[Lung sounds can be used as an indicator for assessing severity of chronic obstructive pulmonary disease at the initial diagnosis]

[Effects of type 2 inflammation on bronchodilator responsiveness of large and small airways in chronic obstructive pulmonary disease]

OBJECTIVE

To investigate the impact of type 2 inflammation markers blood eosinophils (EOS) and fractional exhaled nitric oxide (FeNO) on bronchodilator responsiveness (BDR) in patients with chronic obstructive pulmonary disease (COPD).

METHODS

This study was conducted among 389 patients with an established diagnosis of COPD in our hospital from October, 2019 to October, 2023, who all underwent bronchial dilation test (BDT) of the large and small airways. Based on smoking history, blood EOS, and FeNO, these patients were divided group A (blood EOS < 300/μL + FeNO < 35 ppb + smoking history < 20 pack-years), group B (blood EOS < 300/μL+FeNO < 35 ppb+smoking history ≥20 pack-years), group C (blood EOS ≥300/μL or FeNO≥35 ppb+smoking history ≥20 pack-years), and group D (blood EOS ≥300/μL or FeNO ≥35 ppb+smoking history < 20 pack-years) for analyzing the relationship between clinical indexes and BDR.

RESULTS

BDR evaluation based on forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and maximum mid-expiratory flow (MMEF) yielded consistent results, all showing a younger mean age, higher FeNO levels, and higher blood EOS counts and percentages in patients positive for BDT (P < 0.05). The improvement value and improvement rate of FEV1 were significantly lower in group A than in group D. The improvement value and improvement rate of FEV1 as well as the improvement rate of MMEF were significantly lower in group B than in group D. In the overall patients, age and FeNO were significantly correlated with the improvement value and improvement rate of FEV1 and the improvement rate of MMEF (P < 0.05).

CONCLUSION

Type 2 inflammation markers have different effects on BDR in the large and small airways of COPD patients, and their clinical significance needs further investigation.

Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously threatens people's health, with high morbidity and mortality worldwide. At present, the clinical diagnosis methods of COPD are time-consuming, invasive, and radioactive. Therefore, it is urgent to develop a non-invasive and rapid COPD severity diagnosis technique suitable for daily screening in clinical practice.

RESULTS

This study established an effective model for the preliminary diagnosis of COPD severity using lung sounds with few channels. Firstly, the time-frequency-energy features of 12 channels lung sounds were extracted by Hilbert-Huang transform. And then, channels and features were screened by the reliefF algorithm. Finally, the feature sets were input into a support vector machine to diagnose COPD severity, and the performance with Bayes, decision tree, and deep belief network was compared. Experimental results show that high classification performance using only 4-channel lung sounds of L1, L2, L3, and L4 channels can be achieved by the proposed model. The accuracy, sensitivity, and specificity of mild COPD and moderate + severe COPD were 89.13%, 87.72%, and 91.01%, respectively. The classification performance rates of moderate COPD and severe COPD were 94.26%, 97.32%, and 89.93% for accuracy, sensitivity, and specificity, respectively.

CONCLUSION

This model provides a standardized evaluation with high classification performance rates, which can assist doctors to complete the preliminary diagnosis of COPD severity immediately, and has important clinical significance.