AUTHOR=Zhou Guo , Chen Jun , Zhang Hui 

TITLE=The influencing factors of postoperative sleep disorders in elderly patients undergoing hip replacement surgery under general anesthesia and the construction and validation of a nomogram prediction model

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1655523

DOI=10.3389/fneur.2025.1655523

ISSN=1664-2295

ABSTRACT=ObjectiveTo explore the influencing factors of postoperative sleep disorders in elderly patients undergoing hip replacement (HR) under general anesthesia, and to construct and validate a nomogram prediction model.MethodsA total of 329 patients who underwent general anesthesia HR surgery in our hospital from December 2021 to December 2023 were retrospectively gathered and grouped into a modeling group (230 cases) and a validation group (99 cases). The modeling group was separated into a sleep disorder group and a no sleep disorder group based on postoperative sleep disorders.ResultsOut of 230 patients, 69 experienced sleep disorders, with an incidence rate of 30.00%. Multivariate logistic regression found that age, anesthesia time, surgery time, intraoperative blood loss, postoperative hypoxemia, postoperative VAS score, and postoperative CRP level were risk factors for postoperative sleep disorders in elderly HR patients undergoing general anesthesia (p < 0.05). The AUC of the modeling group and validation group was 0.978 and 0.972, and the H-L test showed χ2 = 7.410 and 7.342, respectively, p = 0.762 and 0.752, indicating good consistency. DCA curve showed that when the high-risk threshold probability was between 0.08 and 0.88, the nomogram model had high clinical value.ConclusionAge, anesthesia time, surgery time, intraoperative blood loss, postoperative hypoxemia, postoperative VAS score, and postoperative CRP level are the influencing factors of postoperative sleep disorders in elderly HR patients undergoing general anesthesia. The nomogram model constructed based on this has good discrimination and consistency, and can predict the postoperative sleep disorders of patients.