ObjectiveTo explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). MethodsA systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and China Biomedical Literature Database up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. ResultsA total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of specific exhaled VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than exhaled VOCs, including forced expiratory flow at 75% lung capacity (FEF75) and modified Medical Research Council dyspnea scale score (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve (AUC) ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high bias risk, and the overall applicability was unclear. ConclusionThe exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.