Dynamic joint prediction model of severe radiation-induced oral mucositis among nasopharyngeal carcinoma: a prospective longitudinal study.

Abstract

Radiation-induced oral mucositis is one of the most common and debilitating side effects in nasopharyngeal carcinoma patients, which can cause decreased quality of life and treatment adherence. But the effective and tailored strategies for preventing radiation-induced oral mucositis are limited due to unclear risk factors and the lack of prediction models for identifying and stratifying high-risk patients. To develop a dynamic joint prediction model for severe radiation-induced oral mucositis among nasopharyngeal carcinoma patients. A prospective longitudinal study was conducted. Multidimensional variables were longitudinally evaluated till 3 months after the completion of radiotherapy. The outcome was the occurrence of severe radiation-induced oral mucositis during follow-up. Dynamic joint prediction model was based on the COX regression analysis and linear mixed effects model. Bootstrap resampling method was used to calculate the area under curve (AUC) and plot receiver operator characteristics (ROC) curve for model internal validation. All statistical analyses were performed in RStudio 4.3.1. A total of 294 participants were included. Risk factors include oral pH > 6.5 (HR = 0.61 [0.37-0.99]), sequential radiotherapy (HR = 1.80 [1.01-3.13]), dose-volume parameter D50 (HR = 1.04 [1.01-1.08]), increased white blood cell count (HR = 4.47 [1.53-14.60]), increased neutrophilic granulocyte percentage (HR = 1.52 [0.97-2.38]), log(anxiety) (HR = 612.78 [7.66-2.71*10^4]), log(depression) (HR = 0.01 [0.0001-0.20]), and log(nutritional status) (HR = 0.12 [0.01-0.82]). The ROC curves and AUC values revealed that the model has acceptable predictive performance. This study established a dynamic joint prediction model for severe radiation-induced oral mucositis based on multidisciplinary risk factors, which could provide guidance for developing targeted multidisciplinary interventions to improve severe radiation-induced oral mucositis.