Journal of thoracic disease最新文献

Background: The pandemic coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) influenced millions of lives, not only healthy subjects but also patients with various comorbidities. Of those, interstitial lung disease (ILD) is known to be specifically vulnerable to SARS-CoV-2, sometimes leading to a lethal outcome. Sarcoidosis, one of the ILDs, has been suggested to be more susceptible to COVID-19, but the results of previous studies are in disagreement.

Methods: We performed a meta-analysis, attempting to explain the correlation between COVID-19 and sarcoidosis. Electronic databases were searched and selected for analysis under pre-established criteria. Risk ratio (RR) for incidence and odds ratio (OR) for severity, i.e., intensive care unit (ICU) admission and death, were calculated with (95%) confidence interval (CI) using R Statistical Software (version 4.3.1).

Results: The RR of COVID-19 incidence on sarcoidosis patients was 5.86 (95% CI: 8.02-11.91). Admission of sarcoidosis patients to the ICU who were infected with SARS-CoV-2 was 2.48 (95% CI: 2.04-3.01). Death of sarcoidosis by COVID-19 was also significantly higher compared with healthy controls (OR =1.95, 95% CI: 1.58-2.41). Both morbidity and mortality due to COVID-19 was significantly higher in the sarcoidosis patients.

Conclusions: Sarcoidosis patients are undeniably prone to SARS-CoV-2 infection, with increased severity, morbidity and greater mortality of COVID-19. Vaccination against SARS-CoV-2 for sarcoidosis therefore is beneficial, and may be a compulsory measure. Further studies regarding other factors are needed for a better understanding of the correlation between sarcoidosis and COVID-19.

Background: There are few papers on three-dimensional (3D) images of the lungs using non-enhanced computed tomography (CT). This study aimed to investigate the accuracy of 3D images of the lungs using non-enhanced CT.

Methods: The study included 10 consecutive cases for each lung lobe, totalling 50 cases between March and December 2022. The patients had both non-enhanced and contrast-enhanced CT taken within 2 months before surgery. A 3D image analysis system (SYNAPSE VINCENT) was used to obtain 3D images of the pulmonary artery (PA), pulmonary vein (PV), and bronchus (Br). The system automatically generated 3D images based on both non-enhanced and contrast-enhanced CTs, which were then compared with each other and also with actual surgical findings.

Results: The coincidence rate of PA, PV, and Br between 3D images based on non-enhanced CT and enhanced CT was 70% for the right lung and 65% for the left lung. The coincidence rate of PA, PV, and Br between 3D images based on non-enhanced CT and actual surgical findings was 100% for the right middle, right lower, and left lower lobes, but 50% for the right upper lobe and 60% for the left upper lobe.

Conclusions: The 3D images of the lungs based on non-enhanced CT showed that the right middle lobe and both lower lobes were correctly depicted. The right and left upper lobes were poorly visualized using non-enhanced CT, while the right upper lobe was poorly visualized using contrast-enhanced CT.

Background: Club cell secretory protein 16 (CC16) has protective roles in airway diseases, including anti-inflammatory, immunomodulatory, and antioxidant functions. This study investigates CC16's potential to repair lung injury from particulate matter 2.5 (PM_2.5) exposure in asthmatic mice.

Methods: In an ovalbumin (OVA)-induced asthma model, 6-week-old male C57BL/6J mice were exposed to PM_2.5 for 24 hours and then treated with CC16. We conducted arterial blood gas analysis, lung function tests, histopathology, and immunohistochemical (IHC) staining. The BEAS-2B cell line was exposed to PM_2.5 for 24 hours and then treated with CC16. Tissues were analyzed by hematoxylin and eosin (HE) staining, electron, and IHC microscopy. The expression of indicators related to inflammation and pyroptosis was also detected and explored by performing RNA sequencing (RNA-seq).

Results: Upon OVA-sensitized asthmatic mice's exposure to PM_2.5, CC16 therapy reversed lung tissue pathology, corrected acidosis in respiratory gases, and normalized airway constriction. Decreased CC16 also bolstered cellular growth, inhibited PM_2.5-mediated pyroptosis, and downregulated the expression of inflammatory cytokines and pyroptosis markers at both protein and RNA levels. Transcriptome profiling showed that CC16 modulated the expression of genes linked to inflammatory adhesion, suppressing them, and upregulated those related to proliferation, particularly E-twenty-six-1 (ETS1).

Conclusions: CC16 efficiently remedies airway epithelial cells (AECs) harm caused by PM_2.5 in asthmatic mice, fostering cellular multiplication and suppressing pyroptosis and inflammation. Our findings imply CC16's potential as a promising therapeutic option for addressing future health threats stemming from PM_2.5 exposure.

Background: Thymoma and thymic carcinoma are the most common tumors of the anterior mediastinum. However, there are little research on applying machine learning (ML) approaches to the prognostic prediction of thymoma and thymic carcinoma. The study aims to develop predictive models utilizing ML techniques to accurately forecast the 5-year survival of patients with thymoma and thymic carcinoma.

Methods: Patients with malignant thymic neoplasms were identified in the Surveillance, Epidemiology, and End Results (SEER) 17 database, and their demographic and clinicopathological characteristics were collected. ML classifiers, including elastic net regularized logistic regression, random forest (RF), non-linear support vector machine (SVM), extreme gradient boosting (XGBoost) machine, and categorical boosting (CatBoost) were trained. The hyper-parameter of the algorithms was optimized by a grid search with five repeats of 10-fold cross-validation. Ensemble models were built based on the three algorithms with the highest area under the receiver operator characteristic (ROC) curve (AUC) in the validation set. The best model among the single models and ensemble model was selected as the final model. Calibration curve and decision curve were adopted to evaluate the calibration performance and clinical utility. For comparison, we constructed a baseline model consisting of age and Masaoka stages using logistic regression.

Results: After data cleaning, 1,363 patients and 841 patients were included in the overall survival (OS) dataset and disease-specific survival (DSS) dataset, respectively. CatBoost [AUC: 0.755; 95% confidence interval (CI): 0.698-0.811] had the best performance in the OS prediction for the original dataset. The ensemble model achieved the highest prognostic efficiency for the original dataset, with an AUC of 0.833 (95% CI: 0.765-0.901). Calibration showed favorable goodness of fit and was further verified with the Hosmer-Lemeshow test (CatBoost: χ²=12.63, P=0.13; ensemble model: χ²=7.61, P=0.47). The decision curve showed that the final model provided a high net benefit. The model could significantly distinguish the prognosis of patients (all P values <0.001). Finally, World Health Organization (WHO) histological classification, Masaoka stage, and age were the variables that significantly contributed to the models' prediction of OS and DSS.

Conclusions: We trained ML-based predictive models that could accurately predict the 5-year OS and DSS of patients with thymoma and thymic carcinoma.

Background: Currently, mitral valve replacement is the primary method of reintervention for patients with rheumatic heart disease (RHD) after percutaneous mitral balloon commissurotomy (PMBC). This study aims to investigate the feasibility of mitral valve repair in such patients and report its therapeutic outcomes compared to mitral valve replacement.

Methods: Data from patients with previous PMBC who underwent mitral valve surgery as disease progression between January 2011 and August 2023 were retrospectively analyzed. The patients were divided into two groups: the repair group and the replacement group. The stabilized inverse probability of treatment weighting (SIPTW) method was used to balance baseline characteristic differences between the two groups. Clinical outcomes investigated included freedom from mitral valve reoperation and overall survival. Survival curves were generated with the Kaplan-Meier (K-M) method, and differences between groups were compared using the Log-rank test.

Results: A total of 210 patients were included in this study (32 in the repair group and 178 in the replacement group), with an average age of 56.6±10.0 years. After SIPTW, the baseline characteristics between the two groups were comparable. The median follow-up time for the entire cohort was 75.6 months. K-M analysis, adjusted for SIPTW, showed no significant differences between the two groups in terms of freedom from mitral valve reoperation and overall survival (Log-rank P=0.07 and 0.36, respectively).

Conclusions: According to our results, even if patients with RHD have previously undergone PMBC, there is still a possibility of mitral valve repair in suitable patients, with good clinical outcomes.

Background: Blunt chest injuries are the predominant cause of rib fractures, which frequently result in pulmonary complications. We hypothesized that different trauma mechanisms and fracture patterns are associated with pulmonary complications in elderly patients (≥65 years) with isolated rib fractures.

Methods: We retrospectively reviewed the data of patients with rib fractures recorded in the electronic medical record system of a trauma center from 2015 to 2019. The correlation between age, comorbidities, trauma mechanisms, fracture patterns, and pulmonary complications was examined.

Results: A total of 168 elderly patients with isolated rib fractures after trauma were included in the study. Except for one patient who died, all other patients recovered and were discharged. Univariate analysis identified age, trauma mechanisms, and fracture patterns as potential risk factors for pulmonary complications. Multivariate logistic regression analysis found that falls [odds ratio (OR) 5.051, 95% confidence interval (CI): 1.380-18.485; P=0.01], rib fracture displacement (OR 4.924, 95% CI: 1.826-13.275; P=0.002), and multiple rib fractures (≥2 ribs) (OR 2.984, 95% CI: 1.182-7.531; P=0.02) were the independent risk factors for pulmonary complications. Subgroup analysis showed that hemothorax occurred in 47 (85.5%, P=0.001) patients with falls, 69 (89.6%, P<0.001) patients with displaced rib fractures, and 99 (74.4%, P<0.001) patients with multiple rib fractures.

Conclusions: The traumatic mechanisms and fracture patterns appear to be related to pulmonary complications, with patients experiencing falls, displaced rib fractures, and multiple rib fractures being more likely to develop these complications.

Background: The standard treatment for pulmonary neuroendocrine neoplasms (pNENs) is surgery in the early stage and is generally determined according to the histologic type and stage. Radiotherapy (RT) is a treatment option for locally advanced or unresectable lung cancers. The aim of this study was to determine the prognostic value of RT in patients with pNENs using data from the Surveillance, Epidemiology, and End Results database.

Methods: We used propensity score matching analysis to balance differences in variables between the RT and no-RT groups. Univariate and multivariate Cox proportional hazards regression analyses were used to evaluate the factors related to overall survival and cancer-specific survival (CSS). A novel nomogram was constructed, and the results were evaluated using the concordance index.

Results: A total of 7,470 cases were identified between 2000 and 2019, among whom 1,429 were placed in the RT group and propensity-score matched with those in the no-RT group at a 1:1 ratio. Age, sex, marital status, disease extension, surgery, and RT were identified as independent prognostic factors of outcome. There was no significant difference in overall or CSS between RT and no-RT patients in the surgery group (P=0.22 and P=0.72, respectively). However, RT was associated with survival benefit in the no-surgery group. According to the concordance index, the nomogram could accurately predict the prognosis of patients with pNENs.

Conclusions: Our findings indicate that RT may provide a survival benefit for patients with pNENs, particularly for those who did not undergo surgery. The nomogram produced in this study may be a used to predict the prognosis of this patient group.

[This corrects the article DOI: 10.21037/jtd-24-8.].

Background: Accurate prediction is essential for the effective management of spontaneous pneumothorax (SP). To improve prediction, this study primarily focuses on using simple chest X-rays to predict ipsilateral recurrence and contralateral occurrence of SP.

Methods: All consecutive subjects diagnosed with SP from July 2017 to June 2023 were retrospectively reviewed. Ipsilateral recurrence and contralateral occurrence of SP within two years of completing treatment were analyzed. Using simple chest X-rays and clinical parameters such as age, sex, smoking, chronic obstructive pulmonary disease (COPD) and surgery, machine learning algorithms were applied to predict SP development. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to highlight the X-ray regions associated with SP development.

Results: The study included 1,086 cases of SP, with 546 right-side and 540 left-side developments. Surgeries were performed in 243 right and 204 left cases. Ipsilateral recurrence occurred in 93 cases total, while contralateral occurrence occurred in 60 right and 34 left cases. For predicting ipsilateral recurrence in the young group, gradient boosting (GB) [area under curve (AUC) of 0.686, accuracy of 0.769, F1 score of 0.733, precision of 0.706, and recall of 0.769] for the right side and logistic regression (AUC of 0.628, accuracy of 0.781, F1 score of 0.753, precision of 0.737, and recall of 0.781) for the left side were the top-performing models. In the older group, K-nearest neighbors (KNN) (AUC of 0.615, accuracy of 0.801, F1 score of 0.760, precision of 0.735, and recall of 0.801) for the right side and logistic regression (AUC of 0.623, accuracy of 0.824, F1 score of 0.804, precision of 0.794, and recall of 0.824) for the left side were the best models. For predicting contralateral occurrence in the young group, random forest (RF) (AUC of 0.597, accuracy of 0.774, F1 score of 0.741, precision of 0.709, and recall of 0.774) for the right side and KNN (AUC of 0.650, accuracy of 0.893, F1 score of 0.849, precision of 0.809, and recall of 0.893) for the left side were the most effective models. In the older group, logistic regression (AUC of 0.630, accuracy of 0.935, F1 score of 0.914, precision of 0.894, and recall of 0.935) for the right side and neural network (NN) (AUC of 0.765, accuracy of 0.961, F1 score of 0.948, precision of 0.936, and recall of 0.961) for the left side were the top performers. Grad-CAM analysis revealed that apical lung portions were strongly associated with both ipsilateral recurrence and contralateral occurrence of SP.

Conclusions: The results of this study suggest that machine learning algorithms using simple X-rays and basic clinical data can predict SP development with fair performance. The apical regions of the lung were strongly associated with SP development, consistent with clinical knowledge.