Effects of Noise Reduction Combined with AI-Based Nursing System on Postoperative Sleep Quality and Delirium Incidence in Older Patients with Severe Glioma

Objective: To explore the effect of noise reduction combined with the artificial intelligence (AI)-based nursing system on postoperative care in older patients with severe glioma. Methods: This retrospective analysis included 100 older patients with severe glioma admitted to Zhoukou Orthopedic Hospital between October 2020 and December 2025. Patients were assigned to the combined nursing group and the control group according to different nursing methods. Sleep quality, the incidence and duration of delirium, the incidence of postoperative complications, and nursing satisfaction were compared between the two groups at 7 and 14 days after surgery. Results: At 7 and 14 days after surgery, the total and each dimension score of the Richards–Campbell Sleep Questionnaire in the combined nursing group were significantly higher than those in the control group ( P < 0.05). The combined nursing group had a lower incidence of delirium (7.27% versus 26.67%) and a shorter duration of delirium than the control group (both P < 0.05). The combined nursing group also had a lower incidence of postoperative complications (5.45% versus 22.22%) and a higher nursing satisfaction rate (96.36% versus 77.78%) than the control group (both P < 0.05). After adjustment for potential confounders, noise reduction combined with the AI-based nursing system was associated with significantly lower odds of postoperative delirium compared with routine care alone (OR = 0.21, 95% CI 0.06–0.78, P  = 0.019). Conclusion: The combined intervention of noise reduction and AI-based nursing system was associated with a more favorable postoperative prognosis among older patients with severe glioma. It may help optimize postoperative nursing strategies for this patient population, although further verification is warranted.