Normalized Pulse Volume as a Superior Predictor of Respiration Recovery and Quantification of Nociception Anti-nociception Balance Compared to Opioid Effect Site Concentration: A Prospective, Observational Study

Onishi Tatsuki; Yoshika Onishi

doi:10.12688/f1000research.146215.1

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Research Article

Normalized Pulse Volume as a Superior Predictor of Respiration Recovery and Quantification of Nociception Anti-nociception Balance Compared to Opioid Effect Site Concentration: A Prospective, Observational Study

[version 1; peer review: 1 approved, 1 not approved]

Onishi Tatsuki ^1-3, Yoshika Onishi¹

PUBLISHED 27 Mar 2024

Author details Author details

¹ Wellbeing Keiei LLC, Kyoto, Kyoto, 6048103, Japan
² Faculty of Data Science, Shiga University, Hikone, Shiga Prefecture, 5220069, Japan
³ Department of Anesthesiology and Pain Medicine, Juntendo University Shizuoka Hospital, Izunokuni, 410-2211, Japan

Onishi Tatsuki
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Software, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Yoshika Onishi
Roles: Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Background

Quantifying pain and the balance between nociception and anti-nociception (NANB) in sedated patients is challenging. Traditional opioid titration methods overlook individual differences, while existing indices like the Noxious Stimulation Response Index (NSRI) lack correlation with effect-site concentration (Ce). The Normalized Pulse Volume (NPV), used in polygraphs, has potential for pain quantification but is underexplored. This study aimed to assess NPV’s efficacy as a pain monitoring tool compared to Ce and to explore its potential in various clinical settings.

Methods

The study included 39 patients undergoing surgery under total intravenous anesthesia from July 2013 to May 2014. Selection criteria were an American Society of Anesthesiologists physical status classification system (ASA score) of 1 or 2 and surgeries with minimal fluid resuscitation or blood loss. Exclusion criteria were significant posture changes, massive hemorrhage, and high perfusion index variation. NPV and Ce were measured using the Masimo SET adult SpO2 sensor.

Results

Out of 39 patients, 9 were excluded. NPV at recovery of spontaneous respiration (RoR) was 2.62 (95% CI: 2.26–2.98) with a coefficient of variation (CoV) of 36.3%, while total Ce was 1.48 ng/ml (95% CI: 1.14–1.84) with a CoV of 62.4%. NPV showed a narrower CoV than Ce (p < 0.05, 1.93*10−5), indicating less variability. NPV outperformed Ce in predicting RoR, suggesting a more accurate reflection of NANB balance. Its superiority in stable measurement underlines its potential as a reliable pain indicator. The study’s limitations include temporal differences in NPV and Ce calculations, affecting comparative analysis.

Conclusion

NPV demonstrates promise as an objective, reliable indicator of pain or NANB, showing a strong correlation with Ce. Its application could improve pain assessments in clinical settings, optimizing patient care and analgesic administration. Future research should integrate NPV with other vital signs for a comprehensive pain monitoring system.

Keywords

Nociception, anti-nociception, normalized pulse volume, objective pain monitoring

Corresponding author: Onishi Tatsuki

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2024 Tatsuki O and Onishi Y. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Tatsuki O and Onishi Y. Normalized Pulse Volume as a Superior Predictor of Respiration Recovery and Quantification of Nociception Anti-nociception Balance Compared to Opioid Effect Site Concentration: A Prospective, Observational Study [version 1; peer review: 1 approved, 1 not approved]. F1000Research 2024, 13:233 (https://doi.org/10.12688/f1000research.146215.1) First published: 27 Mar 2024, 13:233 (https://doi.org/10.12688/f1000research.146215.1) Latest published: 27 Mar 2024, 13:233 (https://doi.org/10.12688/f1000research.146215.1)

Introduction

Objective quantification of pain, or more specifically the balance between nociception and anti-nociception (NANB), has long been a challenging task, especially in sedated or anesthetized patients.¹ The absence of a universally accepted, objective pain monitoring system renders titration of analgesics, particularly opioid analgesics, highly challenging in diverse clinical settings—ranging from the operating room under general anesthesia to intensive care units, and extending to special populations like pediatrics, geriatrics, unconscious patients, non-native speakers, and those undergoing various forms of local anesthesia such as epidural, spinal, and nerve block procedures.²^–⁵

Traditionally, the titration of opioid analgesics was empirical; however, several contemporary medical facilities now rely on drug pharmacokinetic simulations to estimate the effect-site concentration (Ce).⁶^–⁹ While these simulations provide a standardized approach, they often overlook individual variations in drug sensitivity, body composition, and distribution volume. Furthermore, intraoperative fluid resuscitation or blood loss can introduce additional variables that the simulations may not account for. This can inadvertently compromise patient experience, leading to either inadequate analgesia or excessive drug effects, such as respiratory depression with opioids or local anesthetic systemic toxicity.

While attempts have been made to numerically quantify pain or NANB, such as the Noxious Stimulation Response Index (NSRI) and the Surgical Pleth Index (SPI), their adoption in clinical practice remains limited.¹⁰^–¹³ Notably, despite the potential advantages of these indices, previous studies have not compared them against the established standard of Ce. This may partly explain their limited widespread acceptance. Moreover, the accuracy and relevance of these indices in the face of hemodynamic events have been debated, further impeding their universal adoption.¹⁴^–²¹

One notable parameter is the normalized pulse volume (NPV), which represents the ratio of the pulsatile to non-pulsatile component in pulse oximetry. Historically used in polygraphs,²²^,²³ recent research has shown its potential to reflect mental stress. This parameter has also been explored in contexts like peripheral nerve block success and fluid volume assessment.²⁴^–²⁶ However, its utility as an indicator for pain or NANB quantification remains largely unexplored.

Therefore, our study aimed to delve deeper into the realm of objective pain monitoring, exploring the efficacy, limitations, and potential of existing and emerging technologies.

While the challenges and limitations of current pain monitoring systems have been recognized, the potential use of NPV as a marker for the NANB during sedation or anesthesia remains largely unexplored. NPV represents the ratio of the pulsatile (AC) component to the non-pulsatile (DC) component in pulse plethysmography. Historically, this ratio has been used in fields such as physiological psychology, where it is known to decrease with psychological stress, and is thus applied in tools such as polygraphs. In anesthesiology, companies such as Masimo have utilized this measure as the Perfusion Index (PI) to assess circulatory plasma volume. This suggests that under conditions where changes in circulatory plasma volume are minimal, NPV could potentially serve as a reliable indicator of physical stress.

Several studies have observed changes in blood flow in areas targeted by local anesthetics such as epidural, spinal, and nerve blocks, with some research tracking these effects using NPV or PI. However, the application of NPV as a measure of analgesic efficacy under general anesthesia has not been explored. Although some evidence suggests that NPV might better reflect vascular resistance and sympathetic nerve α action than other parameters, its exclusion from initial parameters in tools such as the Surgical Stress Index (SSI) indicates that its potential in this realm is yet to be fully realized.

Given this background, our study hypothesized that NPV accurately reflects the NANB. We aimed to validate the utility of NPV as a novel indicator for the balance of nociceptive stimulation and analgesic efficacy. Additionally, our research explored the relationship between NPV and Ce, which is the standard titrating protocol. Furthermore, by utilizing multiple wavelengths, spanning both infrared and visible light, we strived to refine NPV measurements, ensuring its applicability even under challenging clinical scenarios such as severe anemia, hypovolemia, extreme peripheral vasoconstriction, varying skin pigmentation, and external light interference.

In essence, our goal was to ascertain the validity of NPV in quantifying pain or NANB and to elucidate the relationship between NPV and Ce in a clinical setting.

Methods

Study approval

The design and protocols were reviewed and approved by Tokyo Metropolitan Bokutoh Hospital Ethics Committee the local ethics committee in March 2017, under reference number 29-11.

Patient selection

For the analysis of the relationship between NPV and the Ce, we recruited 39 patients scheduled to undergo surgery under total intravenous anesthesia between July 2013 and May 2014 [Table 1-3].

Table 1. Demographics of cases.

Cases included	29
Age [dimensionless] (SD)	51.5 (17.8)
Male [dimensionless] (per cent)	18 (62.1)
Height [cm] (SD)	164.5 (9.5)
Weight [kg] (SD)	66.9 (10.9)
BMI [kg/m²] (SD)	24.6 (2.9)
ASA 1 [dimensionless] (per cent)	20 (68.9)
Room duration [min] (SD)	182.7 (82.2)
Anesthesia duration [min] (SD)	171.7 (82.3)
Operation duration [min] (SD)	108.4 (78.5)
Operation end to anesthesia end [min] (SD)	23.7 (10.6)
Anesthesia end to discharge from operation room [min] (SD)	5.8 (3.3)
Fentanyl used [mcg] [Q1, Q3]	600 [500, 800]
Remifentanil used [mcg] [Q1, Q3]	2050.4 [1367.0, 3156.8]
Colloid intravenous infusion [ml] [Q1, Q3]	1000 [1000, 1000]
Serous intravenous infusion [ml] [Q1, Q3]	500 [250, 900]

Table 2. Demographics of diseases.

Disease	Number of cases
Chronic Sinusitis	9
Chronic Tonsillitis	7
Nasal Septal Deviation	6
Allergic Rhinitis	4
Chronic Hypertrophic Rhinitis	3
Pansinusitis	3
Maxillary Osteonecrosis	2
Maxillary Cyst	2
Nasolacrimal Duct Obstruction	2
Parotid Gland Tumor	2
Sleep Apnea Syndrome	1
Epiglottic Cyst	1
Cervical Mass	1
Suspected Malignant Lymphoma	1
Nasopharyngeal Cyst	1
Sinus Mycosis or Sinus Fungal Infection	1
Vocal Cord Polyp	1

Table 3. Demographics of procedures.

Procedure	Number of cases
Endoscopic Sinus Surgery	13
Palatine Tonsillectomy	7
Septoplasty	6
Turbinectomy	6
Radical Pan-sinus Surgery	6
Parotid Gland Tumor Excision	2
Lacrimal Duct Silicon Tube Insertion	2
Partial Maxillectomy	2
Dacryocystorhinostomy	2
Direct Laryngoscopy Laryngeal Tumor Excision	1
Cervical Lymph Node Dissection	1
Nasopharyngeal Tumor Excision	1
Nasal Mucosal Cauterization	1
Maxillary Bone Tumor Excision	1
Intranasal Ethmoidectomy	1
Endoscopic Nasal Abscess Drainage	1
Direct Laryngoscopy Vocal Cord Polyp Removal	1

The inclusion criteria were as follows: patients with an ASA score of 1 or 2, indicating healthy individuals or those with only mild systemic diseases. Only patients undergoing surgeries where there would be no changes in the position of the surgical field relative to the sensor, minimal fluid resuscitation or blood loss, and no concomitant use of epidural, spinal, or nerve block anesthesia were included.²⁷^–³³ At our institution, this primarily pertained to otolaryngology and dental and oral surgeries.

The exclusion criteria were as follows: patients undergoing surgeries that might involve significant posture changes or that have the potential for massive hemorrhage. Cases with high perfusion index variation (PVI > 25) or low perfusion index (PI < 1) during induction were excluded.³⁴^–⁴⁴ Cases with insufficient data sets or outliers were also excluded. Of the 38 initially recruited patients, 29 patients are included as 9 were excluded based on the above criteria: 3 due to an insufficient data set, 3 with a high PVI at extubation, and 3 with a low PI at induction.

Procedure protocol

Patients recruited for this segment of the study were equipped with the Masimo SET^® Pulse Oximetry adult SpO2 adhesive sensor (Masimo Corporation, Irvine, CA, United States of America) to measure NPV, in addition to routine monitoring. Anesthesia was induced using a bolus of fentanyl and a continuous infusion of remifentanil and propofol, ensuring that the Bispectral Index™ (BIS™, Medtronic, Minneapolis, MN United States of America) remained between 40 and 60. Fentanyl was titrated to approximately 1 ng/ml towards the end of the operation, with remifentanil adjusted accordingly.⁴⁵

A respiratory rate of eight breaths per minute was maintained until the recovery of spontaneous respiration (RoR). Both NPV and Ce for fentanyl and remifentanil were recorded upon RoR. The estimated Ce was derived using the built-in pharmacokinetic simulations—Shafer model for fentanyl and Minto/Schnider model for remifentanil—provided by the Fortec ORSYS perioperative patient information system (Philips, Netherlands). The total Ce was adjusted considering remifentanil’s greater potency, in alignment with the previous study. The coefficient of variation (CoV) for both NPV and total Ce was evaluated.

Measurement details

Patients were instrumented with the Masimo RainbowSET^® Technology adhesive sensor on their fingertips. Continuous recordings were conducted in conjunction with other vital signs from the time of entry to the operating room. The Ce of the drugs was calculated using the built-in functions of the Philips Orsys anesthesia chart, employing the Shafer method for fentanyl and the Minto/Schnider method for remifentanil. Fentanyl was titrated to achieve an estimated Ce of 1 ng/ml at the end of surgery. We ensured the absence of respiratory depression from sedatives or muscle relaxants. The recovery of spontaneous respiration was defined at 8 breaths/minute, and at this juncture, we compared the NPV with the estimated Ce. For this comparison, the respiratory depressive potency ratio between fentanyl and remifentanil was set at 1:2, based on previous research findings,⁴⁵ and the sum of the effective site concentration of remifentanil and 0.5 times the effective site concentration of fentanyl was recorded (henceforth referred to as CE).

Statistical analysis

Python 3.10 was used to calculate the demographics of cases, the multivariate outlier detection by Mahalanobis distance, the difference in CoV with Z-test, and to bootstrap the distribution of the CoV and the significance level.

Results

Multivariate outlier detection by Mahalanobis distance revealed no case corresponding to outliers as the critical value for the Chi-square distribution with 95% confidence was 5.99. Demographics of the included cases are shown in Tables 1, 2, and 3. NPV on RoR was 2.62 [dimensionless number] with a 95% confidence interval of 2.26–2.98, and a CoV of 36.3%. Conversely, CE on RoR was 1.48 [1.14–1.84] [ng/ml], with a CoV of 62.4% [Figure 1]. The Z test and the test for CoV showed that NPV on RoR was narrower than CE with a significance level of < 0.05 (1.93*10⁻⁵) [Figures 2–4]. In the bootstrapped distribution, the mean and median coefficients of variation for CE were higher than those for NPV, indicating greater relative variability. The confidence interval for NPV was relatively narrow, indicating that the estimation of the coefficient of variation is stable. Alternatively, the confidence interval for CE was wide, indicating a significant uncertainty in the estimation of the coefficient of variation. Although there were areas where the confidence intervals overlapped, differences in the central values were evident. These findings suggest that CE is more variable than NPV [Figure 5].

Figure 1. Scatter plot of NPV and CE.

CE: Weighted Sum of Estimated Effect Site Concentration of Fentanyl and Remifentanil; NPV: Normalized Pulse Volume.

Figure 2. Distribution of NPV and CE.

CE: Weighted Sum of Estimated Effect Site Concentration of Fentanyl and Remifentanil; NPV: Normalized Pulse Volume.

Figure 3. Standardized distribution of NPV and CE.

CE: Weighted Sum of Estimated Effect Site Concentration of Fentanyl and Remifentanil; NPV: Normalized Pulse Volume.

Figure 4. Box plot of CoV of PI and CE.

CE: Weighted Sum of Estimated Effect Site Concentration of Fentanyl and Remifentanil.

Figure 5. Bootstrapped distribution of CoV for PI and CE.

CE: Weighted Sum of Estimated Effect Site Concentration of Fentanyl and Remifentanil.

Discussion

The presence of pain is harmful and associated with both all-cause and cancer-related mortality.⁴⁶^–⁵⁰ Conversely, narcotic analgesics used for pain relief have a negative impact on cancer progression and survival.⁵¹^,⁵² Moreover, drug abuse is a social issue.⁵³^–⁵⁵ Therefore, there is a great social demand for the proper use of analgesics and the quantification of pain, or NANB, as the basis for the use of analgesics, as well as the quantification of NANB and other sensory information.⁵⁶^,⁵⁷

Comparison with previous studies

A range of potential bioindicators for quantifying and othering pain include autonomic parameters such as electromyography, heart rate variability, and skin conductance,⁵⁸ as well as biomarkers in psychiatric disorders, including neuroimaging, metabolite levels, and gene expression changes.⁵⁹ Other potential bioindicators include functional near-infrared spectroscopy (fNIRS) machine learning,⁶⁰ and mechanistic, translational, and quantitative pain assessment tools.⁶¹ Brain-based biomarkers for pain, particularly those identified through neuroimaging and machine learning, have also been proposed.⁶² However, the lack of validated objective markers for nociception and pain remains a challenge.⁶³

Comparative performance of NPV and CE

The NPV demonstrated a superior predictive capability for the RoR, compared with the CE. In the context of assessing NANB, the NPV provided a more accurate reflection than drug simulation techniques. This suggests a stronger correlation between NPV and the balance of noxious stimuli and analgesia.

Potential for multimodal approaches

Building on these findings, there is potential to develop a multimodal approach that integrates the strengths of NPV with other measurements. Such a holistic approach could further enhance the accuracy and reliability of pain assessments in diverse clinical settings.

Study limitations

It is important to note that while NPV is calculated on a beat-to-beat basis, CE is computed only every minute. This temporal discrepancy might influence the comparative analysis between NPV and CE.

Future studies

Our team is keen on advancing this research by testing the efficacy of a pain monitoring system that employs NPV as its primary parameter, combined with other vital signs. Such a comprehensive instrument could offer a more nuanced and accurate pain assessment in clinical settings, further optimizing patient care and ensuring optimal analgesic administration. As this study was conducted with only one focus, RoR, it is necessary to study the predictive effect of stimuli that change over time.⁶⁴

Ethical considerations

This design and protocols were reviewed and approved by Tokyo Metropolitan Bokutoh Hospital Ethics Committee the local ethics committee in March 2017, under reference number.²⁹^–¹¹ Written consent to participate in the study was obtained from the participants at the anaesthesiology pre-operative consultation, not on the day of surgery, and it was explained that there would be no disadvantage in withdrawing. The entire study was conducted in adherence with the Declaration of Helsinki.⁶⁵

Author contribution

OT: Conceptualization, Data curation, Formal Analysis, Methodology, Writing – original draft, Writing – review & editing

YO: Writing – original draft, Writing – review & editing

Data availability

Normalized pulse volume as a superior predictor of respiration recovery and quantification of nociception anti-nociception balance compared to opioid effect site concentration: A prospective, observational study GitHub-https://github.com/bougtoir/npv_nanb

This project contains the following underlying data: main data file with age, gender (NPV ad Ce npv_2023_.csv), original consent form in Japanese (npv_nanb_consent_form.doc), translated consent form in English (npv_nanb_consent_form_en.doc), participant information sheets (npv_profile_.csv), STROBE Statement—Checklist of items that should be included in reports of cohort studies (strobe_checklist_npv.doc), and Jupyter notebook which includes codes to reproduce results (npv_2023.ipynb).

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication) (http://creativecommons.org/publicdomain/zero/1.0/).

Reporting guidelines

STROBE guideline for cohort study.

Acknowledgments

We thank Iwamuratea and Chiron for their assistance with environment management.

We also thank Onishi Kotoha and Onishi Ione for their assistance. This study would not have been possible without their assistance.

We would like to thank Editage (www.editage.jp) for English language editing.

References

1. Ledowski T: Objective monitoring of nociception: a review of current commercial solutions. Br. J. Anaesth. 2019; 123(2): e312–e321. PubMed Abstract | Publisher Full Text | Free Full Text
2. Kotfis K, Zegan-Barańska M, Szydłowski Ł, et al.: Methods of pain assessment in adult intensive care unit patients – Polish version of the CPOT (Critical Care Pain Observation Tool) and BPS (Behavioral Pain Scale). Anaesthesiol Intensive Ther. Polish version. 2017; 49(1): 66–72. PubMed Abstract | Publisher Full Text
3. Rodríguez I, Herskovic V, Gerea C, et al.: Understanding monitoring technologies for adults with pain: systematic literature review. J. Med. Internet Res. 2017; 19(10): e364. PubMed Abstract | Publisher Full Text | Free Full Text
4. Chang VT, Sorger B, Rosenfeld KE, et al.: Pain and palliative medicine. J. Rehabil. Res. Dev. 2007; 44(2): 279–294. Publisher Full Text
5. Kehlet H: Multimodal approach to control postoperative pathophysiology and rehabilitation. Br. J. Anaesth. 1997; 78(5): 606–617. PubMed Abstract | Publisher Full Text
6. Rhudy JL, France CR: Defining the nociceptive flexion reflex (NFR) threshold in human participants: a comparison of different scoring criteria. Pain. 2007; 128(3): 244–253. PubMed Abstract | Publisher Full Text | Free Full Text
7. Luginbühl M, Schumacher PM, Vuilleumier P, et al.: Noxious stimulation response index: a novel anesthetic state index based on hypnotic-opioid interaction. Anesthesiology. 2010; 112(4): 872–880. Publisher Full Text
8. von Dincklage F , Correll C, Schneider MH, et al.: Utility of Nociceptive Flexion Reflex threshold, bispectral Index, Composite Variability Index and Noxious Stimulation Response Index as measures for nociception during general anaesthesia. Anaesthesia. 2012; 67(8): 899–905. PubMed Abstract | Publisher Full Text
9. Struys MM, Sahinovic M, Lichtenbelt BJ, et al.: Optimizing intravenous drug administration by applying pharmacokinetic/pharmacodynamic concepts. Br. J. Anaesth. 2011; 107(1): 38–47. PubMed Abstract | Publisher Full Text
10. Bertolizio G, Garbin M, Ingelmo PM: Evaluation of nociception during pediatric surgery: A topical review. J. Pers. Med. 2023; 13(2): 260. PubMed Abstract | Publisher Full Text | Free Full Text
11. Nishiyama T: Recent advance in patient monitoring. Korean J. Anesthesiol. 2010; 59(3): 144–159. PubMed Abstract | Publisher Full Text | Free Full Text
12. Kehlet H, Wilmore DW: Multimodal strategies to improve surgical outcome. Am. J. Surg. 2002; 183(6): 630–641. Publisher Full Text
13. Bruhn J, Myles PS, Sneyd R, et al.: Depth of anaesthesia monitoring: what’s available, what’s validated and what’s next? Br. J. Anaesth. 2006; 97(1): 85–94. PubMed Abstract | Publisher Full Text
14. Edry R, Recea V, Dikust Y, et al.: Preliminary intraoperative validation of the nociception level index: A noninvasive nociception monitor. Anesthesiology. 2016; 125(1): 193–203. PubMed Abstract | Publisher Full Text
15. Stewart JA, Särkelä MOK, Wennervirta J, et al.: Novel insights on association and reactivity of bispectral Index, frontal electromyogram, and autonomic responses in nociception-sedation monitoring of critical care patients. BMC Anesthesiol. 2022; 22(1): 353. PubMed Abstract | Publisher Full Text | Free Full Text
16. Ben-Israel N, Kliger M, Zuckerman G, et al.: Monitoring the nociception level: a multi-parameter approach. J. Clin. Monit. Comput. 2013; 27(6): 659–668. PubMed Abstract | Publisher Full Text
17. Struys MM, Jensen EW, Smith W, et al.: Performance of the ARX-derived auditory evoked potential index as an indicator of anesthetic depth: a comparison with bispectral index and hemodynamic measures during propofol administration. Anesthesiology. 2002; 96(4): 803–816. PubMed Abstract | Publisher Full Text
18. Huiku M, Uutela K, van Gils M , et al.: Assessment of surgical stress during general anaesthesia. Br. J. Anaesth. 2007; 98(4): 447–455. Publisher Full Text
19. Ledowski T, Ang B, Schmarbeck T, et al.: Monitoring of sympathetic tone to assess postoperative pain: skin conductance vs surgical stress index. Anaesthesia. 2009; 64(7): 727–731. PubMed Abstract | Publisher Full Text
20. Yang G, Jiang M, Ouyang W, et al.: IoT-based remote pain monitoring system: from device to cloud platform. IEEE J. Biomed. Health Inform. 2018; 22(6): 1711–1719. PubMed Abstract | Publisher Full Text
21. Tanaka G, Sawada Y, Matsumura K, et al.: Finger arterial compliance as determined by transmission of light during mental stress and reactive hyperaemia. Eur. J. Appl. Physiol. 2002; 87(6): 562–567. PubMed Abstract | Publisher Full Text
22. Naohiro Y: Case study of Increase of normalized pulse volume to the critical item in the field concealed information test [Japanese]. Technol. Jurisdiction. 2010; 15(1): 65–74.
23. Tanaka G, Kawana S, Sawada Y: Yamakoshi K Normalized pulse volume and blood volume as separate indices of finger arterial and venous vascular tone: examination under propofol. Jpn. Psychol. Res. 2003; 45(1): 50–59. Publisher Full Text
24. Atef HM, Fattah SA, Gaffer ME, et al.: Perfusion index versus non-invasive hemodynamic parameters during insertion of i-gel, classic laryngeal mask airway and endotracheal tube. Indian J. Anaesth. 2013; 57(2): 156–162. PubMed Abstract | Publisher Full Text | Free Full Text
25. Shah PN, Kezo A: Perfusion index during endotracheal intubation and extubation: A prospective observational study. Saudi J Anaesth. 2023; 17(1): 7–11. PubMed Abstract | Publisher Full Text | Free Full Text
26. Rajan K, Dave N, Dias R, et al.: Perfusion index as a predictor of working pediatric caudal block under general anesthesia- A prospective observational study. J. Anaesthesiol. Clin. Pharmacol. 2022; 38(4): 635–639. PubMed Abstract | Publisher Full Text | Free Full Text
27. Chen Y, Fu Q, Mi WD: Effects of stroke volume variation, pulse pressure variation, and pleth variability index in predicting fluid responsiveness during different positive end expiratory pressure in prone position. Zhongguo Yi Xue Ke Xue Yuan Xue Bao Acta Acad. Med. Sin. 2015; 37(2): 179–184. PubMed Abstract | Publisher Full Text
28. Zimmermann M, Feibicke T, Keyl C, et al.: Accuracy of stroke volume variation compared with pleth variability index to predict fluid responsiveness in mechanically ventilated patients undergoing major surgery. Eur. J. Anaesthesiol. 2010; 27(6): 555–561. PubMed Abstract | Publisher Full Text
29. Ganter MT, Geisen M, Hartnack S, et al.: Prediction of fluid responsiveness in mechanically ventilated cardiac surgical patients: the performance of seven different functional hemodynamic parameters. BMC Anesthesiol. 2018; 18(1): 55. PubMed Abstract | Publisher Full Text | Free Full Text
30. Weber F, Rashmi BK, Karaoz-Bulut G, et al.: The predictive value of the Pleth Variability Index on fluid responsiveness in spontaneously breathing anaesthetized children-A prospective observational study. Paediatr. Anaesth. 2020; 30(10): 1124–1131. PubMed Abstract | Publisher Full Text | Free Full Text
31. Wu Y, Zhang F, Sun K, et al.: Evaluation of pleth variability index for predicting hypotension during induction of anesthesia in surgical patients. Zhonghua Yi Xue Za Zhi. 2014; 94(40): 3167–3170. PubMed Abstract
32. DeBarros M, Causey MW, Chesley P, et al.: Reliability of continuous non-invasive assessment of hemoglobin and fluid responsiveness: impact of obesity and abdominal insufflation pressures. Obes. Surg. 2015; 25(7): 1142–1148. PubMed Abstract | Publisher Full Text
33. Kim SJ, Kim SY, Lee HS, et al.: Ability of dynamic preload indices to predict fluid responsiveness in a high femoral-to-radial arterial pressure gradient: a retrospective study. Anesth Pain Med (Seoul). 2021; 16(4): 360–367. PubMed Abstract | Publisher Full Text | Free Full Text
34. Lee HC, Tsai YF, Tsai HI, et al.: Pulse oximeter-derived pleth variability index is a reliable indicator of cardiac preload in patients undergoing liver transplantation. Transplant. Proc. 2016; 48(4): 1055–1058. PubMed Abstract | Publisher Full Text
35. Loupec T, Nanadoumgar H, Frasca D, et al.: Pleth variability index predicts fluid responsiveness in critically ill patients. Crit. Care Med. 2011; 39(2): 294–299. PubMed Abstract | Publisher Full Text
36. Keller G, Cassar E, Desebbe O, et al.: Ability of pleth variability index to detect hemodynamic changes induced by passive leg raising in spontaneously breathing volunteers. Crit. Care. 2008; 12(2): R37. PubMed Abstract | Publisher Full Text | Free Full Text
37. Pişkin Ö, Öz İİ: Accuracy of pleth variability index compared with inferior vena cava diameter to predict fluid responsiveness in mechanically ventilated patients. Medicine. 2017; 96(47): e8889. PubMed Abstract | Publisher Full Text | Free Full Text
38. Lu W, Dong J, Xu Z, et al.: The pleth variability index as an indicator of the central extracellular fluid volume in mechanically ventilated patients after anesthesia induction: comparison with initial distribution volume of glucose. Med. Sci. Monit. 2014; 20(386–92): 386–392. Publisher Full Text
39. Haas S, Trepte C, Hinteregger M, et al.: Prediction of volume responsiveness using pleth variability index in patients undergoing cardiac surgery after cardiopulmonary bypass. J. Anesth. 2012; 26(5): 696–701. PubMed Abstract | Publisher Full Text
40. Desebbe O, Boucau C, Farhat F, et al.: The ability of pleth variability index to predict the hemodynamic effects of positive end-expiratory pressure in mechanically ventilated patients under general anesthesia. Anesth. Analg. 2010; 110(3): 792–798. PubMed Abstract | Publisher Full Text
41. Fu Q, Mi WD, Zhang H: Stroke volume variation and pleth variability index to predict fluid responsiveness during resection of primary retroperitoneal tumors in Hans Chinese. Biosci. Trends. 2012; 6(1): 38–43. PubMed Abstract | Publisher Full Text
42. Cai QF, Mi WD, Yuan WX: The ability of pleth variability index to predict fluid responsiveness in mechanically ventilated patients under general anaesthesia. Zhonghua Wai Ke Za Zhi Chin J. Surg. 2010; 48(21): 1628–1632. PubMed Abstract
43. Monnet X, Guérin L, Jozwiak M, et al.: Pleth variability index is a weak predictor of fluid responsiveness in patients receiving norepinephrine. Br. J. Anaesth. 2013; 110(2): 207–213. PubMed Abstract | Publisher Full Text
44. García-Soler P, Camacho Alonso JM, González-Gómez JM, et al.: Noninvasive hemoglobin monitoring in critically ill pediatric patients at risk of bleeding. Monitorización no invasiva transcutánea de la concentración de hemoglobina en pacientes críticos pediátricos con riesgo de sangrado. Med. Intensiva. 2017; 41(4): 209–215. PubMed Abstract | Publisher Full Text
45. Gelberg J, Jonmarker C, Stenqvist O, et al.: Intravenous boluses of fentanyl, 1 μg kg⁻¹, and remifentanil, 0.5 μg kg⁻¹, give similar maximum ventilatory depression in awake volunteer. Br. J. Anaesth. 2012; 108(6): 1028–1034. PubMed Abstract | Publisher Full Text
46. Smith BH, Elliott AM, Hannaford PC: Pain and subsequent mortality and cancer among women in the Royal College of General Practitioners Oral Contraception Study. Br. J. Gen. Pract. 2003; 53(486): 45–46. PubMed Abstract
47. Macfarlane GJ, McBeth J, Silman AJ: Widespread body pain and mortality: prospective population based study. BMJ (Clin Res Ed). 2001; 323(7314): 662–665. PubMed Abstract | Publisher Full Text | Free Full Text
48. Velik-Salchner C: Computed advisory systems in daily practice for predicting concentrations and effects of combined anesthetics: a new field in anesthesia? Minerva Anestesiol. 2015; 81(11): 1151–1152. PubMed Abstract
49. Rasmussen LS, Larsen K, Houx P, et al.: The International Study of Postoperative Cognitive Dysfunction. The assessment of postoperative cognitive function. Acta Anaesthesiol. Scand. 2001; 45(3): 275–289. Publisher Full Text
50. Shu AH, Wang Q, Chen XB: Effect of different depths of anesthesia on postoperative cognitive function in laparoscopic patients: a randomized clinical trial. Curr. Med. Res. Opin. 2015; 31(10): 1883–1887. PubMed Abstract | Publisher Full Text
51. Amaram-Davila J, Davis M, Reddy A: Opioids and cancer mortality. Curr. Treat. Options Oncol. 2020; 21(3): 22. Publisher Full Text
52. Oswald N, Halle-Smith J, Kerr A, et al.: Perioperative immune function and pain control may underlie early hospital readmission and 90 day mortality following lung cancer resection: A prospective cohort study of 932 patients. Eur. J. Surg. Oncol. 2019; 45(5): 863–869. PubMed Abstract | Publisher Full Text
53. Kata V, Novitch MB, Jones MR, et al.: Opioid addiction, diversion, and abuse in chronic and cancer pain. Curr. Opin. Support. Palliat. Care. 2018; 12(2): 124–130. PubMed Abstract | Publisher Full Text
54. Florence C, Luo F, Rice K: The economic burden of opioid use disorder and fatal opioid overdose in the United States, 2017. Drug Alcohol Depend. 2021; 218: 108350. PubMed Abstract | Publisher Full Text | Free Full Text
55. Krausz RM, Westenberg JN, Ziafat K: The opioid overdose crisis as a global health challenge. Curr. Opin. Psychiatry. 2021; 34(4): 405–412. PubMed Abstract | Publisher Full Text
56. Chen X, Thee C, Gruenewald M, et al.: Comparison of surgical stress index-guided analgesia with standard clinical practice during routine general anesthesia: a pilot study. Anesthesiology. 2010; 112(5): 1175–1183. PubMed Abstract | Publisher Full Text
57. Farhang B, Mathews DM: Pain monitor: reality or fantasy in ambulatory patients. Curr. Opin. Anaesthesiol. 2019; 32(6): 727–734. Publisher Full Text
58. Walter S, Gruss S, Limbrecht-Ecklundt K, et al.: Automatic pain quantification using autonomic parameters. Psychol. Neurosci. 2014; 7(3): 363–380. Publisher Full Text
59. Jakubów P, Kosciuczuk U, Garkowski A, et al.: Possibility of assessing pain with biomarkers in psychiatric disorders.2021. Publisher Full Text
60. Fernandez Rojas R, Huang X, Ou KL: A machine learning approach for the identification of a biomarker of human pain using fNIRS. Sci. Rep. 2019; 9(1): 5645. PubMed Abstract | Publisher Full Text | Free Full Text
61. Arendt-Nielsen L, Curatolo M: Mechanistic, translational, quantitative pain assessment tools in profiling of pain patients and for development of new analgesic compounds. Scand J. Pain. 2013; 4(4): 226–230. PubMed Abstract | Publisher Full Text
62. van der Miesen MM , Lindquist MA, Wager TD: Neuroimaging-based biomarkers for pain: state of the field and current directions. Pain Rep. 2019; 4(4): e751. PubMed Abstract | Publisher Full Text | Free Full Text
63. Cowen R, Stasiowska M, Laycock H, et al.: Assessing pain objectively: the use of physiological markers. Anaesthesia. 2015; 70(7): 828–847. Publisher Full Text
64. Melia U, Vallverdú M, Borrat X, et al.: Prediction of nociceptive responses during sedation by linear and non-linear measures of EEG signals in high frequencies. PLoS One. 2015; 10(4): e0123464. Publisher Full Text
65. Declaration of Helsinki: BMJ. 1996; 313. Publisher Full Text

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Author details Author details

¹ Wellbeing Keiei LLC, Kyoto, Kyoto, 6048103, Japan
² Faculty of Data Science, Shiga University, Hikone, Shiga Prefecture, 5220069, Japan
³ Department of Anesthesiology and Pain Medicine, Juntendo University Shizuoka Hospital, Izunokuni, 410-2211, Japan

Onishi Tatsuki
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Software, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Yoshika Onishi
Roles: Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

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The author(s) declared that no grants were involved in supporting this work.

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Tatsuki O and Onishi Y. Normalized Pulse Volume as a Superior Predictor of Respiration Recovery and Quantification of Nociception Anti-nociception Balance Compared to Opioid Effect Site Concentration: A Prospective, Observational Study [version 1; peer review: 1 approved, 1 not approved]. F1000Research 2024, 13:233 (https://doi.org/10.12688/f1000research.146215.1)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

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Reviewer Report 26 Jun 2024

Mika Särkelä, GE Healthcare Finland Oy, Helsinki, Finland

Not Approved

https://doi.org/10.5256/f1000research.160275.r273068

Tatsuki and Onishi have compared normalized pulse volume and estimated analgesic effect site concentration in 39 surgical patients. I regret my delayed report, but that is partly caused by inaccuracies in the manuscript.

1. Term "normalized pulse ... Continue reading

Tatsuki and Onishi have compared normalized pulse volume and estimated analgesic effect site concentration in 39 surgical patients. I regret my delayed report, but that is partly caused by inaccuracies in the manuscript.

1. Term "normalized pulse volume" (NPV) is not commonly used in the field of anesthesia monitoring. After checking the original article (Sawada Y, et al., 2001 [Ref 1]) it turned out to be just plethysmographic waveform amplitude, also known as perfusion index (PI). I think it is misleading to introduce new name to already established parameter, it only causes confusion among readers. Later in the text authors admit that NPV is also known as PI. Authors have used Masimo device in data recording, it remains unclear if the authors have recorded plethysmogram waveform and derived NPV by themselves, or just recorded the PI calculated by Masimo device and renamed that as "NPV". The unit of PI and NPV is "%", it is not dimensionless parameter as stated in Figure 1. Further, contrary to what authors claim, NPV/PI is used in the commercial nociception parameters like SPI (originally known as SSI). In the article of (Huiku M, et al., 2007 [Ref 2]) it is called as PPGA. Therefore, the criticism for existing methods presented in the introduction apply also to NPV/PI.

2. Even though NPV varies less at RoR phase than Ce it does not mean it would be "superior predictive" over Ce. For assessing this, the authors should take NPV and Ce value before and after the RoR, and evaluate if those values overlap. For example, prediction probability (Pk) analysis is commonly used for the purpose (Smith WD, et al., 1996 [Ref 3])

3. Manuscript contains negligence errors, for example Age is mentioned to be "dimensionless" variable in Table 1, but I assume it is presented in years.

Is the work clearly and accurately presented and does it cite the current literature?

No
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

No
Are all the source data underlying the results available to ensure full reproducibility?

No source data required
Are the conclusions drawn adequately supported by the results?

No

References

1. Sawada Y, Tanaka G, Yamakoshi K: Normalized pulse volume (NPV) derived photo-plethysmographically as a more valid measure of the finger vascular tone.Int J Psychophysiol. 2001; 41 (1): 1-10 PubMed Abstract | Publisher Full Text
2. Huiku M, Uutela K, van Gils M, Korhonen I, et al.: Assessment of surgical stress during general anaesthesia.Br J Anaesth. 2007; 98 (4): 447-55 PubMed Abstract | Publisher Full Text
3. Smith WD, Dutton RC, Smith NT: Measuring the performance of anesthetic depth indicators.Anesthesiology. 1996; 84 (1): 38-51 PubMed Abstract | Publisher Full Text

Competing Interests: I am employee of GE HealthCare, manufacturer of SPI.(I confirm that this potential conflict of interest does not affect my ability to write an objective and unbiased review of the article.)

Reviewer Expertise: Patient monitoring

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Reviewer Report 10 May 2024

Tatsuhiko Arafune, Division of Electronic Information and Biomedical Engineering, School of Schience and Engineering, Tokyo Denki University, Saitama, Japan

Approved

https://doi.org/10.5256/f1000research.160275.r260349

SUMMARY:
This study evaluated the validity of normalized pulse volume (NPV) as a novel measure for quantifying pain and the balance between nociception and antinociception in sedated patients. 39 total venous anesthesia surgical patients were compared to NPV and ... Continue reading

SUMMARY:
This study evaluated the validity of normalized pulse volume (NPV) as a novel measure for quantifying pain and the balance between nociception and antinociception in sedated patients. 39 total venous anesthesia surgical patients were compared to NPV and effect site concentration (Ce), showing that NPV has a narrower coefficient of variation than Ce and is more NPV is a promising objective and reliable indicator of pain or NANB and may contribute to pain assessment and analgesic optimization in clinical practice.

Strengths of the Study:
The strength of this study is that NPV offers less variability and higher reliability than conventional pain assessment methods; NPV correlated strongly with Ce and showed excellent predictive performance, especially during recovery of spontaneous breathing. This allows more precise pain management for individual patients, facilitates postoperative recovery, and reduces the risk of over- or undermedication with analgesics. Thus, NPV is an important tool for improving pain assessment in clinical practice.

Comments to authors:
A wide range of wavelengths from near-infrared to green are used for pulse wave measurement. Each wavelength has its own advantages and disadvantages, and it is difficult to say which wavelength is optimal. Normalized Pulse Volume,” the wavelength of the light source used for pulse wave measurement was 810 nm. What is the wavelength of Masimo's sensor used for measurement in this study? If it is clear, it is desirable to specify it. We would also like to confirm if there are any differences compared to previous studies that used NPV for stress measurement.

Since NPV is an index of vascular tone, it is possible that NPV can be obtained as a clearer index for symptoms such as cancer, which requires stronger pain relief. On the other hand, a situation in which a patient feels strong pain is likely to be accompanied by body movement, which can be assumed to have a negative impact on NPV measurement. If NPV is to be compared with CE, it is necessary to consider whether NPV is superior in detecting not only weak pain that the patient can voluntarily suppress, but also strong pain that is accompanied by body movement.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: No new data collection was required, and this paper describes the main idea of this study to the minimum necessary. On the other hand, as noted in the comments above, additional information would have enhanced the quality of this paper.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Version 1 27 Mar 24	read	read

Tatsuhiko Arafune, Tokyo Denki University, Saitama, Japan
Mika Särkelä, GE Healthcare Finland Oy, Helsinki, Finland

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26 Jun 2024 | for Version 1

Mika Särkelä, GE Healthcare Finland Oy, Helsinki, Finland

2 Views Cite this report Responses(0)

Not Approved

Tatsuki and Onishi have compared normalized pulse volume and estimated analgesic effect site concentration in 39 surgical patients. I regret my delayed report, but that is partly caused by inaccuracies in the manuscript.

1. Term "normalized pulse volume" (NPV) is not commonly used in the field of anesthesia monitoring. After checking the original article (Sawada Y, et al., 2001 [Ref 1]) it turned out to be just plethysmographic waveform amplitude, also known as perfusion index (PI). I think it is misleading to introduce new name to already established parameter, it only causes confusion among readers. Later in the text authors admit that NPV is also known as PI. Authors have used Masimo device in data recording, it remains unclear if the authors have recorded plethysmogram waveform and derived NPV by themselves, or just recorded the PI calculated by Masimo device and renamed that as "NPV". The unit of PI and NPV is "%", it is not dimensionless parameter as stated in Figure 1. Further, contrary to what authors claim, NPV/PI is used in the commercial nociception parameters like SPI (originally known as SSI). In the article of (Huiku M, et al., 2007 [Ref 2]) it is called as PPGA. Therefore, the criticism for existing methods presented in the introduction apply also to NPV/PI.

2. Even though NPV varies less at RoR phase than Ce it does not mean it would be "superior predictive" over Ce. For assessing this, the authors should take NPV and Ce value before and after the RoR, and evaluate if those values overlap. For example, prediction probability (Pk) analysis is commonly used for the purpose (Smith WD, et al., 1996 [Ref 3])

3. Manuscript contains negligence errors, for example Age is mentioned to be "dimensionless" variable in Table 1, but I assume it is presented in years.

Is the work clearly and accurately presented and does it cite the current literature?

No
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

No
Are all the source data underlying the results available to ensure full reproducibility?

No source data required
Are the conclusions drawn adequately supported by the results?

No

References

1. Sawada Y, Tanaka G, Yamakoshi K: Normalized pulse volume (NPV) derived photo-plethysmographically as a more valid measure of the finger vascular tone.Int J Psychophysiol. 2001; 41 (1): 1-10 PubMed Abstract | Publisher Full Text
2. Huiku M, Uutela K, van Gils M, Korhonen I, et al.: Assessment of surgical stress during general anaesthesia.Br J Anaesth. 2007; 98 (4): 447-55 PubMed Abstract | Publisher Full Text
3. Smith WD, Dutton RC, Smith NT: Measuring the performance of anesthetic depth indicators.Anesthesiology. 1996; 84 (1): 38-51 PubMed Abstract | Publisher Full Text

Competing Interests

I am employee of GE HealthCare, manufacturer of SPI.(I confirm that this potential conflict of interest does not affect my ability to write an objective and unbiased review of the article.)

Reviewer Expertise

Patient monitoring

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

Respond to this report

Responses (0)

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Reviewer Report

1 Views

10 May 2024 | for Version 1

Tatsuhiko Arafune, Division of Electronic Information and Biomedical Engineering, School of Schience and Engineering, Tokyo Denki University, Saitama, Japan

1 Views Cite this report Responses(0)

Approved

SUMMARY:
This study evaluated the validity of normalized pulse volume (NPV) as a novel measure for quantifying pain and the balance between nociception and antinociception in sedated patients. 39 total venous anesthesia surgical patients were compared to NPV and effect site concentration (Ce), showing that NPV has a narrower coefficient of variation than Ce and is more NPV is a promising objective and reliable indicator of pain or NANB and may contribute to pain assessment and analgesic optimization in clinical practice.

Strengths of the Study:
The strength of this study is that NPV offers less variability and higher reliability than conventional pain assessment methods; NPV correlated strongly with Ce and showed excellent predictive performance, especially during recovery of spontaneous breathing. This allows more precise pain management for individual patients, facilitates postoperative recovery, and reduces the risk of over- or undermedication with analgesics. Thus, NPV is an important tool for improving pain assessment in clinical practice.

Comments to authors:
A wide range of wavelengths from near-infrared to green are used for pulse wave measurement. Each wavelength has its own advantages and disadvantages, and it is difficult to say which wavelength is optimal. Normalized Pulse Volume,” the wavelength of the light source used for pulse wave measurement was 810 nm. What is the wavelength of Masimo's sensor used for measurement in this study? If it is clear, it is desirable to specify it. We would also like to confirm if there are any differences compared to previous studies that used NPV for stress measurement.

Since NPV is an index of vascular tone, it is possible that NPV can be obtained as a clearer index for symptoms such as cancer, which requires stronger pain relief. On the other hand, a situation in which a patient feels strong pain is likely to be accompanied by body movement, which can be assumed to have a negative impact on NPV measurement. If NPV is to be compared with CE, it is necessary to consider whether NPV is superior in detecting not only weak pain that the patient can voluntarily suppress, but also strong pain that is accompanied by body movement.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

No new data collection was required, and this paper describes the main idea of this study to the minimum necessary. On the other hand, as noted in the comments above, additional information would have enhanced the quality of this paper.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Respond to this report

Responses (0)

[1] 1. Ledowski T: Objective monitoring of nociception: a review of current commercial solutions. Br. J. Anaesth. 2019; 123(2): e312–e321. PubMed Abstract | Publisher Full Text | Free Full Text

[2] 2. Kotfis K, Zegan-Barańska M, Szydłowski Ł, et al.: Methods of pain assessment in adult intensive care unit patients – Polish version of the CPOT (Critical Care Pain Observation Tool) and BPS (Behavioral Pain Scale). Anaesthesiol Intensive Ther. Polish version. 2017; 49(1): 66–72. PubMed Abstract | Publisher Full Text

[3] 3. Rodríguez I, Herskovic V, Gerea C, et al.: Understanding monitoring technologies for adults with pain: systematic literature review. J. Med. Internet Res. 2017; 19(10): e364. PubMed Abstract | Publisher Full Text | Free Full Text

[4] 4. Chang VT, Sorger B, Rosenfeld KE, et al.: Pain and palliative medicine. J. Rehabil. Res. Dev. 2007; 44(2): 279–294. Publisher Full Text

[5] 5. Kehlet H: Multimodal approach to control postoperative pathophysiology and rehabilitation. Br. J. Anaesth. 1997; 78(5): 606–617. PubMed Abstract | Publisher Full Text

[6] 6. Rhudy JL, France CR: Defining the nociceptive flexion reflex (NFR) threshold in human participants: a comparison of different scoring criteria. Pain. 2007; 128(3): 244–253. PubMed Abstract | Publisher Full Text | Free Full Text

[7] 7. Luginbühl M, Schumacher PM, Vuilleumier P, et al.: Noxious stimulation response index: a novel anesthetic state index based on hypnotic-opioid interaction. Anesthesiology. 2010; 112(4): 872–880. Publisher Full Text

[8] 8. von Dincklage F , Correll C, Schneider MH, et al.: Utility of Nociceptive Flexion Reflex threshold, bispectral Index, Composite Variability Index and Noxious Stimulation Response Index as measures for nociception during general anaesthesia. Anaesthesia. 2012; 67(8): 899–905. PubMed Abstract | Publisher Full Text

[9] 9. Struys MM, Sahinovic M, Lichtenbelt BJ, et al.: Optimizing intravenous drug administration by applying pharmacokinetic/pharmacodynamic concepts. Br. J. Anaesth. 2011; 107(1): 38–47. PubMed Abstract | Publisher Full Text

[10] 10. Bertolizio G, Garbin M, Ingelmo PM: Evaluation of nociception during pediatric surgery: A topical review. J. Pers. Med. 2023; 13(2): 260. PubMed Abstract | Publisher Full Text | Free Full Text

[11] 11. Nishiyama T: Recent advance in patient monitoring. Korean J. Anesthesiol. 2010; 59(3): 144–159. PubMed Abstract | Publisher Full Text | Free Full Text

[12] 12. Kehlet H, Wilmore DW: Multimodal strategies to improve surgical outcome. Am. J. Surg. 2002; 183(6): 630–641. Publisher Full Text

[13] 13. Bruhn J, Myles PS, Sneyd R, et al.: Depth of anaesthesia monitoring: what’s available, what’s validated and what’s next? Br. J. Anaesth. 2006; 97(1): 85–94. PubMed Abstract | Publisher Full Text

[14] 14. Edry R, Recea V, Dikust Y, et al.: Preliminary intraoperative validation of the nociception level index: A noninvasive nociception monitor. Anesthesiology. 2016; 125(1): 193–203. PubMed Abstract | Publisher Full Text

[15] 15. Stewart JA, Särkelä MOK, Wennervirta J, et al.: Novel insights on association and reactivity of bispectral Index, frontal electromyogram, and autonomic responses in nociception-sedation monitoring of critical care patients. BMC Anesthesiol. 2022; 22(1): 353. PubMed Abstract | Publisher Full Text | Free Full Text

[16] 16. Ben-Israel N, Kliger M, Zuckerman G, et al.: Monitoring the nociception level: a multi-parameter approach. J. Clin. Monit. Comput. 2013; 27(6): 659–668. PubMed Abstract | Publisher Full Text

[17] 17. Struys MM, Jensen EW, Smith W, et al.: Performance of the ARX-derived auditory evoked potential index as an indicator of anesthetic depth: a comparison with bispectral index and hemodynamic measures during propofol administration. Anesthesiology. 2002; 96(4): 803–816. PubMed Abstract | Publisher Full Text

[18] 18. Huiku M, Uutela K, van Gils M , et al.: Assessment of surgical stress during general anaesthesia. Br. J. Anaesth. 2007; 98(4): 447–455. Publisher Full Text

[19] 19. Ledowski T, Ang B, Schmarbeck T, et al.: Monitoring of sympathetic tone to assess postoperative pain: skin conductance vs surgical stress index. Anaesthesia. 2009; 64(7): 727–731. PubMed Abstract | Publisher Full Text

[20] 20. Yang G, Jiang M, Ouyang W, et al.: IoT-based remote pain monitoring system: from device to cloud platform. IEEE J. Biomed. Health Inform. 2018; 22(6): 1711–1719. PubMed Abstract | Publisher Full Text

[21] 21. Tanaka G, Sawada Y, Matsumura K, et al.: Finger arterial compliance as determined by transmission of light during mental stress and reactive hyperaemia. Eur. J. Appl. Physiol. 2002; 87(6): 562–567. PubMed Abstract | Publisher Full Text

[22] 22. Naohiro Y: Case study of Increase of normalized pulse volume to the critical item in the field concealed information test [Japanese]. Technol. Jurisdiction. 2010; 15(1): 65–74.

[23] 23. Tanaka G, Kawana S, Sawada Y: Yamakoshi K Normalized pulse volume and blood volume as separate indices of finger arterial and venous vascular tone: examination under propofol. Jpn. Psychol. Res. 2003; 45(1): 50–59. Publisher Full Text

[24] 24. Atef HM, Fattah SA, Gaffer ME, et al.: Perfusion index versus non-invasive hemodynamic parameters during insertion of i-gel, classic laryngeal mask airway and endotracheal tube. Indian J. Anaesth. 2013; 57(2): 156–162. PubMed Abstract | Publisher Full Text | Free Full Text

[25] 25. Shah PN, Kezo A: Perfusion index during endotracheal intubation and extubation: A prospective observational study. Saudi J Anaesth. 2023; 17(1): 7–11. PubMed Abstract | Publisher Full Text | Free Full Text

[26] 26. Rajan K, Dave N, Dias R, et al.: Perfusion index as a predictor of working pediatric caudal block under general anesthesia- A prospective observational study. J. Anaesthesiol. Clin. Pharmacol. 2022; 38(4): 635–639. PubMed Abstract | Publisher Full Text | Free Full Text

[27] 27. Chen Y, Fu Q, Mi WD: Effects of stroke volume variation, pulse pressure variation, and pleth variability index in predicting fluid responsiveness during different positive end expiratory pressure in prone position. Zhongguo Yi Xue Ke Xue Yuan Xue Bao Acta Acad. Med. Sin. 2015; 37(2): 179–184. PubMed Abstract | Publisher Full Text

[28] 28. Zimmermann M, Feibicke T, Keyl C, et al.: Accuracy of stroke volume variation compared with pleth variability index to predict fluid responsiveness in mechanically ventilated patients undergoing major surgery. Eur. J. Anaesthesiol. 2010; 27(6): 555–561. PubMed Abstract | Publisher Full Text

[29] 29. Ganter MT, Geisen M, Hartnack S, et al.: Prediction of fluid responsiveness in mechanically ventilated cardiac surgical patients: the performance of seven different functional hemodynamic parameters. BMC Anesthesiol. 2018; 18(1): 55. PubMed Abstract | Publisher Full Text | Free Full Text

[30] 30. Weber F, Rashmi BK, Karaoz-Bulut G, et al.: The predictive value of the Pleth Variability Index on fluid responsiveness in spontaneously breathing anaesthetized children-A prospective observational study. Paediatr. Anaesth. 2020; 30(10): 1124–1131. PubMed Abstract | Publisher Full Text | Free Full Text

[31] 31. Wu Y, Zhang F, Sun K, et al.: Evaluation of pleth variability index for predicting hypotension during induction of anesthesia in surgical patients. Zhonghua Yi Xue Za Zhi. 2014; 94(40): 3167–3170. PubMed Abstract

[32] 32. DeBarros M, Causey MW, Chesley P, et al.: Reliability of continuous non-invasive assessment of hemoglobin and fluid responsiveness: impact of obesity and abdominal insufflation pressures. Obes. Surg. 2015; 25(7): 1142–1148. PubMed Abstract | Publisher Full Text

[33] 33. Kim SJ, Kim SY, Lee HS, et al.: Ability of dynamic preload indices to predict fluid responsiveness in a high femoral-to-radial arterial pressure gradient: a retrospective study. Anesth Pain Med (Seoul). 2021; 16(4): 360–367. PubMed Abstract | Publisher Full Text | Free Full Text

[34] 34. Lee HC, Tsai YF, Tsai HI, et al.: Pulse oximeter-derived pleth variability index is a reliable indicator of cardiac preload in patients undergoing liver transplantation. Transplant. Proc. 2016; 48(4): 1055–1058. PubMed Abstract | Publisher Full Text

[35] 35. Loupec T, Nanadoumgar H, Frasca D, et al.: Pleth variability index predicts fluid responsiveness in critically ill patients. Crit. Care Med. 2011; 39(2): 294–299. PubMed Abstract | Publisher Full Text

[36] 36. Keller G, Cassar E, Desebbe O, et al.: Ability of pleth variability index to detect hemodynamic changes induced by passive leg raising in spontaneously breathing volunteers. Crit. Care. 2008; 12(2): R37. PubMed Abstract | Publisher Full Text | Free Full Text

[37] 37. Pişkin Ö, Öz İİ: Accuracy of pleth variability index compared with inferior vena cava diameter to predict fluid responsiveness in mechanically ventilated patients. Medicine. 2017; 96(47): e8889. PubMed Abstract | Publisher Full Text | Free Full Text

[38] 38. Lu W, Dong J, Xu Z, et al.: The pleth variability index as an indicator of the central extracellular fluid volume in mechanically ventilated patients after anesthesia induction: comparison with initial distribution volume of glucose. Med. Sci. Monit. 2014; 20(386–92): 386–392. Publisher Full Text

[39] 39. Haas S, Trepte C, Hinteregger M, et al.: Prediction of volume responsiveness using pleth variability index in patients undergoing cardiac surgery after cardiopulmonary bypass. J. Anesth. 2012; 26(5): 696–701. PubMed Abstract | Publisher Full Text

[40] 40. Desebbe O, Boucau C, Farhat F, et al.: The ability of pleth variability index to predict the hemodynamic effects of positive end-expiratory pressure in mechanically ventilated patients under general anesthesia. Anesth. Analg. 2010; 110(3): 792–798. PubMed Abstract | Publisher Full Text

[41] 41. Fu Q, Mi WD, Zhang H: Stroke volume variation and pleth variability index to predict fluid responsiveness during resection of primary retroperitoneal tumors in Hans Chinese. Biosci. Trends. 2012; 6(1): 38–43. PubMed Abstract | Publisher Full Text

[42] 42. Cai QF, Mi WD, Yuan WX: The ability of pleth variability index to predict fluid responsiveness in mechanically ventilated patients under general anaesthesia. Zhonghua Wai Ke Za Zhi Chin J. Surg. 2010; 48(21): 1628–1632. PubMed Abstract

[43] 43. Monnet X, Guérin L, Jozwiak M, et al.: Pleth variability index is a weak predictor of fluid responsiveness in patients receiving norepinephrine. Br. J. Anaesth. 2013; 110(2): 207–213. PubMed Abstract | Publisher Full Text

[44] 44. García-Soler P, Camacho Alonso JM, González-Gómez JM, et al.: Noninvasive hemoglobin monitoring in critically ill pediatric patients at risk of bleeding. Monitorización no invasiva transcutánea de la concentración de hemoglobina en pacientes críticos pediátricos con riesgo de sangrado. Med. Intensiva. 2017; 41(4): 209–215. PubMed Abstract | Publisher Full Text

[45] 45. Gelberg J, Jonmarker C, Stenqvist O, et al.: Intravenous boluses of fentanyl, 1 μg kg⁻¹, and remifentanil, 0.5 μg kg⁻¹, give similar maximum ventilatory depression in awake volunteer. Br. J. Anaesth. 2012; 108(6): 1028–1034. PubMed Abstract | Publisher Full Text

[46] 46. Smith BH, Elliott AM, Hannaford PC: Pain and subsequent mortality and cancer among women in the Royal College of General Practitioners Oral Contraception Study. Br. J. Gen. Pract. 2003; 53(486): 45–46. PubMed Abstract

[47] 47. Macfarlane GJ, McBeth J, Silman AJ: Widespread body pain and mortality: prospective population based study. BMJ (Clin Res Ed). 2001; 323(7314): 662–665. PubMed Abstract | Publisher Full Text | Free Full Text

[48] 48. Velik-Salchner C: Computed advisory systems in daily practice for predicting concentrations and effects of combined anesthetics: a new field in anesthesia? Minerva Anestesiol. 2015; 81(11): 1151–1152. PubMed Abstract

[49] 49. Rasmussen LS, Larsen K, Houx P, et al.: The International Study of Postoperative Cognitive Dysfunction. The assessment of postoperative cognitive function. Acta Anaesthesiol. Scand. 2001; 45(3): 275–289. Publisher Full Text

[50] 50. Shu AH, Wang Q, Chen XB: Effect of different depths of anesthesia on postoperative cognitive function in laparoscopic patients: a randomized clinical trial. Curr. Med. Res. Opin. 2015; 31(10): 1883–1887. PubMed Abstract | Publisher Full Text

[51] 51. Amaram-Davila J, Davis M, Reddy A: Opioids and cancer mortality. Curr. Treat. Options Oncol. 2020; 21(3): 22. Publisher Full Text

[52] 52. Oswald N, Halle-Smith J, Kerr A, et al.: Perioperative immune function and pain control may underlie early hospital readmission and 90 day mortality following lung cancer resection: A prospective cohort study of 932 patients. Eur. J. Surg. Oncol. 2019; 45(5): 863–869. PubMed Abstract | Publisher Full Text

[53] 53. Kata V, Novitch MB, Jones MR, et al.: Opioid addiction, diversion, and abuse in chronic and cancer pain. Curr. Opin. Support. Palliat. Care. 2018; 12(2): 124–130. PubMed Abstract | Publisher Full Text

[54] 54. Florence C, Luo F, Rice K: The economic burden of opioid use disorder and fatal opioid overdose in the United States, 2017. Drug Alcohol Depend. 2021; 218: 108350. PubMed Abstract | Publisher Full Text | Free Full Text

[55] 55. Krausz RM, Westenberg JN, Ziafat K: The opioid overdose crisis as a global health challenge. Curr. Opin. Psychiatry. 2021; 34(4): 405–412. PubMed Abstract | Publisher Full Text

[56] 56. Chen X, Thee C, Gruenewald M, et al.: Comparison of surgical stress index-guided analgesia with standard clinical practice during routine general anesthesia: a pilot study. Anesthesiology. 2010; 112(5): 1175–1183. PubMed Abstract | Publisher Full Text

[57] 57. Farhang B, Mathews DM: Pain monitor: reality or fantasy in ambulatory patients. Curr. Opin. Anaesthesiol. 2019; 32(6): 727–734. Publisher Full Text

[58] 58. Walter S, Gruss S, Limbrecht-Ecklundt K, et al.: Automatic pain quantification using autonomic parameters. Psychol. Neurosci. 2014; 7(3): 363–380. Publisher Full Text

[59] 59. Jakubów P, Kosciuczuk U, Garkowski A, et al.: Possibility of assessing pain with biomarkers in psychiatric disorders.2021. Publisher Full Text

[60] 60. Fernandez Rojas R, Huang X, Ou KL: A machine learning approach for the identification of a biomarker of human pain using fNIRS. Sci. Rep. 2019; 9(1): 5645. PubMed Abstract | Publisher Full Text | Free Full Text

[61] 61. Arendt-Nielsen L, Curatolo M: Mechanistic, translational, quantitative pain assessment tools in profiling of pain patients and for development of new analgesic compounds. Scand J. Pain. 2013; 4(4): 226–230. PubMed Abstract | Publisher Full Text

[62] 62. van der Miesen MM , Lindquist MA, Wager TD: Neuroimaging-based biomarkers for pain: state of the field and current directions. Pain Rep. 2019; 4(4): e751. PubMed Abstract | Publisher Full Text | Free Full Text

[63] 63. Cowen R, Stasiowska M, Laycock H, et al.: Assessing pain objectively: the use of physiological markers. Anaesthesia. 2015; 70(7): 828–847. Publisher Full Text

[64] 64. Melia U, Vallverdú M, Borrat X, et al.: Prediction of nociceptive responses during sedation by linear and non-linear measures of EEG signals in high frequencies. PLoS One. 2015; 10(4): e0123464. Publisher Full Text

[65] 65. Declaration of Helsinki: BMJ. 1996; 313. Publisher Full Text

Normalized Pulse Volume as a Superior Predictor of Respiration Recovery and Quantification of Nociception Anti-nociception Balance Compared to Opioid Effect Site Concentration: A Prospective, Observational Study

Abstract

Background

Methods

Results

Conclusion

Keywords

Introduction

Methods

Study approval

Patient selection

Table 1. Demographics of cases.

Table 2. Demographics of diseases.

Table 3. Demographics of procedures.

Procedure protocol

Measurement details

Statistical analysis

Results

Figure 1. Scatter plot of NPV and CE.

Figure 2. Distribution of NPV and CE.

Figure 3. Standardized distribution of NPV and CE.

Figure 4. Box plot of CoV of PI and CE.

Figure 5. Bootstrapped distribution of CoV for PI and CE.

Discussion

Comparison with previous studies

Comparative performance of NPV and CE

Potential for multimodal approaches

Study limitations

Future studies

Author contribution

Data availability

Reporting guidelines

Acknowledgments

References

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