included, 171 (60%) were small single centre studies (sample size <50) performed on term infants (n=136). There were 138 different types of investigations reported: physiological investigations (n=162), responses to various stimuli (n=136), pathological conditions (n=109) and outcome predictor (n=30). Frequency domain analyses were used in 210 articles (73%), followed by time domain (n=139), non-linear methods (n=74) or HRC analyses (n=25). Additionally, over 60 different measures of HRV were reported; in the frequency domain analyses alone there were 29 different ranges used for the low frequency band and 46 for the high frequency band.ConclusionsNeonatal HRV has been used in diverse types of investigations with significant lack of consistency in analysis methods applied. Specific guidelines for HRV analyses in neonates are needed to allow for comparisons between studies.

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Section: Non-linear Hrvmentioning

confidence: 99%

Neonatal heart rate variability: a contemporary scoping review of analysis methods and clinical applications

et al. 2021

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“…In infants, RMSSD correlated with several outcomes in both the short and the long term: ANS stage of development in the case of prematurity (Hoyer et al, 2009;Lucchini et al, 2016;Aye et al, 2018;Schneider et al, 2018), late-onset sepsis (Joshi et al, 2020), extubation success (Latremouille et al, 2018), recovering after admission in intensive care units (Marsillio et al, 2019), pain behavior (Weissman et al, 2012), short-term neurological development when measured during low blood pressure episodes (Semenova et al, 2018), and later neurological development at 2 years (Dimitrijević et al, 2016). RMSSD showed also to change according to the incubator temperature, thus revealing how infant's HRV changes due to environmental temperature (Stéphan-Blanchard et al, 2013).…”

Section: The Future Perspectives For the Clinical Use Of Hrv In The Nmentioning

confidence: 99%

“…Speaking of which, an ML algorithm based on boosted decision trees increased the predictive power of HRV during episodes of low mean blood pressure in preterm infants with a GA between 23 and 31 weeks. Although good results were obtained even with single metrics-among time, frequency, and non-linear ones, RMSSD gave the best area under the curve (0.87)-combining all the metrics through the mentioned algorithm gave an area under the curve of 0.97 for predicting short-term neurological outcomes (i.e., grade III/IV intraventricular hemorrhage or cystic periventricular leukomalacia, NEC, bronchopulmonary dysplasia, infection, and retinopathy) (Semenova et al, 2018).…”

Section: Hrv As An Index To Monitor Clinical Improvementmentioning

confidence: 99%

“…To overcome these limitations, multivariate analyses were introduced—e.g., continuous individual multiorgan variability analysis (CIMVA) ( Seely et al, 2011 ; Frasch et al, 2014 )—, and many researchers started to rely on ML and AI algorithms ( Semenova et al, 2018 ; Vassar et al, 2020 ). As a consequence, research begun to integrate various metrics—as shown by the HeRO monitor, whose HRC index derives from a logistic regression applied on the standard deviation of the RR intervals, sample asymmetry, and SampEn and aims to detect pathological heart decelerations to predict the risk of sepsis within 24 h ( Fairchild and O’Shea, 2010 )—, to combine measurements from different organs for predicting clinical conditions ( Kumar et al, 2020 ), and to define models able to describe HRV as a phenomenon influenced by the whole organism ( Frasch, 2020 ).…”

Section: The Ans Physiology Underlying Hrv and Its Use In The Neonatamentioning

confidence: 99%

“…For this purpose, modern machine learning (ML) and artificial intelligence (AI) algorithms could play a crucial role: through their computational power, they could define models capable of managing the complex physiological interactions between HRV, ANS, and the whole organism, thus boosting our ability to predict the infant's prognosis. Indeed, we already have experiences about the clinical usefulness of ML in both neonatology (Semenova et al, 2018;Ostojic et al, 2020) and HRV analysis (Chiew et al, 2019;Lin et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

Chiera¹,

Cerritelli²,

Casini³

et al. 2020

Front. Neurosci.

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Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before they occur. While specific treatments are possible, they are often timeconsuming, invasive, or painful, with detrimental effects for the development of the infant. In the last 40 years, heart rate variability (HRV) has emerged as a noninvasive measurement to monitor newborns and infants, but it still is underused. Hence, the present paper aims to review the utility of HRV in neonatology and the instruments available to assess it, showing how HRV could be an innovative tool in the years to come. When continuously monitored, HRV could help assess the baby's overall wellbeing and neurological development to detect stress-/painrelated behaviors or pathological conditions, such as respiratory distress syndrome and hyperbilirubinemia, to address when to perform procedures to reduce the baby's stress/pain and interventions, such as therapeutic hypothermia, and to avoid severe complications, such as sepsis and necrotizing enterocolitis, thus reducing mortality. Based on literature and previous experiences, the first step to efficiently introduce HRV in the NICUs could consist in a monitoring system that uses photoplethysmography, which is low-cost and non-invasive, and displays one or a few metrics with good clinical utility. However, to fully harness HRV clinical potential and to greatly improve neonatal care, the monitoring systems will have to rely on modern bioinformatics (machine learning and artificial intelligence algorithms), which could easily integrate infant's HRV metrics, vital signs, and especially past history, thus elaborating models capable to efficiently monitor and predict the infant's clinical conditions. For this reason, hospitals and institutions will have to establish tight collaborations between the obstetric, neonatal, and pediatric departments: this way, healthcare would truly improve in every stage of the perinatal period (from conception to the first years of life), since information about patients' health would flow freely among different professionals, and high-quality research could be performed integrating the data recorded in those departments.

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Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review

Rahman,

Brankovic,

Tracy

et al. 2024

Interact J Med Res

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Background: Computational signal pre-processing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility which will drive clinical adoption.Objective: This review focuses on the Neonatal intensive care unit (NICU) setting and summarises the state-of-the-art computational methods used for pre-processing neonatal clinical physiological signals for the development of machine learning models in predicting the risk of adverse outcomes.Methods: Five databases (Pubmed, Web of Science, Scopus, IEEE, ACM Digital Library) were searched using a combination of keywords and MeSH terms. 3,585 papers from the year 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2,994 papers were screened by title and abstract, and 81 were selected for fulltext review. Of these, 52 were eligible for inclusion in the detailed analysis. Results:The papers included in the review were heterogeneous in design and the selection of adverse outcomes modelled. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal pre-processing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. Conclusions:The review found heterogeneity in techniques and inconsistent reporting of parameters and procedures used for preprocessing neonatal physiological signals, which is necessary to confirm their adherence to clinical practices, usefulness and choice of the best practices. Improving this aspect will ensure transparent reporting and hence facilitate the interpretation and reproducibility of the studies as well as accelerate their clinical adoption. Clinical

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Heart Rate Variability during Periods of Low Blood Pressure as a Predictor of Short-Term Outcome in Preterms

Cited by 4 publications

References 16 publications

Neonatal heart rate variability: a contemporary scoping review of analysis methods and clinical applications

Neonatal heart rate variability: a contemporary scoping review of analysis methods and clinical applications

Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review

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