Pediatric Section, Azienda Ospedaliera Universitaria Integrata Verona, 37126 Verona, Italy.
Department of Surgery, Dentistry, Paediatrics and Gynaecology, University of Verona, 37129 Verona, Italy.
Sensors (Basel). 2024 Sep 28;24(19):6282. doi: 10.3390/s24196282.
Bronchopulmonary dysplasia (BPD) is the most common respiratory disease in preterm and is still associated with increased mortality and morbidity. The great interest lies in identifying early biomarkers that can predict the development of BPD. This pilot study explores the potential of e-nose for the early identification of BPD risk in premature infants by analyzing volatile organic compounds (VOCs) in the exhaled breath condensate (EBC). Fourteen mechanically ventilated very preterm infants were included in this study. The clinical parameters and EBC were collected within the first 24 h of life. The discriminative ability of breath prints between preterms who did and did not develop BPD was investigated using pattern recognition, a machine learning algorithm, and standard statistical methods. We found that e-nose probes can significantly predict the outcome of "no-BPD" vs. "BPD". Specifically, a subset of probes (S18, S24, S14, and S6) were found to be significantly predictive, with an AUC of 0.87, 0.89, 0.82, 0.8, and = 0.019, 0.009, 0.043, 0.047, respectively. The e-nose is an easy-to-use, handheld, non-invasive electronic device that quickly samples breath. Our preliminary study has shown that it has the potential for early prediction of BPD in preterms.
支气管肺发育不良（BPD）是早产儿最常见的呼吸系统疾病，仍与死亡率和发病率增加有关。人们极大的兴趣在于确定可以预测 BPD 发展的早期生物标志物。这项初步研究通过分析呼出气冷凝物（EBC）中的挥发性有机化合物（VOC），探讨了电子鼻在早期识别早产儿 BPD 风险中的潜力。本研究纳入了 14 名机械通气的极早产儿。在生命的头 24 小时内收集临床参数和 EBC。使用模式识别、机器学习算法和标准统计方法研究了呼吸印迹在未发生 BPD 和发生 BPD 的早产儿之间的区分能力。我们发现电子鼻探针可以显著预测“无 BPD”与“BPD”的结果。具体来说，发现一组探针（S18、S24、S14 和 S6）具有显著的预测能力，AUC 分别为 0.87、0.89、0.82、0.8 和 = 0.019、0.009、0.043、0.047。电子鼻是一种易于使用、手持式、非侵入性的电子设备，可以快速采样呼吸。我们的初步研究表明，它有可能早期预测早产儿的 BPD。
