• Wenting Luo, Xiangqing Hou, Yun Sun, Hong Zhang, Huali Ren, Xiangping Ma, Guoping Li, Yongmei Yu, Jianxin Sun, Huajie Wu, Jing Wu, Ruifen Zhong, Siqin Wang, Zhenan Li, Yan Zhao, Liting Wu, Xianhui Zheng, Miaoyuan Xu, Qingyuan Ye, Chuangli Hao, and Baoqing Sun.
• Department of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, China.
• Ann. Med. 2023 Jan 1; 55 (2): 22871882287188.

PurposeFew risk-forecasting models of allergic rhinitis (AR) exist that may aid AR pre-exposure prophylaxis (PrEP) in clinical practice. Therefore, this study aimed to develop and validate an effective clinical model for identifying candidates for AR PrEP using a routine medical questionnaire.MethodsThis study was conducted in 10 Chinese provinces with 13 medical centers (n = 877) between 2019 and 2021. Clinical characteristics and exposure history were collected via face-to-face interviews. Well-trained physicians diagnosed patients with AR based on skin prick test results and clinical performance. The least absolute shrinkage and selection operator model was used to identify potential risk factors for AR, and the logistic regression model was used to construct the risk-forecasting model. Predictive power and model reliability were assessed using area under the receiver operating characteristic curve and calibration curves, respectively.ResultsThis study diagnosed 625 patients with AR who had positive responses to at least one indoor or outdoor allergen and 460 to at least one outdoor pollen allergen. Two nomograms were established to identify two types of AR with various sensitization patterns. Both models had an area under curve of approximately 0.7 in the development and internal validation datasets. Additionally, our findings found good agreement for the calibration curves of both models.ConclusionEarly identification of candidates for AR PrEP using routine medical information may improve the deployment of limited resources and effective health management. Our models showed good performance in predicting AR; therefore, they can serve as potential automatic screening tools to identify AR PrEP candidates.

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