Purpose: Emphysematous pyelonephritis (EPN) is a life-threatening disease requiring immediate treatment. This multicenter retrospective cohort study aimed to analyze the mortality rate and risk factors associated with EPN. Materials and Methods: Between January 2011 and February 2021, 217 patients diagnosed with EPN via computed tomography who visited 14 teaching hospitals were retrospectively analyzed. Clinical data, including age, sex, comorbidities, Huang and Tseng classification, hydronephrosis, acute kidney injury, blood and urine tests, surgical interventions, percutaneous drainage, and conservative treatments, were compared between the survival and death groups. Risk factors for mortality due to EPN were analyzed using univariate and multivariate methods. Results: The mean age of survivors and deceased patients was 67.8 and 69.0 years, respectively (p=0.136). The sex distribution (male/female) was 48/146 and 8/15, respectively (p=0.298). Of the 217 patients, 23 died, resulting in a mortality rate of 10.6%. In univariate analysis, the Huang and Tseng classification (p=0.004), platelet count (p=0.005), and acute kidney injury (p=0.007) were significantly associated with mortality from EPN. In multivariate analysis, only the Huang and Tseng classification (p=0.029) was identified as a risk factor. Mortality rates according to the Huang and Tseng classification were as follows: class I (5.88%), class II (7.50%), class IIIa (14.28%), class IIIb (25.00%), and class IV (23.07%). Conclusions EPN is associated with a high mortality rate. Among various clinical factors, the Huang and Tseng classification was the most significant indicator for predicting mortality.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Purpose: Emphysematous pyelonephritis (EPN) is a life-threatening disease requiring immediate treatment. This multicenter retrospective cohort study aimed to analyze the mortality rate and risk factors associated with EPN. Materials and Methods: Between January 2011 and February 2021, 217 patients diagnosed with EPN via computed tomography who visited 14 teaching hospitals were retrospectively analyzed. Clinical data, including age, sex, comorbidities, Huang and Tseng classification, hydronephrosis, acute kidney injury, blood and urine tests, surgical interventions, percutaneous drainage, and conservative treatments, were compared between the survival and death groups. Risk factors for mortality due to EPN were analyzed using univariate and multivariate methods. Results: The mean age of survivors and deceased patients was 67.8 and 69.0 years, respectively (p=0.136). The sex distribution (male/female) was 48/146 and 8/15, respectively (p=0.298). Of the 217 patients, 23 died, resulting in a mortality rate of 10.6%. In univariate analysis, the Huang and Tseng classification (p=0.004), platelet count (p=0.005), and acute kidney injury (p=0.007) were significantly associated with mortality from EPN. In multivariate analysis, only the Huang and Tseng classification (p=0.029) was identified as a risk factor. Mortality rates according to the Huang and Tseng classification were as follows: class I (5.88%), class II (7.50%), class IIIa (14.28%), class IIIb (25.00%), and class IV (23.07%). Conclusions EPN is associated with a high mortality rate. Among various clinical factors, the Huang and Tseng classification was the most significant indicator for predicting mortality.

Purpose: Emphysematous pyelonephritis (EPN) is a life-threatening disease requiring immediate treatment. This multicenter retrospective cohort study aimed to analyze the mortality rate and risk factors associated with EPN. Materials and Methods: Between January 2011 and February 2021, 217 patients diagnosed with EPN via computed tomography who visited 14 teaching hospitals were retrospectively analyzed. Clinical data, including age, sex, comorbidities, Huang and Tseng classification, hydronephrosis, acute kidney injury, blood and urine tests, surgical interventions, percutaneous drainage, and conservative treatments, were compared between the survival and death groups. Risk factors for mortality due to EPN were analyzed using univariate and multivariate methods. Results: The mean age of survivors and deceased patients was 67.8 and 69.0 years, respectively (p=0.136). The sex distribution (male/female) was 48/146 and 8/15, respectively (p=0.298). Of the 217 patients, 23 died, resulting in a mortality rate of 10.6%. In univariate analysis, the Huang and Tseng classification (p=0.004), platelet count (p=0.005), and acute kidney injury (p=0.007) were significantly associated with mortality from EPN. In multivariate analysis, only the Huang and Tseng classification (p=0.029) was identified as a risk factor. Mortality rates according to the Huang and Tseng classification were as follows: class I (5.88%), class II (7.50%), class IIIa (14.28%), class IIIb (25.00%), and class IV (23.07%). Conclusions EPN is associated with a high mortality rate. Among various clinical factors, the Huang and Tseng classification was the most significant indicator for predicting mortality.

Background: and Purpose Treatments for neuromyelitis optica spectrum disorder (NMOSD) such as eculizumab, ravulizumab, satralizumab, and inebilizumab have significantly advanced relapse prevention, but they remain expensive. Rituximab is an off-label yet popular alternative that offers a cost-effective solution, but its real-world efficacy needs better quantification for guiding the application of newer approved NMOSD treatments (ANTs). This study aimed to determine real-world rituximab failure rates to anticipate the demand for ANTs and aid in resource allocation. Methods: We conducted a nationwide retrospective study involving 605 aquaporin-4-antibody-positive NMOSD patients from 22 centers in South Korea that assessed the efficacy and safety of rituximab over a median follow-up of 47 months. Results: The 605 patients treated with rituximab included 525 (87%) who received continuous therapy throughout the follow-up period (median=47 months, interquartile range=15–87 months). During this period, 117 patients (19%) experienced at least 1 relapse. Notably, 68 of these patients (11% of the total cohort) experienced multiple relapses or at least 1 severe relapse.Additionally, 2% of the patients discontinued rituximab due to adverse events, which included severe infusion reactions, neutropenia, and infections. Conclusions This study has confirmed the efficacy of rituximab in treating NMOSD, as evidenced by an 87% continuation rate among patients over a 4-year follow-up period. Nevertheless, the occurrence of at least one relapse in 19% of the cohort, including 11% who experienced multiple or severe relapses, and a 2% discontinuation rate due to adverse events highlight the urgent need for alternative therapeutic options.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background: and Purpose Treatments for neuromyelitis optica spectrum disorder (NMOSD) such as eculizumab, ravulizumab, satralizumab, and inebilizumab have significantly advanced relapse prevention, but they remain expensive. Rituximab is an off-label yet popular alternative that offers a cost-effective solution, but its real-world efficacy needs better quantification for guiding the application of newer approved NMOSD treatments (ANTs). This study aimed to determine real-world rituximab failure rates to anticipate the demand for ANTs and aid in resource allocation. Methods: We conducted a nationwide retrospective study involving 605 aquaporin-4-antibody-positive NMOSD patients from 22 centers in South Korea that assessed the efficacy and safety of rituximab over a median follow-up of 47 months. Results: The 605 patients treated with rituximab included 525 (87%) who received continuous therapy throughout the follow-up period (median=47 months, interquartile range=15–87 months). During this period, 117 patients (19%) experienced at least 1 relapse. Notably, 68 of these patients (11% of the total cohort) experienced multiple relapses or at least 1 severe relapse.Additionally, 2% of the patients discontinued rituximab due to adverse events, which included severe infusion reactions, neutropenia, and infections. Conclusions This study has confirmed the efficacy of rituximab in treating NMOSD, as evidenced by an 87% continuation rate among patients over a 4-year follow-up period. Nevertheless, the occurrence of at least one relapse in 19% of the cohort, including 11% who experienced multiple or severe relapses, and a 2% discontinuation rate due to adverse events highlight the urgent need for alternative therapeutic options.

Purpose: Emphysematous pyelonephritis (EPN) is a life-threatening disease requiring immediate treatment. This multicenter retrospective cohort study aimed to analyze the mortality rate and risk factors associated with EPN. Materials and Methods: Between January 2011 and February 2021, 217 patients diagnosed with EPN via computed tomography who visited 14 teaching hospitals were retrospectively analyzed. Clinical data, including age, sex, comorbidities, Huang and Tseng classification, hydronephrosis, acute kidney injury, blood and urine tests, surgical interventions, percutaneous drainage, and conservative treatments, were compared between the survival and death groups. Risk factors for mortality due to EPN were analyzed using univariate and multivariate methods. Results: The mean age of survivors and deceased patients was 67.8 and 69.0 years, respectively (p=0.136). The sex distribution (male/female) was 48/146 and 8/15, respectively (p=0.298). Of the 217 patients, 23 died, resulting in a mortality rate of 10.6%. In univariate analysis, the Huang and Tseng classification (p=0.004), platelet count (p=0.005), and acute kidney injury (p=0.007) were significantly associated with mortality from EPN. In multivariate analysis, only the Huang and Tseng classification (p=0.029) was identified as a risk factor. Mortality rates according to the Huang and Tseng classification were as follows: class I (5.88%), class II (7.50%), class IIIa (14.28%), class IIIb (25.00%), and class IV (23.07%). Conclusions EPN is associated with a high mortality rate. Among various clinical factors, the Huang and Tseng classification was the most significant indicator for predicting mortality.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.