Prediction of acute postoperative pain based on intraoperative nociception level (NOL) index values: the impact of machine learning-based analysis

Morisson, Louis; Nadeau-Vallée, Mathieu; Espitalier, Fabien; Laferrière-Langlois, Pascal; Idrissi, Moulay; Lahrichi, Nadia; Gélinas, Céline; Verdonck, Olivier; Richebé, Philippe

doi:10.1007/s10877-022-00897-z

Cited by 11 publications

(16 citation statements)

References 19 publications

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Section: Pain and Nociception Monitorsmentioning

confidence: 99%

“…79 One more recent ancillary analysis of the NOLGYN study 67 evaluated the ability of new machine-learning algorithms to predict moderate to severe acute postoperative pain based on intraoperative NOL values. 80 What Might the Future Hold? Bill Gates said: "We always overestimate the change that will occur in the next 2 years and underestimate the changes that will occur in the next 10."…”

Section: Middle Latency Evoked Auditory Potentialsmentioning

confidence: 99%

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Depth of Anesthesia and Nociception Monitoring: Current State and Vision For 2050

Laferrière-Langlois,

Morisson,

Jeffries

et al. 2024

Anesthesia &Amp; Analgesia

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Anesthesia objectives have evolved into combining hypnosis, amnesia, analgesia, paralysis, and suppression of the sympathetic autonomic nervous system. Technological improvements have led to new monitoring strategies, aimed at translating a qualitative physiological state into quantitative metrics, but the optimal strategies for depth of anesthesia (DoA) and analgesia monitoring continue to stimulate debate. Historically, DoA monitoring used patient’s movement as a surrogate of awareness. Pharmacokinetic models and metrics, including minimum alveolar concentration for inhaled anesthetics and target-controlled infusion models for intravenous anesthesia, provided further insights to clinicians, but electroencephalography and its derivatives (processed EEG; pEEG) offer the potential for personalization of anesthesia care. Current studies appear to affirm that pEEG monitoring decreases the quantity of anesthetics administered, diminishes postanesthesia care unit duration, and may reduce the occurrence of postoperative delirium (notwithstanding the difficulties of defining this condition). Major trials are underway to further elucidate the impact on postoperative cognitive dysfunction. In this manuscript, we discuss the Bispectral (BIS) index, Narcotrend monitor, Patient State Index, entropy-based monitoring, and Neurosense monitor, as well as middle latency evoked auditory potential, before exploring how these technologies could evolve in the upcoming years. In contrast to developments in pEEG monitors, nociception monitors remain by comparison underdeveloped and underutilized. Just as with anesthetic agents, excessive analgesia can lead to harmful side effects, whereas inadequate analgesia is associated with increased stress response, poorer hemodynamic conditions and coagulation, metabolic, and immune system dysregulation. Broadly, 3 distinct monitoring strategies have emerged: motor reflex, central nervous system, and autonomic nervous system monitoring. Generally, nociceptive monitors outperform basic clinical vital sign monitoring in reducing perioperative opioid use. This manuscript describes pupillometry, surgical pleth index, analgesia nociception index, and nociception level index, and suggest how future developments could impact their use. The final section of this review explores the profound implications of future monitoring technologies on anesthesiology practice and envisages 3 transformative scenarios: helping in creation of an optimal analgesic drug, the advent of bidirectional neuron-microelectronic interfaces, and the synergistic combination of hypnosis and virtual reality.

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Section: Pain and Nociception Monitorsmentioning

confidence: 99%

Section: Middle Latency Evoked Auditory Potentialsmentioning

confidence: 99%

Depth of Anesthesia and Nociception Monitoring: Current State and Vision For 2050

Laferrière-Langlois,

Morisson,

Jeffries

et al. 2024

Anesthesia &Amp; Analgesia

Self Cite

View full text Add to dashboard Cite

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“…We included predictors from EHR and wearable devices that might affect acute postoperative pain and chronic opioid use. [4][5][6][7][8][9][10] The variables were grouped into five domains: (1) demographics (e.g., age, gender, race, and ethnicity) and socioeconomic status (e.g., income, education);…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Recent studies show that the use of Machine Learning (ML) models might predict pain and opioid requirements following surgery. [4][5][6][7][8][9][10] However, ML applications in acute pain management have been limited by population, surgery type, and the electronic health records (EHR) predictors used for model development. The present study explored the potential for ML models to predict postoperative acute pain and chronic opioid use.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of this study was to explore the state-of-the-art application of ML techniques to predict acute postoperative pain and chronic opioid use. We: (1) developed a range of ML models; (2) compared model performance with others; [4][5][6][7][8][9][10] and (3) used advanced visualization technique (Shapley additive explanations (SHAP) 11 ) to identify the most pivotal predictors of model outcomes and individual patient outcomes.…”

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confidence: 99%

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Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data

Soley,

Speed,

Xie

et al. 2024

Appl Clin Inform

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Background: Managing acute postoperative pain and minimizing chronic opioid use is crucial for patient recovery and long-term well-being. Objective: This study explored using preoperative electronic health records (EHR) and wearable device data for machine-learning models that predict postoperative acute pain and chronic opioid use. Methods: The study cohort consisted of ~347 All of Us Research Program participants who underwent one of eight surgical procedures and shared EHR and wearable device data. We developed four machine learning models and used the Shapley additive explanations (SHAP) technique to identify the most relevant predictors of acute pain and chronic opioid use. Results: The stacking ensemble model achieved the highest accuracy in predicting acute pain (0.68) and chronic opioid use (0.89). The area under the curve (AUC) score for severe pain vs. other pain was highest (0.88) when predicting acute post-operative pain. Values of logistic regression, random forest, extreme gradient boosting, and stacking ensemble ranged from 0.74 to 0.90 when predicting postoperative chronic opioid use. Variables from wearable devices played a prominent role in predicting both outcomes. Conclusions: SHAP detection of individual risk factors for severe pain can help healthcare providers tailor pain management plans. Accurate prediction of postoperative chronic opioid use before surgery can help mitigate the risk for the outcomes we studied. Prediction can also reduce the chances of opioid overuse and dependence. Such mitigation can promote safer and more effective pain control for patients during their recovery.

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Predicting Chronic Pain and Treatment Outcomes Using Machine Learning Models Based on High-dimensional Clinical Data From a Large Retrospective Cohort

Wu,

Chen,

et al. 2024

Clinical Therapeutics

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Prediction of acute postoperative pain based on intraoperative nociception level (NOL) index values: the impact of machine learning-based analysis

Cited by 11 publications

References 19 publications

Depth of Anesthesia and Nociception Monitoring: Current State and Vision For 2050

Depth of Anesthesia and Nociception Monitoring: Current State and Vision For 2050

Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data

Predicting Chronic Pain and Treatment Outcomes Using Machine Learning Models Based on High-dimensional Clinical Data From a Large Retrospective Cohort

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