EEG-derived pain threshold index for prediction of postoperative pain in patients undergoing laparoscopic urological surgery: a comparison with surgical pleth index

Predicting postoperative pain in children: an observational study using the pain threshold Index

Objective

While the pain threshold index (PTI) holds potential as a tool for monitoring analgesia-pain equilibrium, its precision in forecasting postoperative pain in children remains unconfirmed. This study's primary aim was to assess the PTI's predictive precision for postoperative pain.

Methods

Children (aged 2-16 years) undergoing general surgery under general anesthesia were included. Within 5 min prior to the patient's emergence from surgery, data including PTI, wavelet index (WLI), heart rates (HR) and mean arterial pressure (MAP) were collected. Subsequently, a 15-min pain assessment was conducted following the patient's awakening. The accuracy of these indicators in discerning between mild and moderate to severe postoperative pain was evaluated through receiver operating characteristic (ROC) analysis.

Results

The analysis encompassed data from 90 children. ROC analysis showed that PTI was slightly better than HR, MAP and WLI in predicting postoperative pain, but its predictive value was limited. The area under the curve (AUC) was 0.659 [0.537∼0.780] and the optimal threshold was 65[64-67]. Sensitivity and specificity were determined at 0.90 and 0.50, respectively. In a multivariable logistic regression model, a higher predictive accuracy was found for a multivariable predictor combining PTI values with gender, BMI, HR and MAP (AUC, 0.768; 95%CI, 0.669-0.866). Upon further scrutinizing the age groups, PTI's AUC was 0.796 for children aged 9-16, 0.656 for those aged 4-8, and 0.601 for younger individuals.

Conclusions

PTI, when used alone, lacks acceptable accuracy in predicting postoperative pain in children aged 2 to 16 years. However, when combined with other factors, it shows improved predictive accuracy. Notably, PTI appears to be more accurate in older children.

EEG-derived pain threshold index-guided versus standard care during propofol-remifentanil anesthesia: A randomized controlled trial

Purpose

The pain threshold index (PTI), a novel index of nociception based on spontaneous EEG wavelet analysis, has been reported to provide reliable accuracy for predicting postoperative pain and hemodynamic reactivity. The present study is aimed to investigate whether PTI-guided analgesia reduces the pain intensity and rate of remedial analgesia in the post-anesthesia care unit (PACU).

Methods

A total of 122 females undergoing elective gynecologic surgeries had been randomized to receive either PTI-guided analgesia (PTI group) or standard clinical care (control group). Remifentanil administration in the PTI group was guided by PTI to maintain the value between 40 and 65, while that in the control group was guided by hemodynamic changes. The primary outcome was remedial analgesia rate in the PACU. The postoperative pain scores, intraoperative remifentanil requirements, opioid-related adverse events and perioperative serum stress hormone concentrations between the two groups were also compared.

Findings

It was found that 23 of 58 patients (40%) in the control group and 8 of 58 patients (14%) in the PTI group needed remedial analgesia. The relative risk of receiving remedial analgesia was 2.88 (95% CI, 1.40-5.89, P = 0.002) in the control group. Sufentanil consumption in the PACU (μg) was lower in the PTI group (P = 0.002) than in the control group. Remifentanil and propofol consumption, opioid-related adverse events between these two groups were comparable.

Implications

PTI-guided analgesia during gynaecologic operations resulted in 25.87% less remedial analgesia. However, studies with different PTI thresholds and larger, more diverse populations should be conducted to further demonstrate the clinical effectiveness of PTI.

Validation of three nociception indices to predict immediate postoperative pain before emergence from general anaesthesia: a prospective double-blind, observational study

BACKGROUND

Nociception monitoring devices are designed to estimate nociception during general anaesthesia. We evaluated the predictive accuracy of heart rate and three nociception indices to predict postoperative pain before emergence from general anaesthesia.

METHODS

In patients undergoing trauma or orthopaedic surgery, HR, Surgical Pleth Index® (SPI), Pupillary Pain Index® (PPI), and Nociception Level® (NOL) were simultaneously recorded for 5 min after the end of surgery but before return of consciousness. After admission to the recovery room, pain scores were assessed regularly for 2 h. HR, SPI, PPI, and NOL were analysed for their predictive accuracy of postoperative pain and opioid consumption with assessment of area under the receiver operating characteristic (AUC) curves, Spearman rank-correlation coefficient, and regression modelling.

RESULTS

Data for 60 subjects were analysed. The AUC (95% confidence interval [95% CI]) of the predictive accuracy for moderate-to-severe postoperative pain differed between nociception indices (HR=0.46 [0.29-0.64], P=0.671; SPI=0.46 [0.31-0.61], P=0.621; PPI=0.52 [0.36-0.68], P=0.770; NOL=0.66 [0.51-0.81], P=0.038). In a multivariable logistic regression model, a higher predictive accuracy was found for a multivariable predictor combining NOL values with ASA physical status and information about use of regional anaesthesia (AUC=0.83 [0.72-0.94], P<0.001).

CONCLUSIONS

Heart rate, Surgical Pleth Index, Pupillary Pain Index, and Nociception Level measured before emergence from general anaesthesia do not yet have sufficient diagnostic accuracy for prediction of postoperative pain.

CLINICAL TRIAL REGISTRATION

NCT05063227.

Elevated Surgical Pleth Index at the End of Surgery Is Associated with Postoperative Moderate-to-Severe Pain: A Systematic Review and Meta-Analysis

Despite acceptance of the surgical pleth index (SPI) for monitoring the intraoperative balance between noxious stimulation and anti-nociception under general anesthesia, its efficacy for predicting postoperative moderate-to-severe pain remains unclear. We searched electronic databases (e.g., Google Scholar, MEDLINE, Cochrane Library, and EMBASE) to identify articles focusing on associations of SPI at the end of surgery with immediate moderate-to-severe pain in the postanesthesia care unit from inception to 7 July 2022. A total of six observational studies involving 756 adults published between 2016 and 2020 were eligible for quantitative syntheses. Pooled results revealed higher values of SPI in patients with moderate-to-severe pain than those without (mean difference: 7.82, 95% CI: 3.69 to 11.95, p = 0.002, I2 = 46%). In addition, an elevated SPI at the end of surgery was able to predict moderate-to-severe pain with a sensitivity of 0.71 (95% confidence interval (CI): 0.65–0.77; I2 = 29.01%) and a specificity of 0.58 (95% CI: 0.39–0.74; I2 = 79.31%). The overall accuracy based on the summary receiver operating characteristic (sROC) curve was 0.72. In conclusion, this meta-analysis highlighted the feasibility of the surgical pleth index to predict postoperative moderate-to-severe pain immediately after surgery. Our results from a limited number of studies warrant further investigations for verification.

Classification of Tonic Pain Experience based on Phase Connectivity in the Alpha Frequency Band of the Electroencephalogram using Convolutional Neural Networks

The complexity of brain activity involved in the generation of the experience of pain makes it hard to identify neural markers able to predict pain states. The within and between subjects variability of pain hinders the predictive potential of machine learning models trained across participants. This challenge can be tackled by implementing deep learning classifiers based on convolutional neural networks (CNNs). We targeted phase-based connectivity in the alpha band recorded with electroencephalography (EEG) during resting states and sensory conditions (eyes open [O] and closed [C] as resting states, and warm [W] and hot [H] water as sensory conditions). Connectivity features were extracted and re-organized as square matrices, because CNNs are effective in detecting the patterns from 2D data. To assess the classifier performance we implemented two complementary approaches: we 1) trained and tested the classifier with data from all participants, and 2) using a leave-one-out approach, that is excluding one participant at a time during training while using their data as a test set. The accuracy of binary classification between pain condition (H) and eyes open resting state (O) was 94.16% with the first approach, and 61.01 % with the leave-one-out approach. Clinical relevance-Further validation of the CNN classifier may help caregivers track the rehabilitation of chronic pain patients and dynamically modify the therapy. Further refinement of the model may allow its application in critical care setting with unresponsive patients to identify pain-like states otherwise incommunicable to medical personnel.

Electroencephalogram-derived pain index for evaluating pain during labor

Background

The discriminative ability of a point-of-care electroencephalogram (EEG)-derived pain index (Pi) for objectively assessing pain has been validated in chronic pain patients. The current study aimed to determine its feasibility in assessing labor pain in an obstetric setting.

Methods

Parturients were enrolled from the delivery room at the department of obstetrics in a tertiary hospital between February and June of 2018. Pi values and relevant numerical rating scale (NRS) scores were collected at different stages of labor in the presence or absence of epidural analgesia. The correlation between Pi values and NRS scores was analyzed using the Pearson correlation analysis. The receiver operating characteristic (ROC) curve was plotted to estimate the discriminative capability of Pi to detect labor pain in parturients.

Results

Eighty paturients were eligible for inclusion. The Pearson correlation analysis exhibited a positive correlation between Pi values and NRS scores in parturients (r = 0.768, P < 0.001). The ROC analysis revealed a cut-off Pi value of 18.37 to discriminate between mild and moderate-to-severe labor pain in parturients. Further analysis indicated that Pi values had the best diagnostic accuracy reflected by the highest area under the curve (AUC) of 0.857, with a sensitivity and specificity of 0.767 and 0.833, respectively, and a Youden index of 0.6. Subgroup analyses further substantiated the correlations between Pi values and NRS scores, especially in parturients with higher pain intensity.

Conclusion

This study indicates that Pi values derived from EEGs significantly correlate with the NRS scores, and can serve as a way to quantitatively and objectively evaluate labor pain in parturients.

Predictive value of EEG-derived pain threshold index for acute postoperative pain in children

BACKGROUND

Electroencephalogram (EEG)-derived pain threshold index (PTI) has been developed as a novel pain recognition indicator and has been proved to be useful in the prediction of acute postoperative pain in adults. Evidence of its usability in children is limited. The aim of this study was to investigate the prediction value of this novel pain indicator PTI for acute postoperative pain in children.

METHODS

A total of 80 patients undergoing laparoscopic surgery under general anesthesia were enrolled. Blood pressure, heart rate (HR), surgical pleth index (SPI), PTI, and EEG-derived sedative index-wavelet index (WLI) data were recorded at the end of the surgery. The postoperative pain scores Face, Legs, Activity, Cry, Consolability (FLACC) were obtained in the emergence room 5 min after the children wake up. Receiver-operating characteristic curve was performed to analyze the predictive value of PTI, SPI, HR, and mean arterial pressure (MAP). The consistency between SPI and PTI was also evaluated.

RESULTS

Results showed that the areas under curves (95%CI) of PTI and SPI were 0.796 (95% CI: 0.694-0.895) and 0.753 (95% CI: 0.632-0.874), respectively, with the best cut-off value of 58 and 45 to discriminate between mild and moderate to severe pain.

CONCLUSION

This study suggested that PTI obtained at the end of the surgery could predict acute postoperative pain in children with an acceptable accuracy. It will help with early recognition and treatment of postoperative pain, thus reducing the pain in children. In addition, PTI had a good consistency with SPI in predicting acute postoperative pain in children.

Low Back Pain Assessment Based on Alpha Oscillation Changes in Spontaneous Electroencephalogram (EEG)

Objectively and accurately assessing pain in clinical settings is challenging. Previous studies showed that alpha oscillations of electroencephalogram data are correlated with subjective perceived pain. Based on this finding, this study is aimed at assessing chronic low back pain based on alpha oscillations. Multichannel electroencephalogram data were recorded from 27 subjects with chronic low back pain under the simple conditions of closing eyes or opening eyes. Spectral analyses were conducted to extract the alpha band responses, and the alpha powers were calculated for the two conditions, respectively. Normalized alpha power was calculated by subtracting the alpha power in the eyes-open condition from that in the eyes-closed condition. The correlation between the alpha power and the subjective pain intensity was evaluated in frontal, central, and posterior regions. The normalized alpha power in the central region was negatively correlated with the subjective pain intensity (R = -0.50, P = 0.01), with the strongest correlation occurring at the Cz electrode (R = -0.59, P = 0.04). The correlation analysis results demonstrated the possibility of using the differences of alpha spectral power between eyes-closed and eyes-open conditions as a measure for assessing chronic low back pain. The findings suggest that the normalized alpha power in the central region may be used as a measurable and quantitative indicator of chronic pain for clinical applications.