Naltrexone Induced Agitation Management: Employing a Hybrid Artificial Neural Network Model to Determine the Appropriate Dosage of Intravenous Diazepam

Aim: Agitation is a prevalent symptom of opioid withdrawal caused by naltrexone. Managing agitation induced by naltrexone poses challenges as current drug interventions are either ineffective or require intensive care. This study sought to determine the most effective diazepam dosage for alleviating naltrexone-induced agitation. Materials and Methods: This research examined a dataset comprising 615 patient medical records from Loghman Hakim Hospital in Tehran, Iran, focusing on cases of patients experiencing naltrexone-induced agitation. The dataset included individuals who were administered low-dose diazepam (<10 mg; 383 cases) and high-dose diazepam (>10 mg; 232 cases). The predictive performance of the developed models was assessed based on metrics such as accuracy, specificity, sensitivity, F1‑score, and ROC curve analysis. Results: The bat algorithm demonstrated the highest performance among meta-heuristic algorithms, achieving a score of 89.5% (0.895) at iteration 128. A comparative evaluation of five decision tree classifiers revealed that the Extra Trees Classifier surpassed others, attaining an accuracy of 0.8649, sensitivity of 0.8649, precision of 0.8645, F1-score of 0.8649, and area under the curve (AUC) of 0.9343. Following the determination of feature importance and training of a multilayer perceptron neural network with weighted features, the model exhibited superior performance with an accuracy of 0.91, sensitivity of 0.9, precision of 0.92, F1-score of 0.91, and AUC of 0.94. Conclusion: Features for predicting the appropriate dose of diazepam in patients with naltrexone-induced agitation included recent opioid use, Richmond Agitation-Sedation scale, amount of ingested naltrexone, pulse rate, systolic blood pressure, level of consciousness, serum levels of sodium, creatinine, and lactate dehydrogenase. Our research findings indicate that a weighted multilayer perceptron neural network shows promise in accurately forecasting the necessity of increased doses of diazepam for patients experiencing naltrexone-induced agitation. This is particularly evident when utilizing meta-heuristic techniques for feature selection and assigning importance of selected features based on the classifier with the highest AUC. This model could guide clinicians in tailoring diazepam doses to manage naltrexone induced agitation safely.