Purpose: The association between the fecal microbiota and colorectal cancer (CRC) risk has been suggested in epidemiologic studies. However, data from large-scale population-based studies are lacking. Materials and Methods: In this case-control study, we recruited 283 CRC patients from the Center for Colorectal Cancer, National Cancer Center Hospital, Korea to perform 16S rRNA gene sequencing of fecal samples. A total of 283 age- and sex-matched healthy participants were selected from 890 cohort of healthy Koreans that are publicly available (PRJEB33905). The microbial dysbiosis index (MDI) was calculated based on the differentially abundant species. The association between MDI and CRC risk was observed using conditional logistic regression. Sparse Canonical Correlation Analysis was performed to integrate species data with microbial pathways obtained by PICRUSt2. Results: There is a significant divergence of the microbial composition between CRC patients and controls (permutational multivariate analysis of variance p=0.001). Those who were in third tertile of the MDI showed a significantly increased risk of CRC in the total population (odds ratio [OR], 6.93; 95% confidence interval [CI], 3.98 to 12.06; p-trend < 0.001) compared to those in the lowest tertile. Similar results were found for men (OR, 6.28; 95% CI, 3.04 to 12.98; p-trend < 0.001) and women (OR, 7.39; 95% CI, 3.10 to 17.63; p-trend < 0.001). Bacteroides coprocola and Bacteroides plebeius species and 12 metabolic pathways were interrelated in healthy controls that explain 91% covariation across samples. Conclusion Dysbiosis in the fecal microbiota may be associated with an increased risk of CRC. Due to the potentially modifiable nature of the gut microbiota, our findings may have implications for CRC prevention among Koreans.

Purpose: The association between the fecal microbiota and colorectal cancer (CRC) risk has been suggested in epidemiologic studies. However, data from large-scale population-based studies are lacking. Materials and Methods: In this case-control study, we recruited 283 CRC patients from the Center for Colorectal Cancer, National Cancer Center Hospital, Korea to perform 16S rRNA gene sequencing of fecal samples. A total of 283 age- and sex-matched healthy participants were selected from 890 cohort of healthy Koreans that are publicly available (PRJEB33905). The microbial dysbiosis index (MDI) was calculated based on the differentially abundant species. The association between MDI and CRC risk was observed using conditional logistic regression. Sparse Canonical Correlation Analysis was performed to integrate species data with microbial pathways obtained by PICRUSt2. Results: There is a significant divergence of the microbial composition between CRC patients and controls (permutational multivariate analysis of variance p=0.001). Those who were in third tertile of the MDI showed a significantly increased risk of CRC in the total population (odds ratio [OR], 6.93; 95% confidence interval [CI], 3.98 to 12.06; p-trend < 0.001) compared to those in the lowest tertile. Similar results were found for men (OR, 6.28; 95% CI, 3.04 to 12.98; p-trend < 0.001) and women (OR, 7.39; 95% CI, 3.10 to 17.63; p-trend < 0.001). Bacteroides coprocola and Bacteroides plebeius species and 12 metabolic pathways were interrelated in healthy controls that explain 91% covariation across samples. Conclusion Dysbiosis in the fecal microbiota may be associated with an increased risk of CRC. Due to the potentially modifiable nature of the gut microbiota, our findings may have implications for CRC prevention among Koreans.

Purpose: The association between the fecal microbiota and colorectal cancer (CRC) risk has been suggested in epidemiologic studies. However, data from large-scale population-based studies are lacking. Materials and Methods: In this case-control study, we recruited 283 CRC patients from the Center for Colorectal Cancer, National Cancer Center Hospital, Korea to perform 16S rRNA gene sequencing of fecal samples. A total of 283 age- and sex-matched healthy participants were selected from 890 cohort of healthy Koreans that are publicly available (PRJEB33905). The microbial dysbiosis index (MDI) was calculated based on the differentially abundant species. The association between MDI and CRC risk was observed using conditional logistic regression. Sparse Canonical Correlation Analysis was performed to integrate species data with microbial pathways obtained by PICRUSt2. Results: There is a significant divergence of the microbial composition between CRC patients and controls (permutational multivariate analysis of variance p=0.001). Those who were in third tertile of the MDI showed a significantly increased risk of CRC in the total population (odds ratio [OR], 6.93; 95% confidence interval [CI], 3.98 to 12.06; p-trend < 0.001) compared to those in the lowest tertile. Similar results were found for men (OR, 6.28; 95% CI, 3.04 to 12.98; p-trend < 0.001) and women (OR, 7.39; 95% CI, 3.10 to 17.63; p-trend < 0.001). Bacteroides coprocola and Bacteroides plebeius species and 12 metabolic pathways were interrelated in healthy controls that explain 91% covariation across samples. Conclusion Dysbiosis in the fecal microbiota may be associated with an increased risk of CRC. Due to the potentially modifiable nature of the gut microbiota, our findings may have implications for CRC prevention among Koreans.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

Background/Aims: Systemic lupus erythematosus (SLE) responder index (SRI)-4 response has been achieved with belimumab treatment in patients with moderate disease activity in cornerstone clinical trials and following studies. However, most studies involved patients treated with a mean prednisolone-equivalent dose of approximately 10 mg/d and focused on the steroid-sparing effect of belimumab. We aimed to identify the effect of belimumab in patients with mild-to-moderate SLE who were treated with low-dose or no corticosteroids. Methods: We retrospectively reviewed the electronic medical records of patients treated with belimumab for at least 6 months between May 2021 and June 2022. The primary endpoint was SRI-4 response at 6 months. Results: Thirty-one patients were included (13 low dose- and 18 steroid non-users). The mean age was 39.2 ± 11.4 years, and 90.3% of patients were female. The baseline Safety of Estrogens in Lupus Erythematosus National Assessment-Systemic Lupus Erythematosus Disease Activity Index (SELENA-SLEDAI) score was 6.0 (4.0–9.0). The primary endpoint was achieved in 32.3% (10/31) of patients. Significant improvements in anemia, C4 levels, and SELENA-SLEDAI score were observed during treatment. Univariate analysis showed that the baseline SELENA-SLEDAI and arthritis were significantly associated with SRI-4 response at 6 months, and only the SELENA-SLEDAI remained significant (p = 0.014) in multivariate analysis. Conclusions This cohort study is the first to report the efficacy of belimumab after minimizing the effect of corticosteroids. Belimumab showed efficacy in improving the SELENA-SLEDAI score, anemia, and low C4 in patients who did not receive corticosteroids or received only low doses.

Purpose: Due to the shortage of lung donors relative to the number of patients waiting for lung transplantation (LTx), more than one-third of patients on the waitlist have died without receiving LTx in Korea. Therefore, the importance of fair and effective allocation policies has been emphasized. This study investigated the characteristics of the current urgency-based allocation system in Korea by simulating the Eurotransplant lung allocation score (ET-LAS) using a nationwide multi-institutional registry for LTx in Korea. Materials and Methods: This study used data from the Korean Organ Transplantation Registry (KOTRY), along with additional retrospective data for ET-LAS calculation. A total of 194 patients were included in this study between January 2015 and December 2019. The Korean urgency definition classifies an LTx candidate as having statuses 0–3 according to urgency. The ET-LAS was analyzed according to the Korean urgency status. Results: In total, 92 patients received lung transplants at status 0, 85 at status 1, and 17 at status 2/3. The ET-LAS showed a bimodal distribution with distinct peaks corresponding to status 0 and non-status 0. There was no significant difference in the ET-LAS among non-status 0 patients. In logistic and decision tree analyses, oxygen supplementation methods, particularly oxygen masks and high-flow nasal cannulas, were significantly associated with a high ET-LAS (≥50) among non-status 0 patients. Conclusion Simulation of the ET-LAS with KOTRY data showed that the Korean urgency definition may not allocate lungs by urgency, especially for patients in non-status 0; therefore, it needs to be revised.

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R2 and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R2 value was 0.27 and in set II, LightGBM was the best model with the highest R2 value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R2 value of 0.56 and the lowest MSE of 174.07. Conclusion The LightGBM model showed the best performance in predicting postoperative lung function.

Objective: Renal involvement in anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) can lead to severe renal dysfunction requiring dialysis at diagnosis. We aimed to study the clinical and pathologic characteristics of patients with AAV dependent on dialysis at presentation and the long-term renal outcomes of patients who recovered from dialysis. Methods: This retrospective study analyzed data of patients diagnosed with AAV who were on dialysis from July 2005 to May 2021 at a single tertiary center in Korea. Results: Thirty-four patients were included in the study (median age: 64.5 years, females: 61.8%), of which 13 discontinued and 21 continued dialysis. The proportion of normal glomeruli (p<0.001) and interstitial fibrosis (p=0.024) showed significant differences between both groups. Multivariable analysis showed that the proportion of normal glomeruli was associated with dialysis discontinuation (odds ratio=1.29, 95% confidence interval 0.99~1.68, p=0.063), although without statistical significance. Treatment modalities, including plasmapheresis, did not show significance with dialysis discontinuation. In the follow-up analysis of 13 patients who had discontinued dialysis for a median of 81 months, 12 did not require dialysis, and their glomerular filtration rate values significantly increased at follow-up time compared to when they stopped dialysis (37.5 [28.5~45.5] vs. 24.0 [18.5~30.0] mL/ min/1.73 m²; p=0.008). Conclusion Approximately 38% of AAV patients on dialysis discontinued dialysis, and the recovered patients had improved renal function without dialysis during longer follow-up. Patients with AAV on dialysis should be given the possibility of dialysis discontinuation and renal recovery, especially those with normal glomeruli in kidney pathology.