Risk prediction of demoralization syndrome in patients with oral cancer.
10.7518/hxkq.2025.2024340
- Author:
Liyan MAO
1
;
Xixi YANG
2
;
Xiaoqin BI
3
;
Min LIU
4
;
Chongyang ZHAO
5
;
Zuozhen WEN
2
Author Information
1. West China School of Nursing, Sichuan University, Chengdu 610041, China.
2. Dept. of Stomatology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.
3. State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Dept. of Orthognathic and Temporomandibular Joint Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China.
4. Dept. of Orthopedic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China.
5. Dept. of Evidence-based Medicine and Clinical Epidemiology, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Publication Type:Journal Article
- Keywords:
demoralization syndrome;
machine learning;
oral cancer;
prediction model
- MeSH:
Humans;
Mouth Neoplasms/complications*;
Male;
Female;
Nomograms;
Middle Aged;
Syndrome;
Aged;
Adult;
Risk Factors;
Risk Assessment;
Machine Learning
- From:
West China Journal of Stomatology
2025;43(3):395-405
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:This study aimed to construct a risk prediction model for the occurrence of the demora-lization syndrome in patients with oral cancer and provide a scientific basis for the prevention of this syndrome in patients with oral cancer and the development of personalized care programs.
METHODS:A total of 486 patients with oral cancer in West China Hospital of Stomatology of Sichuan University and Sun Yat-sen Memorial Hospital of Sun Yat-sen University from 2024 March to July were selected by convenience sampling. We integrated clinical data and evidence from previous studies to identify the key variables affecting the demoralization syndrome in patients with oral cancer. The 486 patients were divided into a training set and a validation set in an 8∶2 ratio. A clinical risk prediction model was established based on the individual data of 365 patients in the development cohort. Through least absolute shrinkage and selection operator (LASSO) regression, a moderate to severe risk prediction model of demoralization syndrome in oral cancer was constructed, and a clinical machine-learning nomogram was constructed. Bootstrap resampling was used for internal validation. The data of 121 patients in the validation cohort were externally validated.
RESULTS:The incidence of the demoralization syndrome in patients with oral cancer was 405 cases (83.3%), of which 279 cases (57.4%) were mild, 176 cases (36.2%) were moderate, and 31 cases (6.4%) were severe. The core model, including patient education level, disease understanding, and MDASI-HN score, was used to predict the risk of outcome. Internal validation of the model yielded C statistic of 0.783 6 (95% CI: 0.78-0.87), beta of 0.843 4, and calibration intercept of -0.040 6. Through external validation, the validation set C statistic was 0.80 (95%CI: 0.71-0.87), beta was 0.80, and calibration intercept was -0.08.
CONCLUSIONS:Our risk prediction mo-del of the demoralization syndrome in patients with oral cancer performed robustly in validation cohorts of different nur-sing environments. The model has good correction and good discrimination and can be used as an evaluation and prediction item at admission.
