Mechanisms and Risk Factors Linking Neuroleptic Malignant Syndrome (NMS) to Dopaminergic and Autonomic Dysfunction

Neuroleptic Malignant Syndrome (NMS) is a rare but life-threatening neurological emergency that arises primarily from the use of dopamine antagonist antipsychotic medications. Clinically, it is characterized by hyperthermia, muscle rigidity, altered mental status, and signs of autonomic dysregulation. Despite being a well-documented phenomenon, the underlying pathophysiological mechanisms of NMS remain poorly understood, and early detection remains a clinical challenge. This study introduces a comprehensive and explainable data-driven framework aimed at elucidating the multifactorial etiology of NMS. We developed a high-fidelity synthetic dataset representing patients exposed to antipsychotic therapies and modelled key variables such as dopaminergic blockade, anticholinergic burden, autonomic instability, creatine kinase levels, and fever. Using this dataset, we performed logistic regression to evaluate risk contributions, XGBoost classification to determine feature importance, and survival analysis (Kaplan-Meier and Cox models) to assess the temporal dimension of disease progression. Additionally, a mechanistic network model was constructed to visualize how pharmacological and physiological components converge to produce the NMS phenotype. Our findings indicate that fever and elevated creatine kinase are robust biomarkers of NMS, while autonomic and dopaminergic pathways appear to interact synergistically to exacerbate clinical outcomes. The XGBoost model achieved strong predictive performance (AUC = 0.93), reinforcing the clinical relevance of our feature selection. Overall, this research bridges the gap between statistical inference, machine learning, and neuropharmacological theory. It lays the foundation for developing early-warning tools, risk stratification systems, and personalized interventions in psychiatry. Future directions include real-world validation, temporal modelling, and integration with electronic health record systems for clinical deployment.