Frontiers in oncology · 2026
Development and validation of a nomogram for prediction postoperative sleep disorders in patients with oral cancer: a prospective study.
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Qiu Ruyue, Zhou Yunyu, Wang Guangman, Yu Jingya, Li Yajun, Mao Liyan, Lubamba Grace Paka, Bi Xiaoqin
Аннотация
OBJECTIVES: This cross-sectional study aimed to develop and validate a predictive model to identify risk factors associated with postoperative sleep disturbances in patients with oral cancer.
METHODS: Data were collected from 385 patients with oral cancer who underwent surgery at the Department of Head and Neck Oncology of a tertiary hospital in Sichuan Province between July 2024 and December 2024. Participants were recruited through convenience sampling and allocated to a training group (n = 269) or a validation group (n = 116). The dataset encompassed a comprehensive range of demographic, clinical, and surgical variables, as well as psychometric assessments obtained from validated instruments, including the Pittsburgh Sleep Quality Index (PSQI), MD Anderson Symptom Inventory-Head and Neck Module (MDASI-H&N), Hospital Anxiety and Depression Scale (HADS), and Social Support Rating Scale (SSRS). Model performance was evaluated through reliability testing, intergroup comparisons, and multivariable logistic regression analyses to estimate odds ratios (ORs) and 95% confidence intervals (CIs). A nomogram was subsequently constructed and internally validated.
RESULTS: The incidence of postoperative sleep disturbances was 39.41% in the training set and 39.66% in the validation set. Independent predictors included history of alcohol consumption, longer surgical duration, higher MDASI-H&N and anxiety scores, and lower social support levels. Receiver operating characteristic (ROC) curve analysis demonstrated excellent discrimination, with areas under the curve (AUCs) of 0.902 for the training set and 0.967 for the validation set. Calibration curves showed close agreement between predicted and observed outcomes, with mean absolute errors (MAEs) of 0.0559 and 0.0942, respectively. Decision curve analysis (DCA) further confirmed strong clinical utility within a threshold probability range of 0.0-0.4.
CONCLUSION: A practical predictive model was developed to identify oral cancer patients at risk of postoperative sleep disturbances. The model achieved strong discrimination, accurate calibration, and notable clinical value.
METHODS: Data were collected from 385 patients with oral cancer who underwent surgery at the Department of Head and Neck Oncology of a tertiary hospital in Sichuan Province between July 2024 and December 2024. Participants were recruited through convenience sampling and allocated to a training group (n = 269) or a validation group (n = 116). The dataset encompassed a comprehensive range of demographic, clinical, and surgical variables, as well as psychometric assessments obtained from validated instruments, including the Pittsburgh Sleep Quality Index (PSQI), MD Anderson Symptom Inventory-Head and Neck Module (MDASI-H&N), Hospital Anxiety and Depression Scale (HADS), and Social Support Rating Scale (SSRS). Model performance was evaluated through reliability testing, intergroup comparisons, and multivariable logistic regression analyses to estimate odds ratios (ORs) and 95% confidence intervals (CIs). A nomogram was subsequently constructed and internally validated.
RESULTS: The incidence of postoperative sleep disturbances was 39.41% in the training set and 39.66% in the validation set. Independent predictors included history of alcohol consumption, longer surgical duration, higher MDASI-H&N and anxiety scores, and lower social support levels. Receiver operating characteristic (ROC) curve analysis demonstrated excellent discrimination, with areas under the curve (AUCs) of 0.902 for the training set and 0.967 for the validation set. Calibration curves showed close agreement between predicted and observed outcomes, with mean absolute errors (MAEs) of 0.0559 and 0.0942, respectively. Decision curve analysis (DCA) further confirmed strong clinical utility within a threshold probability range of 0.0-0.4.
CONCLUSION: A practical predictive model was developed to identify oral cancer patients at risk of postoperative sleep disturbances. The model achieved strong discrimination, accurate calibration, and notable clinical value.