Cargando…

Intraoperative use of the machine learning-derived nociception level monitor results in less pain in the first 90 min after surgery

In this pooled analysis of two randomized clinical trials, intraoperative opioid dosing based on the nociception level-index produced less pain compared to standard care with a difference in pain scores in the post-anesthesia care unit of 1.5 (95% CI 0.8–2.2) points on an 11-point scale. The proport...

Descripción completa

Detalles Bibliográficos
Autores principales: van der Wal, Imeen, Meijer, Fleur, Fuica, Rivka, Silman, Zmira, Boon, Martijn, Martini, Chris, van Velzen, Monique, Dahan, Albert, Niesters, Marieke, Gozal, Yaacov
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9869062/
https://www.ncbi.nlm.nih.gov/pubmed/36700141
http://dx.doi.org/10.3389/fpain.2022.1086862
_version_ 1784876686886567936
author van der Wal, Imeen
Meijer, Fleur
Fuica, Rivka
Silman, Zmira
Boon, Martijn
Martini, Chris
van Velzen, Monique
Dahan, Albert
Niesters, Marieke
Gozal, Yaacov
author_facet van der Wal, Imeen
Meijer, Fleur
Fuica, Rivka
Silman, Zmira
Boon, Martijn
Martini, Chris
van Velzen, Monique
Dahan, Albert
Niesters, Marieke
Gozal, Yaacov
author_sort van der Wal, Imeen
collection PubMed
description In this pooled analysis of two randomized clinical trials, intraoperative opioid dosing based on the nociception level-index produced less pain compared to standard care with a difference in pain scores in the post-anesthesia care unit of 1.5 (95% CI 0.8–2.2) points on an 11-point scale. The proportion of patients with severe pain was lower by 70%. Severe postoperative pain remains a significant problem and associates with several adverse outcomes. Here, we determined whether the application of a monitor that detects intraoperative nociceptive events, based on machine learning technology, and treatment of such events reduces pain scores in the post-anesthesia care unit (PACU). To that end, we performed a pooled analysis of two trials in adult patients, undergoing elective major abdominal surgery, on the effect of intraoperative nociception level monitor (NOL)-guided fentanyl dosing on PACU pain was performed. Patients received NOL-guided fentanyl dosing or standard care (fentanyl dosing based on hemodynamic parameters). Goal of the intervention was to keep NOL at values that indicated absence of nociception. The primary endpoint of the study was the median pain score obtained in the first 90 min in the PACU. Pain scores were collected at 15 min intervals on an 11-point Likert scale. Data from 125 patients (55 men, 70 women, age range 21–86 years) were analyzed. Sixty-one patients received NOL-guided fentanyl dosing and 64 standard care. Median PACU pain score was 1.5 points (0.8–2.2) lower in the NOL group compared to the standard care; the proportion of patients with severe pain was 70% lower in the NOL group (p = 0.045). The only significant factor associated with increased odds for severe pain was the standard of care compared to NOL treatment (OR 6.0, 95% CI 1.4 −25.9, p = 0.017). The use of a machine learning-based technology to guide opioid dosing during major abdominal surgery resulted in reduced PACU pain scores with less patients in severe pain.
format Online
Article
Text
id pubmed-9869062
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-98690622023-01-24 Intraoperative use of the machine learning-derived nociception level monitor results in less pain in the first 90 min after surgery van der Wal, Imeen Meijer, Fleur Fuica, Rivka Silman, Zmira Boon, Martijn Martini, Chris van Velzen, Monique Dahan, Albert Niesters, Marieke Gozal, Yaacov Front Pain Res (Lausanne) Pain Research In this pooled analysis of two randomized clinical trials, intraoperative opioid dosing based on the nociception level-index produced less pain compared to standard care with a difference in pain scores in the post-anesthesia care unit of 1.5 (95% CI 0.8–2.2) points on an 11-point scale. The proportion of patients with severe pain was lower by 70%. Severe postoperative pain remains a significant problem and associates with several adverse outcomes. Here, we determined whether the application of a monitor that detects intraoperative nociceptive events, based on machine learning technology, and treatment of such events reduces pain scores in the post-anesthesia care unit (PACU). To that end, we performed a pooled analysis of two trials in adult patients, undergoing elective major abdominal surgery, on the effect of intraoperative nociception level monitor (NOL)-guided fentanyl dosing on PACU pain was performed. Patients received NOL-guided fentanyl dosing or standard care (fentanyl dosing based on hemodynamic parameters). Goal of the intervention was to keep NOL at values that indicated absence of nociception. The primary endpoint of the study was the median pain score obtained in the first 90 min in the PACU. Pain scores were collected at 15 min intervals on an 11-point Likert scale. Data from 125 patients (55 men, 70 women, age range 21–86 years) were analyzed. Sixty-one patients received NOL-guided fentanyl dosing and 64 standard care. Median PACU pain score was 1.5 points (0.8–2.2) lower in the NOL group compared to the standard care; the proportion of patients with severe pain was 70% lower in the NOL group (p = 0.045). The only significant factor associated with increased odds for severe pain was the standard of care compared to NOL treatment (OR 6.0, 95% CI 1.4 −25.9, p = 0.017). The use of a machine learning-based technology to guide opioid dosing during major abdominal surgery resulted in reduced PACU pain scores with less patients in severe pain. Frontiers Media S.A. 2023-01-09 /pmc/articles/PMC9869062/ /pubmed/36700141 http://dx.doi.org/10.3389/fpain.2022.1086862 Text en © 2023 van der Wal, Meijer, Fuica, Silman, Boon, Martini, van Velzen, Dahan, Niesters and Gozal. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pain Research
van der Wal, Imeen
Meijer, Fleur
Fuica, Rivka
Silman, Zmira
Boon, Martijn
Martini, Chris
van Velzen, Monique
Dahan, Albert
Niesters, Marieke
Gozal, Yaacov
Intraoperative use of the machine learning-derived nociception level monitor results in less pain in the first 90 min after surgery
title Intraoperative use of the machine learning-derived nociception level monitor results in less pain in the first 90 min after surgery
title_full Intraoperative use of the machine learning-derived nociception level monitor results in less pain in the first 90 min after surgery
title_fullStr Intraoperative use of the machine learning-derived nociception level monitor results in less pain in the first 90 min after surgery
title_full_unstemmed Intraoperative use of the machine learning-derived nociception level monitor results in less pain in the first 90 min after surgery
title_short Intraoperative use of the machine learning-derived nociception level monitor results in less pain in the first 90 min after surgery
title_sort intraoperative use of the machine learning-derived nociception level monitor results in less pain in the first 90 min after surgery
topic Pain Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9869062/
https://www.ncbi.nlm.nih.gov/pubmed/36700141
http://dx.doi.org/10.3389/fpain.2022.1086862
work_keys_str_mv AT vanderwalimeen intraoperativeuseofthemachinelearningderivednociceptionlevelmonitorresultsinlesspaininthefirst90minaftersurgery
AT meijerfleur intraoperativeuseofthemachinelearningderivednociceptionlevelmonitorresultsinlesspaininthefirst90minaftersurgery
AT fuicarivka intraoperativeuseofthemachinelearningderivednociceptionlevelmonitorresultsinlesspaininthefirst90minaftersurgery
AT silmanzmira intraoperativeuseofthemachinelearningderivednociceptionlevelmonitorresultsinlesspaininthefirst90minaftersurgery
AT boonmartijn intraoperativeuseofthemachinelearningderivednociceptionlevelmonitorresultsinlesspaininthefirst90minaftersurgery
AT martinichris intraoperativeuseofthemachinelearningderivednociceptionlevelmonitorresultsinlesspaininthefirst90minaftersurgery
AT vanvelzenmonique intraoperativeuseofthemachinelearningderivednociceptionlevelmonitorresultsinlesspaininthefirst90minaftersurgery
AT dahanalbert intraoperativeuseofthemachinelearningderivednociceptionlevelmonitorresultsinlesspaininthefirst90minaftersurgery
AT niestersmarieke intraoperativeuseofthemachinelearningderivednociceptionlevelmonitorresultsinlesspaininthefirst90minaftersurgery
AT gozalyaacov intraoperativeuseofthemachinelearningderivednociceptionlevelmonitorresultsinlesspaininthefirst90minaftersurgery