Clinically Diagnose Asthma and Monitor Its Severity Using an Ultrasensitive Chemiresistive Nitric Oxide (NO) Gas Sensor via Exhaled Breath Analysis Assisted by Pattern Recognition

Fractional exhaled nitric oxide (FeNO) is widely recognized as a reliable biomarker for asthma. FeNO sensors can help diagnose asthma and monitor its severity. In this study, an ultrasensitive chemiresistive gas sensor, sensitive to the key breath biomarkers of asthma─nitric oxide (NO) and H ₂S─was fabricated using Ag-decorated ZnO. The sensor exhibits detection limits of 5 ppb for NO and 50 ppb for H ₂S, and it can discriminate 10 ppb NO and 60 ppb H ₂S from the exhaled breaths. Clinically, a total of 80 exhaled breath samples were collected and tested, including 40 from asthma patients (APs) and 40 from healthy control subjects (HCs). The AP group was effectively distinguished from the HC group using a pattern recognition algorithm (PCA), attributed to the sensor’s beneficial cross-sensitivity to asthma biomarkers. A diagnostic model distinguishing asthma from non-asthma was constructed using the support vector machine (SVM) algorithm, achieving an overall accuracy, sensitivity, and specificity of 0.81, 0.88, and 0.75, respectively. The area under the curve (AUC) value for all subjects in the receiver operating characteristic (ROC) curve was 0.92. The severity of asthma in three inpatients was monitored using the clinical evaluation method of diurnal peak expiratory flow (PEF) variation, alongside our sensor. The sensor’s response values exhibited a strong correlation (r = −0.74 (p < 0.05)) with the diurnal PEF variation values. To validate the sensor’s diagnostic capability, six breath samples from both HCs and APs were tested simultaneously using our sensor and a commercial electrochemical NO sensor utilized clinically. With r = −0.98 (p < 0.05) and R ² = 0.94, a strong linear relationship between two types of response values was observed, confirming the sensor’s accuracy and reliability in detecting NO concentrations in exhaled breath. Theoretical adsorption models of NO on the surface of the sensor were constructed using DFT calculations to elucidate the mechanisms driving the sensor’s ultrasensitivity. Overall, the sensor demonstrates a significant potential for use in clinical practice to diagnose asthma and monitor its severity.