Comparison between Analgesia Nociception Index (ANI) and self-reported measures for diagnosing pain in conscious individuals: a systematic review and meta-analysis

Baroni, Daniela; Abreu, Lucas Guimarães; Paiva, Saul Martins; Costa, Luciane Rezende

doi:10.1038/s41598-022-06993-z

Cited by 11 publications

(5 citation statements)

References 57 publications

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“…Baroni et al conducted a meta-analysis to assess ANI's potential in awake patients after surgery [18]. Of the 16 diverse articles, 9 studies focusing on NRS and ANI were included for meta-analysis.…”

Section: Resultsmentioning

confidence: 99%

Progress in the validation of nociception monitoring in guiding intraoperative analgesic therapy

Van Santvliet,

Vereecke

2024

Current Opinion in Anaesthesiology

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Purpose of review This article summarizes the current level of validation for several nociception monitors using a categorized validation process to facilitate the comparison of performance. Recent findings Nociception monitors improve the detection of a shift in the nociception and antinociception balance during anesthesia, guiding perioperative analgesic therapy. A clear overview and comparison of the validation process for these monitors is missing. Results Within a 2-year time-frame, we identified validation studies for four monitors [analgesia nociception index (ANI), nociception level monitor (NOL), surgical pleth index (SPI), and pupillometry]. We categorized these studies in one out of six mandatory validation steps: developmental studies, clinical validation studies, pharmacological validation studies, clinical utility studies, outcome improvement studies and economical evaluation studies. The current level of validation for most monitors is mainly focused on the first three categories, whereas ANI, NOL, and SPI advanced most in the availability of clinical utility studies and provide confirmation of a clinical outcome improvement. Analysis of economical value for public health effects is not yet publicly available for the studied monitors. Summary This review proposes a stepwise structure for validation of new monitoring technology, which facilitates comparison between the level of validation of different devices and identifies the need for future research questions.

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Section: Resultsmentioning

confidence: 99%

Progress in the validation of nociception monitoring in guiding intraoperative analgesic therapy

Van Santvliet,

Vereecke

2024

Current Opinion in Anaesthesiology

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“…Studies have suggested that ANI is an objective and reliable indicator for predicting intraoperative pain [ 10 , 20 , 27 ]. Although its correlation has been weak when applied to conscious patients, a previous meta-analysis has reported the ANI to be more helpful when applied immediately after sedation or surgery [ 28 ]. To date, there is insufficient evidence to assess nociception or pain solely using ANI, especially in the awake state.…”

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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“…In addition, some studies have reported that ANI values in patients differ when under general anesthesia and when in a conscious state [ 56 ]; thus, ANI in an awake state is difficult to interpret [ 57 ]. Baroni et al performed a meta-analysis of nine studies that assessed ANI and self-reported measures in conscious patients [ 58 ]. They found a weak negative correlation between ANI and NRS in the post-anesthetic recovery room ( r = −0.0984; 95% CI −0.397 to 0.220, I 2 = 95.82%).…”

Section: Limitations Of Animentioning

confidence: 99%

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

2022

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At present, there is no objective and absolute measure of nociception, although various monitoring techniques have been developed. One such technique is the Analgesia Nociception Index (ANI), which is calculated from heart rate variability that reflects the relative parasympathetic tone. ANI is expressed on a non-unit scale of 0–100 (100 indicates maximal relative parasympathetic tone). Several studies indicated that ANI-guided anesthesia may help reduce intraoperative opioid use. The usefulness of ANI in the intensive care unit (ICU) and during surgery has also been reported. However, some limitations of ANI have also been reported; for example, ANI is affected by emotions and some drugs. In 2022, a high frequency variability index (HFVI), which was renamed from ANI and uses the same algorithm as ANI, was commercialized; therefore, ANI/HFVI are currently in the spotlight. Unlike ANI, HFVI can be displayed along with other biometric information on the Root ® monitor. ANI/HFVI monitoring may affect the prognosis of not only patients in the perioperative period but those in ICU, those who receive home medical care, or outpatients. In this article, we present an updated review on ANI that has been published in the last decade, introduce HFVI, and discuss the outlooks of ANI/HFVI.

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Comparison between Analgesia Nociception Index (ANI) and self-reported measures for diagnosing pain in conscious individuals: a systematic review and meta-analysis

Cited by 11 publications

References 57 publications

Progress in the validation of nociception monitoring in guiding intraoperative analgesic therapy

Progress in the validation of nociception monitoring in guiding intraoperative analgesic therapy

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

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

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