Development and Internal Validation of Risk Assessment Models for Chronic Obstructive Pulmonary Disease in Coal Workers

Coal workers are more likely to develop chronic obstructive pulmonary disease due to exposure to occupational hazards such as dust. In this study, a risk scoring system is constructed according to the optimal model to provide feasible suggestions for the prevention of chronic obstructive pulmonary disease in coal workers. Using 3955 coal workers who participated in occupational health check-ups at Gequan mine and Dongpang mine of Hebei Jizhong Energy from July 2018 to August 2018 as the study subjects, random forest, logistic regression, and convolutional neural network models are established, and model performance is evaluated to select the optimal model, and finally a risk scoring system is constructed according to the optimal model to achieve model visualization. The training set results show that the logistic, random forest, and CNN models have sensitivities of 78.55%, 86.89%, and 77.18%; specificities of 85.23%, 92.32%, and 87.61%; accuracies of 81.21%, 85.40%, and 83.02%; Brier scores of 0.14, 0.10, and 0.14; and AUCs of 0.76, 0.88, and 0.78, respectively, and similar results are obtained for the test set and validation set, with the random forest model outperforming the other two models. The risk scoring system constructed according to the importance ranking of random forest predictor variables has an AUC of 0.842; the evaluation results of the risk scoring system shows that its accuracy rate is 83.7% and the AUC is 0.827, and the established risk scoring system has good discriminatory ability. The random forest model outperforms the CNN and logistic regression models. The chronic obstructive pulmonary disease risk scoring system constructed based on the random forest model has good discriminatory power.

The Prevalence of Symptomatic Dry Eye Disease Among Coal Workers in Huainan Region of China

Purpose

To investigate the prevalence and influencing factors of symptomatic dry eye disease (DED) in Chinese coal workers.

Methods

The prevalence of symptomatic DED in coal workers was investigated by using the questionnaire of Ocular Surface Disease Index (OSDI) and the influencing factors were explored.

Results

The prevalence of symptomatic DED was 50.7% in coal workers. Of the influencing factors of symptomatic DED, the level of dust exposure had an odds ratio (OR) of 1.26, the time of dust exposure had an OR of 1.02, and the age had an OR of 1.03.

Conclusion

There was a high morbidity of symptomatic DED among coal workers and the level and the time of dust exposure and the age of coal workers had important effects.

Convolutional Neural Network-Based ECG-Assisted Diagnosis for Coal Workers

OBJECTIVE

To process and extract electrocardiogram (ECG, ECG, or EKG) features using a convolutional neural network (CNN) to establish an ECG-assisted diagnosis model.

METHODS

Coal workers who underwent physical examinations at Gequan Mine Hospital and Dongpang Mine Hospital of Hebei Jizhong Energy from July 2020 to September 2020 were selected as the study subjects. The ECG images were preprocessed. We use Python software and convolutional neural network to establish ECG images recognition and classification model.We usecalibration curve, calibration-in-the-large, Brier score, specificity, sensitivity, F1 score, Kappa value, accuracy, and area under the curve (AUC) of ROC to evaluate the performance of the model.

RESULTS

The number of abnormal ECG results was 849, and the rate of abnormal results was 25.02%. The test set accuracies of the sinus bradycardia model, nonspecific intraventricular conduction delay model, myocardial ischemia model, and sinus tachycardia model were 97.66%, 96.49%, 93.62%, and 93.02%, respectively; sensitivities were 96.63%, 96.30%, 96.88% and 95.24%, respectively; specificities were 98.78%, 96.67%, 86.67%, and 90.90%, respectively; Brier scores were 0.03, 0.07, 0.09, and 0.11, respectively; Calibration-in-the-large values were 0.026, 0.110, 0.041, and 0.098, respectively.

CONCLUSIONS

The convolutional neural network model can accurately identify the main ECG abnormality types of coal workers. Additionally, the main ECG abnormalities in these coal company workers were sinus bradycardia, non-specific intraventricular conduction delay, myocardial ischemia, and sinus tachycardia.

Characterizing patterns of small pneumoconiotic opacities on chest radiographs of New Mexico coal miners

Small pneumoconiotic opacities in coal miners are usually described as rounded, regular, and upper zone predominant. We aim to characterize chest radiographic patterns in New Mexico coal miners in comparison with other miners. Of the 330 chest radiographs reviewed, small pneumoconiotic opacities in New Mexico miners were almost always irregularly shaped, and lower lung zone predominant, consistent with diffuse dust-related pulmonary fibrosis. There was no significant difference in patterns of opacities between miners with exposure to coal mine dust exclusively, mixed coal and noncoal mine dust, and no coal dust. Our findings indicate that New Mexico coal miners demonstrate a different pattern of small pneumoconiotic opacities than the classic nodular pneumoconiosis described in the literature, predominantly from Appalachian miners. This may indicate differences in racial/ethnic characteristics or in the silica/silicate content of dust between the Appalachian and Mountain West regions.

COPD and levels of Hsp70 (HSPA1A) and Hsp27 (HSPB1) in plasma and lymphocytes among coal workers: a case-control study

This case-control study aimed to investigate whether the levels of Hsp70 (HSPA1A) and Hsp27 (HSPB1) in plasma and lymphocytes were associated with the risk of chronic obstructive pulmonary disease (COPD) among coal workers. A total of 76 COPD cases and 48 age-matched healthy controls from a group of coal workers were included. The case group consisted of 35 COPD patients whose condition was complicated with coal workers' pneumoconiosis (CWP) and 41 COPD patients without CWP. Heat shock proteins (Hsps) in plasma and lymphocytes were detected by ELISA and flow cytometry, respectively. Multiple logistic regression models were applied to estimate the association between Hsp levels and COPD risk. Our results showed that plasma Hsp70 and lymphocyte Hsp27 levels were significantly higher and plasma Hsp27 levels were significantly lower in COPD cases than in controls (p < 0.01). No significant differences in lymphocyte Hsp70 levels were found between COPD cases and the matched subjects. Higher plasma Hsp70 levels (odds ratio (OR) = 13.8, 95 % confidence interval (CI) = 5.7-33.5) and lower plasma Hsp27 levels (OR = 4.6, 95 % CI = 2.0-10.5) were significantly associated with an increased risk of COPD after adjusting for confounders. Higher lymphocyte Hsp27 levels were only associated with an increased risk of COPD with CWP (OR = 6.6, 95 % CI = 2.0-22.1) but not with an increased risk of COPD without CWP (OR = 3.0, 95 % CI = 0.9-8.9). Additionally, there were strong joint effects of different Hsps on COPD risk. These results showed that higher levels of plasma Hsp70 and lower levels of plasma Hsp27 might be associated with an increased risk of COPD among coal workers. They may have the potential to serve as monitoring markers for COPD in coal workers.