Computed tomography-guided transthoracic needle biopsy (CT-TNB) is widely used in the diagnosis of solitary pulmonary nodule (SPN). However, CT-TNB-induced pneumothorax occurs frequently. This study aimed to establish a predictive model for pneumothorax following CT-TNB for SPN. The prediction model was developed in a cohort that consisted of 311 patients with SPN who underwent CT-TNB. An independent external validation cohort contained 227 consecutive patients. The least absolute shrinkage and selection operator (Lasso) regression analysis was used for data dimension reduction and predictors selection. Multivariable logistic regression was used to develop the predictive model, which was presented with a nomogram. Area under the curve (AUC) was used to determine the discrimination of the proposed model. The calibration was used to test the goodness-of-fit of the model, and decision curve analysis (DCA) was used for evaluating its clinical usefulness. Five variables (age, diagnosis of nodule, puncture times, puncture distance, and puncture position) were filtered by Lasso regression. AUC of the predictive model and the validation were 0.801 (95% CI, 0.738-0.865) and 0.738 (95% CI, 0.656-0.820), respectively. The model was well-calibrated (P > 0.05), and DCA demonstrated its clinical usefulness. Thus, this predictive model might facilitate the individualized preoperative prediction of pneumothorax in CT-TNB for SPN.

BackgroundSeveral risk factors have been proposed for bleeding during bronchoscopy, including immunosuppression, thrombocytopenia, pulmonary arterial hypertension, and mechanical ventilation. However, research on bronchoscopic biopsy-induced bleeding in the population of lung cancer without these “proposed risk factors” remains lacking.Patients and methodsA total of 531 lung cancer patients with endobronchial biopsy (EBB) were enrolled in this retrospective observational study. Patients were divided into biopsy-induced bleeding group (n=162) and non-bleeding group (n=369). Using multiple logistic regression, independent risk factors for EBB bleeding were identified.ResultsThe location, histologic type, and stage of lung cancer were independently associated with EBB bleeding, as assessed by multiple logistic regression (p<0.05) in patients with lung cancer. Moreover, during EBB, the risk of bleeding of endobronchial lesions located in the central airways was significantly higher when compared to that in peripheral bronchi (odds ratio [OR], 2.211; 95% CI, 1.276–3.830; p=0.005). In addition, squamous cell carcinoma and small-cell lung carcinoma were more susceptible to bleeding during biopsy when compared with adenocarcinoma (OR, 3.107, 2.389; 95% CI, 1.832–5.271, 1.271–4.489; p=0.000, p=0.007, respectively). Patients with advanced lung cancer were more prone to EBB bleeding compared to patients in the early stages of disease (OR, 1.583; 95% CI, 1.065–2.354; p=0.023).ConclusionLesions located in the central airways, histologic types of squamous cell carcinoma and small-cell lung carcinoma, and stages of advanced lung cancer were the independent risk factors for hemorrhage in EBB.

Background and purpose High blood urea nitrogen (BUN) is associated with an elevated risk of mortality in various diseases, such as heart failure and pneumonia. Heart failure and pneumonia are common comorbidities of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). However, data on the relationship of BUN levels with mortality in patients with AECOPD are sparse. The purpose of this study was to evaluate the correlation between BUN level and in-hospital mortality in a cohort of patients with AECOPD who presented at the emergency department (ED). Methods A total of 842 patients with AECOPD were enrolled in the retrospective observational study from January 2018 to September 2020. The outcome was all-cause in-hospital mortality. Receiver operating characteristic (ROC) curve analysis and logistic regression models were performed to evaluate the association of BUN levels with in-hospital mortality in patients with AECOPD. Propensity score matching was used to assemble a cohort of patients with similar baseline characteristics, and logistic regression models were also performed in the propensity score matching cohort. Results During hospitalization, 26 patients (3.09%) died from all causes, 142 patients (16.86%) needed invasive ventilation, and 190 patients (22.57%) were admitted to the ICU. The mean level of blood urea nitrogen was 7.5 ± 4.5 mmol/L. Patients in the hospital non-survivor group had higher BUN levels (13.48 ± 9.62 mmol/L vs. 7.35 ± 4.14 mmol/L, p < 0.001) than those in the survivor group. The area under the curve (AUC) was 0.76 (95% CI 0.73–0.79, p < 0.001), and the optimal BUN level cutoff was 7.63 mmol/L for hospital mortality. As a continuous variable, BUN level was associated with hospital mortality after adjusting respiratory rate, level of consciousness, pH, PCO2, lactic acid, albumin, glucose, CRP, hemoglobin, platelet distribution width, D-dimer, and pro-B-type natriuretic peptide (OR 1.10, 95% CI 1.03–1.17, p=0.005). The OR of hospital mortality was significantly higher in the BUN level ≥7.63 mmol/L group than in the BUN level <7.63 mmol/L group in adjusted model (OR 3.29, 95% CI 1.05–10.29, p=0.041). Similar results were found after multiple imputation and in the propensity score matching cohort. Conclusions Increased BUN level at ED admission is associated with hospital mortality in patients with AECOPD who present at the ED. The level of 7.63 mmol/L can be used as a cutoff value for critical stratification.

Malignant glomus tumor, or glomangiosarcoma, is a very rare mesenchymal neoplasm that, when seen, occurs in visceral organs. Despite having histologic features of malignancy, these tumors usually do not metastasize. However, when metastasis occurs, this disease is often fatal. Our report presents the case of a 59-year-old female patient with a highly aggressive and widely metastatic glomus tumor of the lung.

Background Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a common presentation in emergency departments (ED) that can be fatal. This study aimed to develop a mortality risk assessment model for patients presenting to the ED with AECOPD and hypercapnic respiratory failure. Methods We analysed 601 participants who were presented to an ED of a tertiary hospital with AECOPD between 2018 and 2020. Patient demographics, vital signs, and altered mental status were assessed on admission; moreover, the initial laboratory findings and major comorbidities were assessed. We used least absolute shrinkage and selection operator (LASSO) regression to identify predictors for establishing a nomogram for in-hospital mortality. Predictive ability was assessed using the area under the receiver operating curve (AUC). A 500 bootstrap method was applied for internal validation; moreover, the model’s clinical utility was evaluated using decision curve analysis (DCA). Additionally, the nomogram was compared with other prognostic models, including CRB65, CURB65, BAP65, and NEWS. Results Among the 601 patients, 19 (3.16%) died during hospitalization. LASSO regression analysis identified 7 variables, including respiratory rate, PCO2, lactic acid, blood urea nitrogen, haemoglobin, platelet distribution width, and platelet count. These 7 variables and the variable of concomitant pneumonia were used to establish a predictive model. The nomogram showed good calibration and discrimination for mortality (AUC 0.940; 95% CI 0.895–0.985), which was higher than that of previous models. The DCA showed that our nomogram had clinical utility. Conclusions Our nomogram, which is based on clinical variables that can be easily obtained at presentation, showed favourable predictive accuracy for mortality in patients with AECOPD with hypercapnic respiratory failure.