华西口腔医学杂志 ›› 2025, Vol. 43 ›› Issue (3): 395-405.doi: 10.7518/hxkq.2025.2024340

• 临床研究 • 上一篇    下一篇

口腔癌患者失志综合征风险预测模型构建及验证

毛莉艳1,2(), 杨茜茜3, 毕小琴2(), 刘敏4, 赵重阳5, 温作珍3   

  1. 1.四川大学华西护理学院,成都 610041
    2.口腔疾病防治全国重点实验室 国家口腔医学中心 国家口腔疾病临床医学研究中心 四川大学华西口腔医院正颌及关节外科,成都 610041
    3.中山大学孙逸仙纪念医院口腔科,广州 510120
    4.四川大学华西医院骨科,成都 610041
    5.四川大学华西医院中国循证医学中心,成都 610041
  • 收稿日期:2024-09-12 修回日期:2024-12-31 出版日期:2025-06-01 发布日期:2025-06-10
  • 通讯作者: 毕小琴 E-mail:mm940415mly@163.com;hxbxq@163.com
  • 作者简介:毛莉艳,主管护师,硕士,E-mail:mm940415mly@163.com
  • 基金资助:
    四川省科技计划项目(2022JDKP0007);成都市医学科研课题(2022015)

Risk prediction of demoralization syndrome in patients with oral cancer

Mao Liyan1,2(), Yang Xixi3, Bi Xiaoqin2(), Liu Min4, Zhao Chongyang5, Wen Zuozhen3   

  1. 1.West China School of Nursing, Sichuan University, Chengdu 610041, China
    2.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
    3.Dept. of Stomatology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, 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
  • Received:2024-09-12 Revised:2024-12-31 Online:2025-06-01 Published:2025-06-10
  • Contact: Bi Xiaoqin E-mail:mm940415mly@163.com;hxbxq@163.com
  • Supported by:
    The Sichuan Province Science and Technology Plan(2022JDKP0007);Medical Research Project of Chengdu(2022015)

摘要:

目的 构建口腔癌患者发生失志综合征的风险预测模型,为帮助口腔癌患者更好地应对失志综合征,并为其制定更加个性化的支持方案提供参考依据。 方法 选取2024年3月—7月在四川大学华西口腔医院及中山大学孙逸仙纪念医院共486例口腔癌住院患者作为研究对象。综合分析临床资料和既往研究证据,以确定影响口腔癌患者失志综合征的关键变量。将486例患者按照8∶2的比例分为训练集和验证集,将365例患者的个体数据纳入训练集,基于最小绝对收缩和选择算子(LASSO)回归构建口腔癌失志综合征中重度风险预测模型并构建列线图。采用Bootstrap重采样进行内部验证,通过121例验证组患者的独立数据进行外部验证。 结果 口腔癌患者失志综合征总发生率为83.3%(405例)。其中,轻度失志患者占比48.9%(198例),中度失志患者占比43.4%(176例),重度失志患者占比7.7%(31例)。核心模型包括患者文化水平、疾病了解程度和MDASI-HN评分。模型内部验证结果显示C统计量为0.783 6(95%CI为0.78~0.87),校准斜率为0.843 4,截距为-0.040 6。外部验证集的C统计量为0.80(95%CI为0.71~0.87),校准斜率为0.80,截距为-0.08。 结论 口腔癌患者失志综合征风险预测模型在不同护理环境的验证队列中表现稳健,模型校正良好,具有良好的区分度,可作为入院评估预测项目的参考。

关键词: 口腔癌, 失志综合征, 机器学习, 预测模型

Abstract:

Objective 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. Conclusion Our risk prediction model of the demoralization syndrome in patients with oral cancer performed robustly in validation cohorts of different nursing environments. The model has good correction and good discrimination and can be used as an evaluation and prediction item at admission.

Key words: oral cancer, demoralization syndrome, machine learning, prediction model

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