Seventeen percent of Korean adults over the age of 45 years have mild chronic obstructive pulmonary disease.

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7772 (75.9%) recovered, and 2237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities > 90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.

Lung function in cases of chronic airflow obstruction (CAO) due to tuberculous destroyed lung, which is still common in Korea, has not been objectively investigated. We evaluated lung functions and postbronchodilator responses in 21 CAO patients with a forced expiratory volume in 1 s (FEV1) of 30-65% of the predicted value, and compared some of these results with those of age-, sex- and FEV1% predicted-matched patients with chronic obstructive pulmonary disease (COPD). In addition, we analyzed the lung functions of CAO patients with respect to wheezing. The forced vital capacity (FVC) (P < 0.05) and postbronchodilator FEV1 of CAO patients were lower than those of COPD patients (P < 0.05). When a positive bronchodilator response was defined as an absolute change of FEV1 (FEV1 delta(abs)) of more than 0.2 l (P < 0.05) and a percentage of initial FEV1 (FEV1 delta%init) of more than 12%, the positive rates in CAO patients were lower than in COPD patients (P < 0.05). Among the CAO patients, patients with wheezing showed lower forced expiratory flow 25%-75% (FEF(25-75%)) (P < 0.05) and higher airway resistance than those without wheezing (P < 0.05). CAO patients with wheezing were more responsive to bronchodilator than those without wheezing. Although the pathophysiology of CAO differs from that of COPD, bronchodilator therapy could be useful for treating CAO, especially in cases presenting with wheezing.

These results indicate that VEGF is a useful marker for predicting the tumor invasion in patients with early tongue cancer.

Iterative reconstruction allows ultra-low-dose CT and affords acceptable image quality, allowing size measurements of solid pulmonary nodules to be made.

Changes in symptom score and peak expiratory flow (PEF) and the use of medications during the first acute exacerbation of asthma were studied in 41 patients and matched controls who took part in a panel study. An acute exacerbation was defined as the presence of at least one of the following: any unscheduled physician visit, visit to an emergency room, or hospitalization for treatment of asthma; a decrease in PEF by 30% from the patient's best reading; an increase in asthma symptoms during the day and night for over 48 h and not responding to usual medications; and the commencement or doubling of the dose of oral or inhaled steroids for any of the foregoing reasons. Data from -9 to -7 d before the onset of an acute exacerbation were taken as the baseline data, with which the values of the subsequent 14 d were compared. Significant increases in symptoms occurred before a significant reduction in PEF. None of the patients had a decrease of more than 30% in PEF before the onset of symptoms. Daily variation in PEF was not significantly increased from the baseline. Our results suggest that PEF monitoring is not as sensitive as a symptom diary for revealing acute exacerbations of asthma, and that a 30% decrease in PEF is too stringent a criterion for defining an acute exacerbation.

BackgroundAdvances in bronchoscopy and CT-guided lung biopsy have improved the evaluation of small pulmonary lesions (PLs), leading to an increase in preoperative histological diagnosis.We aimed to evaluate the efficacy and safety of transbronchial lung biopsy using radial endobronchial ultrasound and virtual bronchoscopic navigation (TBLB-rEBUS&VBN) and CT-guided transthoracic needle biopsy (CT-TNB) for tissue diagnosis of small PLs.MethodsA systematic search was performed in five electronic databases, including MEDLINE, EMBASE, Cochrane Library Central Register of Controlled Trials, Web of Science, and Scopus, for relevant studies in May 2016; the selected articles were assessed using meta-analysis. The articles were limited to those published after 2000 that studied small PLs ≤ 3 cm in diameter.ResultsFrom 7345 records, 9 articles on the bronchoscopic (BR) approach and 15 articles on the percutaneous (PC) approach were selected. The pooled diagnostic yield was 75% (95% confidence interval [CI], 69–80) using the BR approach and 93% (95% CI, 90–96) using the PC approach. For PLs ≤ 2 cm, the PC approach (pooled diagnostic yield: 92%, 95% CI: 88–95) was superior to the BR approach (66%, 95% CI: 55–76). However, for PLs > 2 cm but ≤ 3 cm, the diagnostic yield using the BR approach was improved to 81% (95% CI, 75–85). Complications of pneumothorax and hemorrhage were rare with the BR approach but common with the PC approach.ConclusionsCT-TNB was superior to TBLB-rEBUS&VBN for the evaluation of small PLs. However, for lesions greater than 2 cm, the BR approach may be considered considering its diagnostic yield of over 80% and the low risk of procedure-related complications.

The rapid spread of COVID-19 is likely to result in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7,772 (75.9%) recovered, and 2,237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.3% [95% confidence interval: 83.3, 97.3]and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities >90%. The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (at large clusters or from personal contact with an infected individual), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources have to be wisely allocated without hesitation.