Bedside analysis of heart rate variability by Analgesia Nociception Index (ANI) predicts hypotension after spinal anesthesia for elective Caesarean delivery

Ursulet, Lionel; Cros, Jérôme; Jonckheere, Julien De; Sengès, P.; Vincelot, A.; Nathan, Nathalie

doi:10.1097/00003643-201206001-00014

Cited by 3 publications

(4 citation statements)

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“…The results of our study are against the results of the study done by Ghabach MB et al, [5], Remart´ınez J et al, [6] and Ursulet, L et al, [7]. The purpose of this study was to assess the effect of antenatal weight gain on baseline heart rate variability and incidence of hypotension in singleton parturients with a normal pre-pregnancy body mass index, presenting at term for elective caesarean section under spinal anaesthesia.…”

Section: Discussionsupporting

confidence: 60%

Heart Rate Variability as A Predictor of Hypotension Following Spinal Anesthesia for Elective Caesarian Section in Preeclamptic Parturients: A Descriptive Observational Study

Shehata¹,

Sakka

Omran

et al. 2019

Open Access Maced J Med Sci

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AIM: In this study we aimed to find out the heart rate variability measuring using electrical cardiometry is not reliable as a predictor for hypotension following spinal anesthesia in preeclamptic parturients undergoing elective cesarean section. METHODS: Electrical Cardiometry system was used to measure Heart rate variability (HRV) at five different time points before fluid loading (T0, baseline), after fluid loading (T1), 5 min after spinal anaesthesia (T2), 15 min after spinal anaesthesia (T3) and 30 min after spinal anaesthesia (T4). Traditional HRV measurement was determined using time-domain analysis. This Observational descriptive cohort study was conducted in Kasr Al-Ainy Hospital, Faculty of Medicine, Cairo University from February 2018 till June 2019, after approval of the Ethical Committee and written patients consent. RESULTS: The main finding of the current study is that heart rate variability measuring using electrical cardiometry is not reliable as a predictor for hypotension following spinal anaesthesia in preeclamptic parturients undergoing elective cesarean section. CONCLUSION: Heart rate variability cannot be used as a predictor for hypotension following spinal anaesthesia in preeclamptic patients undergoing elective caesarean section using electrical cardiometry.

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Section: Discussionsupporting

confidence: 60%

Heart Rate Variability as A Predictor of Hypotension Following Spinal Anesthesia for Elective Caesarian Section in Preeclamptic Parturients: A Descriptive Observational Study

Shehata¹,

Sakka

Omran

et al. 2019

Open Access Maced J Med Sci

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“…The combination of SSTs with other predictors, such as heart rate variability, may improve the predictive value of HRV assessment in the static state. 36,39 The ANS plays a central role in the adaptation and short-term control of the cardiovascular system to meet various hemodynamic requirements. 48 Pre-operative ANS assessment, for example, by assessing HRV, may provide useful information regarding the risk for hypotension following induction of spinal anesthesia.…”

Section: Discussionmentioning

confidence: 99%

“…14 However, three further studies determined that the baseline LF/HF was not predictive of hypotension. 13,17,36 Sakata et al 36 and Ursulet et al 39 suggested that dynamic heart rate variations associated with different patient positions improved the predictive value compared with a static state.…”

Section: Domain III Baseline Sympathovagal Balance Indicesmentioning

confidence: 99%

Prediction of spinal anesthesia-induced hypotension during elective cesarean section: a systematic review of prospective observational studies

Chen

Liao

et al. 2021

International Journal of Obstetric Anesthesia

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…HRV has been shown in several studies to measure Autonomic Nervous System tone, strongly influenced by anaesthetic drugs [42]. ANI has been Complexity 3 employed in several research in order to validate it as a device capable of measuring the nociception balance [43][44][45][46]. ANI seems more sensitive than other traditional measures based on hemodynamic response of patient under propofol interventions to moderate nociceptive stimuli [47].…”

Section: Problem Descriptionmentioning

confidence: 99%

A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision‐Making Process in Medicine

et al. 2018

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One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index (ANI) as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery.

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Bedside analysis of heart rate variability by Analgesia Nociception Index (ANI) predicts hypotension after spinal anesthesia for elective Caesarean delivery

Cited by 3 publications

References 0 publications

Heart Rate Variability as A Predictor of Hypotension Following Spinal Anesthesia for Elective Caesarian Section in Preeclamptic Parturients: A Descriptive Observational Study

Heart Rate Variability as A Predictor of Hypotension Following Spinal Anesthesia for Elective Caesarian Section in Preeclamptic Parturients: A Descriptive Observational Study

Prediction of spinal anesthesia-induced hypotension during elective cesarean section: a systematic review of prospective observational studies

A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision‐Making Process in Medicine

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