Development and validation of an oral frailty risk prediction model for lung cancer patients undergoing chemotherapy
10.3969/j.issn.1671-8283.2025.09.003
- VernacularTitle:肺癌化疗患者口腔衰弱预测模型的构建及验证
- Author:
Lijuan LIU
1
;
Jianqin LIN
;
Lei YE
;
Xiaohui JIANG
;
Haiyu LIU
;
Yanan HANG
;
Sijing PENG
;
Zijun DU
Author Information
1. 南京医科大学附属脑科医院呼吸内科,江苏 南京,210029
- Publication Type:Journal Article
- Keywords:
lung cancer;
chemotherapy;
oral frailty;
risk prediction model;
nursing strategies;
cross-sectional study
- From:
Modern Clinical Nursing
2025;24(9):17-26
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the status of oral frailty(OF)in patients who underwent chemotherapy for lung cancer,identify key factors influencing OF,and develop a risk prediction model.Methods Using convenience sampling,431 lung cancer inpatient were recruited from three Tier-IIIA hospitals in Jiangsu Province between September and November 2024 as the training cohort.The patients were divided into OF and non-OF groups.Relevant data were compared between the two groups.Multifactorial logistic regression analysis was performed to determine factors that associated with OF,and a risk prediction model was created accordingly.Receiver operating characteristic(ROC)curve analysis was used to predict model performance.In December 2024,additional 185 patients from one other Tier-IIIA hospitals were recruited to validate the developed model.Results The prevalence of OF among lung-cancer patients undergoing chemotherapy was 58.93%.Following listed items were identified as the risk factors of OF(all P<0.05):older in age(OR=3.420),poor education(OR=0.030),brain metastasis(OR=7.880),high nutritional risk screening 2002 score(OR=1.550),elevated C-reactive protein(OR=1.100),and elevated lactate dehydrogenase(OR=1.010).ROC area under the curve(AUC)of the model was 0.860(95%CI:0.830-0.900)in modelling cohort and 0.840(95%CI:0.780-0.900)in validation cohort.Hosmer-Lemeshow goodness-of-fit test yielded χ 2=4.870,P=0.770 for the training set and χ 2=2.770,P=0.950 for the validation set.Conclusion The risk prediction model for OF developed in this study demonstrates a good predictive performance and can facilitate early identification of high-risk patients,thereby providing a scientific basis for clinical interventions.
