Abstract
BACKGROUND: Predictive models can identify patients at risk and thus enable personalized interventions. Despite the increasing number of prediction models used to predict the risk of dysphagia after radiotherapy in patients with head and neck cancer (HNC), there is still uncertainty about the effectiveness of these models in clinical practice and about the quality and applicability of future studies. The aim of this study was to systematically evaluate and analyze all predictive models used to predict dysphagia in patients with HNC after radiotherapy. METHODS: PubMed, Cochrane Library, EMbase and Web of Science databases were searched from database establishment to August 31, 2024. Data from selected studies were extracted using predefined tables and the quality of the predictive modelling studies was assessed using the PROBAST tool. Meta-analysis of the predictive performance of the model was performed using the "metafor" package in R software. RESULTS: Twenty-five models predicting the risk of dysphagia after radiotherapy in patients with HNC were included, covering a total of 8,024 patients. Common predictors include mean dose to pharyngeal constrictor muscles, treatment setting, and tumor site. Of these models, most were constructed based on logistic regression, while only two studies used machine learning methods. The area under the receiver operating characteristic curve (AUC) reported values for these models ranged from 0.57 to 0.909, with 13 studies having a combined AUC value of 0.78 (95% CI: 0.74-0.81). All studies showed a high risk of bias as assessed by the PROBAST tool. CONCLUSION: Most of the published prediction models in this study have good discrimination. However, all studies were considered to have a high risk of bias based on PROBAST assessments. Future studies should focus on large sample size and rigorously designed multicenter external validation to improve the reliability and clinical applicability of prediction models for dysphagia after radiotherapy for HNC. SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero, identifier CRD42024587252.