Diagnostic value of exhaled volatile organic compounds in pulmonary cystic fibrosis: A systematic review
- VernacularTitle:呼出气体挥发性有机化合物对肺囊性纤维化诊断价值的系统评价
- Author:
Xiaoping YU
1
;
Zhixia SU
2
;
Kai YAN
2
;
Taining SHA
2
;
Yuhang HE
3
;
Yanyan ZHANG
4
;
Yujian TAO
5
;
Hong GUO
6
;
Guangyu LU
2
;
Weijuan GONG
7
Author Information
1. Department of Health Management Center, Affiliated Hospital of Yangzhou University, Yangzhou, 225012, Jiangsu, P. R. China
2. School of Public Health, Yangzhou University, Yangzhou, 225009, Jiangsu, P. R. China
3. School of Nursing, Yangzhou University, Yangzhou, 225009, Jiangsu, P. R. China
4. Testing Center of Yangzhou University, Yangzhou University, Yangzhou, 225009, Jiangsu, P. R. China
5. Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Yangzhou University, Yangzhou, 225012, Jiangsu, P. R. China
6. Department of Thoracic Surgery, Affiliated Hospital of Yangzhou University, Yangzhou, 225012, Jiangsu, P. R. China
7. Department of Gastroenterology, Affiliated Hospital of Yangzhou University, Yangzhou, 225012, Jiangsu, P. R. China
- Publication Type:Journal Article
- Keywords:
Cystic fibrosis;
volatile organic compounds;
exhaled breath;
biomarker;
systematic review
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(02):223-229
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). Methods A systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed databases up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS), and the risk of bias and applicability of included prediction model studies were assessed by the prediction model risk of bias assessment tool (PROBAST). Results A 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 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 VOCs, including forced expiratory flow at 75% of forced vital capacity (FEF75) and modified Medical Research Council scale for the assessment of dyspnea (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve 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, and the overall applicability was unclear. Conclusion The 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.
