Dynamic Monitoring and Network Analysis of Alcohol Withdrawal Symptoms: Implications for Psychiatric Nursing Practice

Background: Alcohol withdrawal syndrome (AWS) is a complex condition characterized by a range of symptoms that can vary in severity. Effective management of AWS during the acute phase is crucial for patient safety and treatment outcomes. This study aimed to investigate the temporal changes in alcohol withdrawal symptoms and identify key symptoms relevant to nursing care using a dynamic monitoring approach and network analysis techniques. Methods: The study included 82 inpatients with alcohol use disorder (AUD) admitted to Wenzhou Seventh People’s Hospital. Withdrawal symptoms were assessed using the Clinical Institute Withdrawal Assessment for Alcohol, Revised (CIWA‐Ar) scale at baseline and every 8 h up to 40 h. Repeated measures ANOVA and network analysis were employed to examine symptom changes over time and the interrelationships between symptoms. Results: Withdrawal scores significantly decreased over time, with the critical interval for symptom reduction identified between 8 and 32 h. Network analysis revealed visual disturbances as the most central symptom across both baseline and 40 h time points. Anxiety emerged as a crucial bridging symptom at 40 h, influencing the overall symptom profile. Conclusions: This study highlights the dynamic nature of alcohol withdrawal symptoms and identifies visual disturbances and anxiety as key symptoms relevant to nursing care. The findings emphasize the importance of close monitoring and targeted interventions during the acute withdrawal phase, particularly within the first 32 h. Psychiatric nurses should prioritize the assessment and management of visual disturbances and anxiety to optimize patient outcomes.