Diagnostic value of serum vascular endothelial growth factor-D in Korean patients with lymphangioleiomyomatosis.

BACKGROUND: Lymphangioleiomyomatosis (LAM) is a rare multisystemic disorder characterized by the proliferation of abnormal smooth muscle-like cells. Although serum vascular endothelial growth factor-D (VEGF-D) is currently used as a diagnostic biomarker for LAM, its diagnostic value in Korean patients is unclear. OBJECTIVES: To evaluate the diagnostic value of serum VEGF-D for LAM in Korean patients. DESIGN: A multicenter prospective cohort study. METHODS: Serum samples were prospectively collected from five medical institutions, from patients with LAM (n = 40) and controls (n = 24; healthy participants = 3, other cystic lung diseases = 13, idiopathic pulmonary fibrosis = 4, idiopathic nonspecific interstitial pneumonia = 4). Serum VEGF-D levels were measured using the enzyme-linked immunosorbent assay, and the diagnostic value was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: The mean age of patients with LAM was 44.5 years, and all were female (controls: 47.8 years; female: 70.8%, p < 0.001). The serum VEGF-D levels were significantly higher in patients with LAM than those in the control group (median: 708.9 pg/mL vs 325.3 pg/mL, p < 0.001). In the ROC curve analysis, serum VEGF-D levels showed good predicting performance for LAM diagnosis (area under the curve = 0.918) with an optimal cut-off value of 432.7 pg/mL (sensitivity = 85.0%, specificity = 87.5%). When 800 pg/mL was used as the cut-off value, the specificity of serum VEGF-D for LAM diagnosis increased to 100.0%. CONCLUSION: Our results suggest that serum VEGF-D may be a useful biomarker for diagnosing LAM in Korean patients, similar to previous reports.

Blood test for diagnosis of lymphangioleiomyomatosis in Korea: role of vascular endothelial growth factor-DIn this study, we discuss a blood test to diagnose a rare lung disease, called lymphangioleiomyomatosis (LAM). LAM primarily affects women, especially during their childbearing years, and can cause serious lung problems such as damage and cyst (air-filled sac) formation. The blood test looks for a special protein in the blood, called vascular endothelial growth factor-D (VEGF-D). If someone has a lot of this protein, it usually means that they have LAM. We have found that when VEGF-D levels are high, the test can effectively separate LAM from other lung diseases. We also found that raising this threshold to higher levels made it much more likely to correctly distinguish a group of people who do not have the disease from patients with LAM. Our study is important because it's the first to show the usefulness of blood VEGF-D testing in Korean LAM patients, and because it suggests an easier and less inconvenient way for physicians to diagnose LAM in Koreans. Our findings are an important step in improving the management of Korean patients with LAM.

Association of baseline muscle mass with functional outcomes in intensive care unit survivors: A single-center retrospective cohort study in Korea.

In critical care settings, ultrasound (US) of the quadriceps muscle and Bioelectrical Impedance Analysis (BIA) are noninvasive and widely available tools to evaluate muscle mass. We studied whether baseline muscle mass affects physical function in intensive care unit (ICU) survivors after discharge. This retrospective review of a prospective cohort enrolled 30 patients admitted to the medical ICU between April 2016 and June 2018. On ICU admission, quadriceps muscle thickness and skeletal muscle mass were measured using US and BIA, respectively. Muscle strength and physical function were measured using handgrip dynamometry, the 6-min walk test, and the Barthel index questionnaire survey during every clinic visit at 1, 3, 6, and 12 months after hospital discharge. Skeletal muscle mass at ICU admission was statistically correlated with the 6-min walk distance (6MWD) and Barthel index score. The segmental lean mass of the right arm was also positively correlated with handgrip muscle strength at 6 months after discharge. Likewise, the correlation between quadriceps muscle thickness at ICU admission and 6MWD at 6 months after discharge was positive and statistically significant. Multivariate regression analysis showed that skeletal muscle mass was associated with a reduced 6MWD, but the length of ICU stay was not. The segmental lean mass of the right arm also showed a significant association with handgrip strength after discharge. Low muscle mass on ICU admission is associated with reduced muscle strength, causing impaired physical function after hospital discharge in ICU survivors.

Occupational and environmental risk factors for idiopathic pulmonary fibrosis: A case-control study.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease of unknown etiology. The aim of this study was to evaluate the environmental and occupational risk factors of IPF. METHODS: This hospital-based, case-control study included 206 patients with IPF selected from the Seoul National University Bundang Hospital Interstitial Lung Disease registry and 167 controls without lung disease. Data on occupation, lifestyle, transportation, and types of environmental and occupational dust exposure were obtained using a questionnaire. IPF diagnosis was confirmed based on the recent guidelines, and the possibility of hypersensitivity pneumonitis was excluded. Multiple logistic regression was performed to determine the risk factors for IPF. RESULTS: After adjusting for age and sex, ever-smokers (odds ratio [OR], 2.35; 95 % confidence interval [CI]: 1.51-3.68) and individuals who smoked more than 30 pack-years (OR, 2.79; 95%CI: 1.70-4.68) showed an increased risk for IPF. Any occupational dust exposure (adjusted OR, 2.08; 95%CI: 1.19-3.72), especially exposure to chemicals (adjusted OR, 3.52; 99%CI: 1.56-9.05), was associated with IPF after adjusting for age, sex, and smoking. CONCLUSIONS: Smoking and occupational dust exposure are associated with an increased risk for IPF. Both factors have dose and duration-dependent relationships with the risk for IPF.

Understanding the effects of Haemophilus influenzae colonization on bronchiectasis: a retrospective cohort study.

BACKGROUND: Bacterial colonization is an essential aspect of bronchiectasis. Although Haemophilus influenzae is a frequent colonizer in some regions, its clinical impacts are poorly understood. This study aimed to elucidate the impact of H. influenzae colonization in patients with bronchiectasis. METHODS: This retrospective study screened adult patients diagnosed with bronchiectasis at a tertiary referral center between April 1, 2003, and May 16, 2021, in South Korea. Propensity score matching was used to match patients with and without H. influenzae colonization. We assessed the severity of bronchiectasis as per the bronchiectasis severity index, the incidence of exacerbation, differences in lung function, and all-cause mortality. RESULTS: Out of the 4,453 patients with bronchiectasis, 79 (1.8%) were colonized by H. influenzae. After 1:2 propensity score matching, 78 and 154 patients were selected from the H. influenzae colonizer and non-colonizer groups, respectively. Although there were no significant differences between the groups regarding baseline demographics, patients colonized with H. influenzae had a higher bronchiectasis severity index (median 6 [interquartile range 4-8] vs. 4 [2-7], p = 0.002), associated with extensive radiographic involvement (52.2% vs. 37.2%, p = 0.045) and mild exacerbation without hospitalization (adjusted incidence rate ratio 0.15; 95% confidence interval 0.12-0.24). Lung function and mortality rates did not reveal significant differences, regardless of H. influenzae colonization. CONCLUSION: H. influenzae colonization in bronchiectasis was associated with more severe disease and greater incidence of mild exacerbation, but not lung function and mortality. Attention should be paid to patients with bronchiectasis with H. influenzae colonization.

Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs.

OBJECTIVES: To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs. METHODS: To develop a deep learning-based prognostic model using chest radiographs (DLPM), the patients diagnosed with IPF during 2011-2021 were retrospectively collected and were divided into training (n = 1007), validation (n = 117), and internal test (n = 187) datasets. Up to 10 consecutive radiographs were included for each patient. For external testing, three cohorts from independent institutions were collected (n = 152, 141, and 207). The discrimination performance of DLPM was evaluated using areas under the time-dependent receiver operating characteristic curves (TD-AUCs) for 3-year survival and compared with that of forced vital capacity (FVC). Multivariable Cox regression was performed to investigate whether the DLPM was an independent prognostic factor from FVC. We devised a modified gender-age-physiology (GAP) index (GAP-CR), by replacing DLCO with DLPM. RESULTS: DLPM showed similar-to-higher performance at predicting 3-year survival than FVC in three external test cohorts (TD-AUC: 0.83 [95% CI: 0.76-0.90] vs. 0.68 [0.59-0.77], p < 0.001; 0.76 [0.68-0.85] vs. 0.70 [0.60-0.80], p = 0.21; 0.79 [0.72-0.86] vs. 0.76 [0.69-0.83], p = 0.41). DLPM worked as an independent prognostic factor from FVC in all three cohorts (ps < 0.001). The GAP-CR index showed a higher 3-year TD-AUC than the original GAP index in two of the three external test cohorts (TD-AUC: 0.85 [0.80-0.91] vs. 0.79 [0.72-0.86], p = 0.02; 0.72 [0.64-0.80] vs. 0.69 [0.61-0.78], p = 0.56; 0.76 [0.69-0.83] vs. 0.68 [0.60-0.76], p = 0.01). CONCLUSIONS: A deep learning model successfully predicted survival in patients with IPF from chest radiographs, comparable to and independent of FVC. CLINICAL RELEVANCE STATEMENT: Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity. KEY POINTS: • A deep learning-based prognostic model for idiopathic pulmonary fibrosis was developed using 6063 radiographs. • The prognostic performance of the model was comparable-to-higher than forced vital capacity, and was independent from FVC in all three external test cohorts. • A modified gender-age-physiology index replacing diffusing capacity for carbon monoxide with the deep learning model showed higher performance than the original index in two external test cohorts.