Background/Aims: This study aimed to evaluate the performance of the Model for End-Stage Liver Disease (MELD) 3.0 for predicting mortality and liver-related complications compared with the Child-Pugh classification, albumin-bilirubin (ALBI) grade, the MELD, and the MELD sodium (MELDNa) score. Methods: We evaluated a multicenter retrospective cohort of incorporated patients with cirrhosis between 2013 and 2019. We conducted comparisons of the area under the receiver operating characteristic curve (AUROC) of the MELD3.0 and other models for predicting 3-month mortality. Additionally, we assessed the risk of cirrhosis-related complications according to the MELD3.0 score. Results: A total of 3,314 patients were included. The mean age was 55.9±11.3 years, and 70.2% of the patients were male. Within the initial 3 months, 220 patients (6.6%) died, and the MELD3.0had the best predictive performance among the tested models, with an AUROC of 0.851, outperforming the Child-Pugh classification, ALBI grade, MELD, and MELDNa. A high MELD3.0score was associated with an increased risk of mortality. Compared with that of the group with a MELD3.0 score <10 points, the adjusted hazard ratio of the group with a score of 10–20 pointswas 2.176, and that for the group with a score of ≥20 points was 4.892. Each 1-point increase inthe MELD3.0 score increased the risk of cirrhosis-related complications by 1.033-fold. The risk of hepatorenal syndrome showed the highest increase, with an adjusted hazard ratio of 1.149, followed by hepatic encephalopathy and ascites. Conclusions The MELD3.0 demonstrated robust prognostic performance in predicting mortality in patients with cirrhosis. Moreover, the MELD3.0 score was linked to cirrhosis-related complications, particularly those involving kidney function, such as hepatorenal syndrome and ascites.

Background/Aims: This study aimed to evaluate the performance of the Model for End-Stage Liver Disease (MELD) 3.0 for predicting mortality and liver-related complications compared with the Child-Pugh classification, albumin-bilirubin (ALBI) grade, the MELD, and the MELD sodium (MELDNa) score. Methods: We evaluated a multicenter retrospective cohort of incorporated patients with cirrhosis between 2013 and 2019. We conducted comparisons of the area under the receiver operating characteristic curve (AUROC) of the MELD3.0 and other models for predicting 3-month mortality. Additionally, we assessed the risk of cirrhosis-related complications according to the MELD3.0 score. Results: A total of 3,314 patients were included. The mean age was 55.9±11.3 years, and 70.2% of the patients were male. Within the initial 3 months, 220 patients (6.6%) died, and the MELD3.0had the best predictive performance among the tested models, with an AUROC of 0.851, outperforming the Child-Pugh classification, ALBI grade, MELD, and MELDNa. A high MELD3.0score was associated with an increased risk of mortality. Compared with that of the group with a MELD3.0 score <10 points, the adjusted hazard ratio of the group with a score of 10–20 pointswas 2.176, and that for the group with a score of ≥20 points was 4.892. Each 1-point increase inthe MELD3.0 score increased the risk of cirrhosis-related complications by 1.033-fold. The risk of hepatorenal syndrome showed the highest increase, with an adjusted hazard ratio of 1.149, followed by hepatic encephalopathy and ascites. Conclusions The MELD3.0 demonstrated robust prognostic performance in predicting mortality in patients with cirrhosis. Moreover, the MELD3.0 score was linked to cirrhosis-related complications, particularly those involving kidney function, such as hepatorenal syndrome and ascites.

Background/Aims: This study aimed to evaluate the performance of the Model for End-Stage Liver Disease (MELD) 3.0 for predicting mortality and liver-related complications compared with the Child-Pugh classification, albumin-bilirubin (ALBI) grade, the MELD, and the MELD sodium (MELDNa) score. Methods: We evaluated a multicenter retrospective cohort of incorporated patients with cirrhosis between 2013 and 2019. We conducted comparisons of the area under the receiver operating characteristic curve (AUROC) of the MELD3.0 and other models for predicting 3-month mortality. Additionally, we assessed the risk of cirrhosis-related complications according to the MELD3.0 score. Results: A total of 3,314 patients were included. The mean age was 55.9±11.3 years, and 70.2% of the patients were male. Within the initial 3 months, 220 patients (6.6%) died, and the MELD3.0had the best predictive performance among the tested models, with an AUROC of 0.851, outperforming the Child-Pugh classification, ALBI grade, MELD, and MELDNa. A high MELD3.0score was associated with an increased risk of mortality. Compared with that of the group with a MELD3.0 score <10 points, the adjusted hazard ratio of the group with a score of 10–20 pointswas 2.176, and that for the group with a score of ≥20 points was 4.892. Each 1-point increase inthe MELD3.0 score increased the risk of cirrhosis-related complications by 1.033-fold. The risk of hepatorenal syndrome showed the highest increase, with an adjusted hazard ratio of 1.149, followed by hepatic encephalopathy and ascites. Conclusions The MELD3.0 demonstrated robust prognostic performance in predicting mortality in patients with cirrhosis. Moreover, the MELD3.0 score was linked to cirrhosis-related complications, particularly those involving kidney function, such as hepatorenal syndrome and ascites.

Background/Aims: This study aimed to evaluate the performance of the Model for End-Stage Liver Disease (MELD) 3.0 for predicting mortality and liver-related complications compared with the Child-Pugh classification, albumin-bilirubin (ALBI) grade, the MELD, and the MELD sodium (MELDNa) score. Methods: We evaluated a multicenter retrospective cohort of incorporated patients with cirrhosis between 2013 and 2019. We conducted comparisons of the area under the receiver operating characteristic curve (AUROC) of the MELD3.0 and other models for predicting 3-month mortality. Additionally, we assessed the risk of cirrhosis-related complications according to the MELD3.0 score. Results: A total of 3,314 patients were included. The mean age was 55.9±11.3 years, and 70.2% of the patients were male. Within the initial 3 months, 220 patients (6.6%) died, and the MELD3.0had the best predictive performance among the tested models, with an AUROC of 0.851, outperforming the Child-Pugh classification, ALBI grade, MELD, and MELDNa. A high MELD3.0score was associated with an increased risk of mortality. Compared with that of the group with a MELD3.0 score <10 points, the adjusted hazard ratio of the group with a score of 10–20 pointswas 2.176, and that for the group with a score of ≥20 points was 4.892. Each 1-point increase inthe MELD3.0 score increased the risk of cirrhosis-related complications by 1.033-fold. The risk of hepatorenal syndrome showed the highest increase, with an adjusted hazard ratio of 1.149, followed by hepatic encephalopathy and ascites. Conclusions The MELD3.0 demonstrated robust prognostic performance in predicting mortality in patients with cirrhosis. Moreover, the MELD3.0 score was linked to cirrhosis-related complications, particularly those involving kidney function, such as hepatorenal syndrome and ascites.

Background: Pre-diabetes can develop into type 2 diabetes mellitus, but can prevented by regular exercise.However, the outcomes when combining unsupervised Kinect-based mixed-reality (KMR) exercise with continuous glucose monitoring (CGM) remain unclear. Therefore, this single-arm pilot trial examined changes in blood glucose (BG) concentrations over 672 hours (4 weeks), including a 2-week period of KMR exercise and CGM in individuals with pre-diabetes. Methods: This was a pre-and post-treatment case-control study with nine participants. General questionnaires were administered and body composition, fasting BG concentrations, and 2-hour oral glucose tolerance test (2-OGTT) results were measured pre-and post-treatment. Weekly average glucose concentrations, hyperglycemia rate, hypoglycemia rate, average glucose concentration over time, amount of physical activity, amount of food intake, and pre- and postprandial BG (immediately and 30, 60, 90, and 120 minutes after lunch) were measured over 4 weeks (pre-test, exercise, and post -test weeks). Glucose concentrations were measured before exercising, between sets, and 30 and 60 minutes after exercise during the 2 weeks of unsupervised exercise (3 days/week). Results: In all participants, body mass index (27.16±2.92 kg/m²), fasting BG (108.00±7.19 mg/dL), 2-OGTT (162.56±18.12 mg/dL), hyperglycemia rate (P= 0.040), and 90-minute postprandial BG (P= 0.035) were significantly reduced during the 2 exercise weeks, and the 2-OGTT result (P= 0.044) and diastolic blood pressure (DBP) (P= 0.046) were significantly reduced at the post- test as compared with the pre-test. Conclusion This study found that 2 weeks of unsupervised KMR exercise reduced 2-OGTT, DBP, hyperglycemia rate, and 90-minute postprandial BG concentration. We believed this effect could be identified more clearly in studies involving a larger number of participants and longer durations of exercise.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Background: Pre-diabetes can develop into type 2 diabetes mellitus, but can prevented by regular exercise.However, the outcomes when combining unsupervised Kinect-based mixed-reality (KMR) exercise with continuous glucose monitoring (CGM) remain unclear. Therefore, this single-arm pilot trial examined changes in blood glucose (BG) concentrations over 672 hours (4 weeks), including a 2-week period of KMR exercise and CGM in individuals with pre-diabetes. Methods: This was a pre-and post-treatment case-control study with nine participants. General questionnaires were administered and body composition, fasting BG concentrations, and 2-hour oral glucose tolerance test (2-OGTT) results were measured pre-and post-treatment. Weekly average glucose concentrations, hyperglycemia rate, hypoglycemia rate, average glucose concentration over time, amount of physical activity, amount of food intake, and pre- and postprandial BG (immediately and 30, 60, 90, and 120 minutes after lunch) were measured over 4 weeks (pre-test, exercise, and post -test weeks). Glucose concentrations were measured before exercising, between sets, and 30 and 60 minutes after exercise during the 2 weeks of unsupervised exercise (3 days/week). Results: In all participants, body mass index (27.16±2.92 kg/m²), fasting BG (108.00±7.19 mg/dL), 2-OGTT (162.56±18.12 mg/dL), hyperglycemia rate (P= 0.040), and 90-minute postprandial BG (P= 0.035) were significantly reduced during the 2 exercise weeks, and the 2-OGTT result (P= 0.044) and diastolic blood pressure (DBP) (P= 0.046) were significantly reduced at the post- test as compared with the pre-test. Conclusion This study found that 2 weeks of unsupervised KMR exercise reduced 2-OGTT, DBP, hyperglycemia rate, and 90-minute postprandial BG concentration. We believed this effect could be identified more clearly in studies involving a larger number of participants and longer durations of exercise.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Background: Pre-diabetes can develop into type 2 diabetes mellitus, but can prevented by regular exercise.However, the outcomes when combining unsupervised Kinect-based mixed-reality (KMR) exercise with continuous glucose monitoring (CGM) remain unclear. Therefore, this single-arm pilot trial examined changes in blood glucose (BG) concentrations over 672 hours (4 weeks), including a 2-week period of KMR exercise and CGM in individuals with pre-diabetes. Methods: This was a pre-and post-treatment case-control study with nine participants. General questionnaires were administered and body composition, fasting BG concentrations, and 2-hour oral glucose tolerance test (2-OGTT) results were measured pre-and post-treatment. Weekly average glucose concentrations, hyperglycemia rate, hypoglycemia rate, average glucose concentration over time, amount of physical activity, amount of food intake, and pre- and postprandial BG (immediately and 30, 60, 90, and 120 minutes after lunch) were measured over 4 weeks (pre-test, exercise, and post -test weeks). Glucose concentrations were measured before exercising, between sets, and 30 and 60 minutes after exercise during the 2 weeks of unsupervised exercise (3 days/week). Results: In all participants, body mass index (27.16±2.92 kg/m²), fasting BG (108.00±7.19 mg/dL), 2-OGTT (162.56±18.12 mg/dL), hyperglycemia rate (P= 0.040), and 90-minute postprandial BG (P= 0.035) were significantly reduced during the 2 exercise weeks, and the 2-OGTT result (P= 0.044) and diastolic blood pressure (DBP) (P= 0.046) were significantly reduced at the post- test as compared with the pre-test. Conclusion This study found that 2 weeks of unsupervised KMR exercise reduced 2-OGTT, DBP, hyperglycemia rate, and 90-minute postprandial BG concentration. We believed this effect could be identified more clearly in studies involving a larger number of participants and longer durations of exercise.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.