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.
Humans ; Mouth Neoplasms/complications* ; Male ; Female ; Nomograms ; Middle Aged ; Syndrome ; Aged ; Adult ; Risk Factors ; Risk Assessment ; Machine Learning

Objective: To evaluate the feasibility and outcomes of the reconstruction of large anterior palatal fistulae by anteriorly based dorsal tongue flaps to provide a rational reference of anteriorly based dorsal tongue flaps for clinicians. Methods : Five patients with anterior hernia had a defect range of 1.0 cm × 1.0 cm to 1.5 cm × 2.0 cm, and the anterior tongue was 1.3 cm × 3.5 cm to 2.0 cm × 3.5 cm. The defects were all repaired with anteriorly based dorsal tongue flaps. The clinical efficacy was evaluated after operation, including whether the mucosal flap was infected, whether there was any shedding before the pedicle, and whether there was any perforation after operation. Thereafter, patients who were satisfied with their chewing, swallowing, speech function and appearance were followed up Results: All patients underwent successful reconstruction of palatal defects by anteriorly based tongue flaps, and no case of spontaneous detachment of the tongue flap occurred. Patients with palatal fistulae were followed up for 16-28 months, and no recurrence was encountered. The operation had no effect on the speech, agitation and swallowing function of the tongue, and patients were satisfied with the appearance. Conclusion The dorsal lingual mucosal flap pedicled with the anterior tongue is a safe and reliable method for repairing large anterior palatal fistula.