AUTHOR=Zhu Yabin , Xiang Dong , Xing Hailin , Li Yunxiang , Xie Hong , Jiang Lin 

TITLE=Nutritional and inflammatory markers for predicting delirium after radical hysterectomy for cervical cancer: development of a nomogram

JOURNAL=Frontiers in Nutrition

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1644157

DOI=10.3389/fnut.2025.1644157

ISSN=2296-861X

ABSTRACT=BackgroundThis investigation aimed to establish pivotal determinants of postoperative delirium (POD) following radical hysterectomy for cervical carcinoma (CC) and formulate an individualized risk stratification tool.MethodsWe conducted a retrospective cohort study encompassing 253 geriatric patients undergoing radical hysterectomy for CC between 2021 and 2025. We systematically evaluated potential predictors using a two-phase regression model: first through univariate analysis (P < 0.1), followed by multivariate logistic regression (P < 0.05) to identify independent predictors of POD. Key clinical, demographic, and laboratory variables were included in the analysis. The incidence of POD was assessed using the Confusion Assessment Method (CAM) during the 7-day perioperative period. The predictive nomogram was developed using R and was rigorously validated through both internal cohort validation and external validation. ROC, calibration, and decision curve analyses were used to assess the nomogram's predictive performance.ResultsThe POD incidence reached 16.2% (n = 41) during the 7-day postoperative surveillance. Multivariable analysis delineated five independent predictors: advanced age (OR = 1.12, P = 0.031), depressed albumin-fibrinogen ratio (AFR; OR = 0.69, P = 0.029), elevated neutrophil-lymphocyte ratio (NLR; OR = 3.51, P = 0.001), Controlling Nutritional Status (CONUT) score (OR = 1.81, P = 0.003), and Geriatric Nutritional Risk Index (GNRI; OR = 0.94, P = 0.001). The constructed nomogram exhibited robust discriminative capacity, achieving area under curve (AUC) values of 0.821 and 0.966 in internal and external validations, respectively.ConclusionsThis research introduced an effective nomogram prediction model for predicting POD after radical hysterectomy for CC, providing a straightforward and visual method for individualized risk assessment.