Analgesia nociception index and high frequency variability index: promising indicators of relative parasympathetic tone

Yoshida, Keisuke; Obara, Shinju; Inoue, Satoki

doi:10.1007/s00540-022-03126-8

Cited by 17 publications

(12 citation statements)

References 68 publications

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“…To date, there is insufficient evidence to assess nociception or pain solely using ANI, especially in the awake state. However, the use of ANI as a non-invasive indirect measure of pain has received more attention in recent years [ 29 ]. Similar to our study, Boselli et al [ 20 ] also evaluated ANI performance in predicting immediate postoperative pain in patients who were initially induced using propofol.…”

Section: Discussionmentioning

confidence: 99%

Machine learning model of facial expression outperforms models using analgesia nociception index and vital signs to predict postoperative pain intensity: a pilot study

Park,

Yoon

et al. 2024

Korean J Anesthesiol

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Background: Few studies have evaluated the use of automated artificial intelligence (AI)-based pain recognition in postoperative settings or the correlation with pain intensity. In this study, various machine learning (ML)-based models using facial expressions, the analgesia nociception index (ANI), and vital signs were developed to predict postoperative pain intensity, and their performances for predicting severe postoperative pain were compared.Methods: In total, 155 facial expressions from patients who underwent gastrectomy were recorded postoperatively; one blinded anesthesiologist simultaneously recorded the ANI score, vital signs, and patient self-assessed pain intensity based on the 11-point numerical rating scale (NRS). The ML models’ area under the receiver operating characteristic curves (AUROCs) were calculated and compared using DeLong’s test.Results: ML models were constructed using facial expressions, ANI, vital signs, and different combinations of the three datasets. The ML model constructed using facial expressions best predicted an NRS ≥ 7 (AUROC 0.93) followed by the ML model combining facial expressions and vital signs (AUROC 0.84) in the test-set. ML models constructed using combined physiological signals (vital signs, ANI) performed better than models based on individual parameters for predicting NRS ≥ 7, although the AUROCs were inferior to those of the ML model based on facial expressions (all P < 0.050). Among these parameters, absolute and relative ANI had the worst AUROCs (0.69 and 0.68, respectively) for predicting NRS ≥ 7.Conclusions: The ML model constructed using facial expressions best predicted severe postoperative pain (NRS ≥ 7) and outperformed models constructed from physiological signals.

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Section: Discussionmentioning

confidence: 99%

Machine learning model of facial expression outperforms models using analgesia nociception index and vital signs to predict postoperative pain intensity: a pilot study

Park,

Yoon

et al. 2024

Korean J Anesthesiol

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“…However, HFVI may be displayed concurrently with additional biometric information. In doing so, HFVI can form part of a multiparameter observation [ 24 ].…”

Section: Nociception Monitoring In Ofamentioning

confidence: 99%

Can Opioid-Free Anaesthesia Be Personalised? A Narrative Review

Goff

Hina

Malik

et al. 2023

JPM

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Background: A significant amount of evidence suggests that Opioid-Free Anaesthesia (OFA) may provide better outcomes for patients undergoing surgery, sparing patients who are particularly vulnerable to adverse side effects of opioids. However, to what extent personalizing OFA is feasible and beneficial has not been adequately described. Methods: We conducted a narrative literature review aiming to provide a comprehensive understanding of nociception and pain and its context within the field of OFA. Physiological (including monitoring), pharmacological, procedural (type of surgery), genetical and phenotypical (including patients’ conditions) were considered. Results: We did not find any monitoring robustly associated with improved outcomes. However, we found evidence supporting particular OFA indications, such as bariatric and cancer surgery. We found that vulnerable patients may benefit more from OFA, with an interesting field of research in patients suffering from vascular disease. We found a variety of techniques and medications making it impossible to consider OFA as a single technique. Our findings suggest that a vast field of research remains unexplored. In particular, a deeper understanding of nociception with an interest in its genetic and acquired contributors would be an excellent starting point paving the way for personalised OFA. Conclusion: Recent developments in OFA may present a more holistic approach, challenging the use of opioids. Understanding better nociception, given the variety of OFA techniques, may help to maximize their potential in different contexts and potential indications.

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“…Diğer yandan çok yakın zamanda, rölatif olarak parasempatik tonusu izlemek için kullanılabilen yüksek frekans değişkenlik indeksi (high frequency variability index-HFVI, Mdoloris Medical Systems) tanımlandı (71)…”

Section: Nosiseptif Fleksör Refleks (Nociceptive Flexor Reflex-nfr)unclassified

Nociception Monitoring

Bilgin¹,

Cansabuncu²

2023

JARSS

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Tissue damage and inflammation-induced nociception during surgery are the primary reasons for administering general anesthesia to the patient. During general anesthesia, analgesics inhibit autonomic and somatic responses, hypnotic agents prevent awareness, and neuromuscular blocking agents inhibit reflex movements. Careful monitoring of the effects of general anesthesia is necessary to avoid over- or under-dosing of anesthetics and thus to prevent associated complications and adverse effects. In general, hemodynamic parameters have been used to guide the intraoperative administration of analgesics such as opioids, but hemodynamic parameters are not standardized and cannot always provide a clear assessment. There is growing interest in techniques that can objectively monitor the analgesic component of anesthesia to achieve an appropriate balance of nociception and anti-nociception and to guide analgesic administration. The purpose of this review is to provide information about nociception monitoring techniques, which are increasingly used in clinical practice. Keywords: General anesthesia, nociception, anti-nociception, monitoring

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Analgesia nociception index and high frequency variability index: promising indicators of relative parasympathetic tone

Cited by 17 publications

References 68 publications

Machine learning model of facial expression outperforms models using analgesia nociception index and vital signs to predict postoperative pain intensity: a pilot study

Machine learning model of facial expression outperforms models using analgesia nociception index and vital signs to predict postoperative pain intensity: a pilot study

Can Opioid-Free Anaesthesia Be Personalised? A Narrative Review

Nociception Monitoring

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