Neuromuscular blocking agents (NMBAs) are commonly employed in anesthesia to facilitate intubation and improve surgical working conditions. Understanding their pharmacokinetics (PK) and pharmacodynamics (PD) is crucial for optimizing their administration. PK describes drug absorption, distribution, metabolism, and elimination, while PD links drug concentration to its pharmacological effects. Integrating PK and PD information through PKPD modeling offers insights into the relationship between drug concentration and effect. Recent advancements in machine learning (ML) have shown promise in modeling the PD of anesthetics, offering potential benefits over traditional PKPD models. This study aimed to develop an automated data logger for recording neuromuscular transmission (NMT) measurements and rocuronium infusion data during surgery. A secondary goal was to predict TOF ratios using both traditional PKPD models and newer ML techniques. The experimental setup involved development of data logger software. Data preprocessing consolidated data, removed outliers, and applied interpolation for missing values. Machine learning models, including linear regression, decision trees, and extreme gradient boosting, were trained and evaluated using double Leave-One-Group-Out cross-validation. Additionally, a traditional PKPD model estimated pharmacokinetic parameters based on patient characteristics and rocuronium administration data. Model performance was assessed using metrics such as Root Mean Squared Error (RMSE), Normalized RMSE (NRMSE), R-squared (R²), and the Pearson correlation coefficient. From March 23 to June 20, 2023, a prospective observational study at Leiden University Medical Center (LUMC) included 42 patients in the operating room. Data were collected from three distinct monitors using data logger software. The collected data was divided into TOF-Cuff and GE NMT monitor subsets, excluding continuous rocuronium infusion records. In terms of model performance, machine learning models displayed suboptimal results when applied to GE NMT monitor data, indicated by high RMSE and low R² values. In contrast, basic and optimized PKPD models exhibited better predictive capabilities. Similar trends were observed in the performance evaluation of TOF-Cuff data, with machine learning models less effective compared to PKPD models. An in-depth analysis of NRMSE revealed outliers, mainly in the optimized PKPD model. Cumulative distribution plots highlighted performance variations across subjects, particularly in the TOF-Cuff results. The dataset comprised 42 subjects undergoing surgical procedures, but the effective sample size for analysis was limited due to deep neuromuscular blockade, sensor placement issues, and data loss. Traditional pharmacokinetic-pharmacodynamic (PKPD) models, based on data from 423 patients, outperformed machine learning models in predicting Train of Four (TOF) ratios, as the latter faced overfitting challenges with a smaller dataset. Future directions suggest collecting more extensive data (ideally closer to 100 subjects) to improve machine learning model performance and possibly include features like time until full neuromuscular blockade recovery. Additionally, identifying the most critical features behind machine learning predictions can help streamline computational methods. Overall, refining the software, increasing data, and feature analysis can enhance machine learning-based neuromuscular blockade prediction. In conclusion, this study introduced a novel data logger software for recording neuromuscular blockade data during surgery at LUMC. While machine learning approaches fell short in approximating TOF ratios, future research with expanded datasets and more comprehensive feature analysis holds promise for the development of more robust machine learning models.

Introduction Opioids are vital for pain management but are highly addictive and may lead to opioid-induced respiratory depression (OIRD), which is the primary cause of death related to both prescription and illicit opioid use. This study employed unsupervised machine learning (ML) to examine potential changes in cluster patterns post-opioid administration and their relationship with respiratory depression levels. Additionally, comprehensive data analysis was conducted based on questions that resulted from observations of cluster behaviour. Methods Following preprocessing, a preliminary study evaluated three different models, each trained on the baseline and post-opioid epochs, aiming to identify the most efficient distance metrics and feature space combinations. These models included principal component analysis with Euclidean distance, computed feature space with Euclidean distance, and time series with dynamic time warping. Next, the preferred approach, determined by testing different hypotheses regarding the desired cluster behaviour, underwent further refinement, and predictions were generated for post-antagonist epochs. Finally, feature influence was determined and questions were identified for the data-analysis. Results In total, 34 subjects were included, resulting in a total of 6630 epochs for model development. Based on the preliminary study, it was decided to opt for a fuzzy clustering approach using calculated features as input, resulting in membership values indicating the probability of an epoch belonging to a certain cluster. A change in membership values was observed post-opioid as well as a recovery to baseline values post-antagonist in the majority of subjects when clustering was obtained for each subject individually. However, it was expressed as a sudden switch followed by a prolonged plateau phase rather than a gradual transition which was expected. Conversely, when trained collectively over all subjects, the majority of subjects showed no difference, probably due to the presence of inter-subject variability. Nevertheless, SHAP value analysis identified the same feature behaviour among subjects, despite variations in orientation and positioning, hinting at the potential for adjustment using static features. Yet, no significant correlations were found between static features and feature behaviour within this study. Conclusion In this study, a fuzzy clustering model was implemented, incorporating SHAP value analysis to enhance the interpretability of the clustering results. Although the model successfully identified changes in respiratory flow patterns associated with OIRD and subsequent recovery after naloxone administration, it requires further refinement.

Introduction Opioids are vital for pain management but are highly addictive and may lead to opioid-induced respiratory depression (OIRD), which is the primary cause of death related to both prescription and illicit opioid use. This study employed unsupervised machine learning (ML) to examine potential changes in cluster patterns post-opioid administration and their relationship with respiratory depression levels. Additionally, comprehensive data analysis was conducted based on questions that resulted from observations of cluster behaviour. Methods Following preprocessing, a preliminary study evaluated three different models, each trained on the baseline and post-opioid epochs, aiming to identify the most efficient distance metrics and feature space combinations. These models included principal component analysis with Euclidean distance, computed feature space with Euclidean distance, and time series with dynamic time warping. Next, the preferred approach, determined by testing different hypotheses regarding the desired cluster behaviour, underwent further refinement, and predictions were generated for post-antagonist epochs. Finally, feature influence was determined and questions were identified for the data-analysis. Results In total, 34 subjects were included, resulting in a total of 6630 epochs for model development. Based on the preliminary study, it was decided to opt for a fuzzy clustering approach using calculated features as input, resulting in membership values indicating the probability of an epoch belonging to a certain cluster. A change in membership values was observed post-opioid as well as a recovery to baseline values post-antagonist in the majority of subjects when clustering was obtained for each subject individually. However, it was expressed as a sudden switch followed by a prolonged plateau phase rather than a gradual transition which was expected. Conversely, when trained collectively over all subjects, the majority of subjects showed no difference, probably due to the presence of inter-subject variability. Nevertheless, SHAP value analysis identified the same feature behaviour among subjects, despite variations in orientation and positioning, hinting at the potential for adjustment using static features. Yet, no significant correlations were found between static features and feature behaviour within this study. Conclusion In this study, a fuzzy clustering model was implemented, incorporating SHAP value analysis to enhance the interpretability of the clustering results. Although the model successfully identified changes in respiratory flow patterns associated with OIRD and subsequent recovery after naloxone administration, it requires further refinement.