Background: The 2020 European Society of Cardiology (ESC) 0-hr/1-hr algorithm using high-sensitivity cardiac troponin I (hs-cTnI) for non-ST-segment elevation acute coronary syndrome (NSTE-ACS) aims at early diagnosis and shorter emergency department (ED) stays. While this algorithm has been well-established in controlled studies, real-world implementation remains challenging. We evaluated the algorithm’s clinical performance and risk stratification capability in patients with chest pain or discomfort. Methods: We measured hs-cTnI in 4,678 patients suspected of NSTE-ACS between August 2022 and July 2023, using an Atellica IM Analyzer (Siemens Healthineers, Erlangen, Germany). We categorized patients into rule-in, observe, or rule-out groups according to the algorithm and assessed its diagnostic performance for NSTE-ACS. The final diagnosis of NSTE-ACS was adjudicated by two independent physicians. Additionally, we evaluated 30-day all-cause mortality, hazard risk, and ED length of stay across the three groups. Results: The algorithm categorized 3,408 (72.9%), 573 (12.2%), and 697 (14.9%) patients into the rule-out, observe, and rule-in groups, respectively. Among 90 patients diagnosed as having NSTE-ACS, none were falsely categorized into the rule-out group. Survival analysis revealed significant differences (P < 0.001), with Cox hazard ratios of 2.38 (95% confidence interval: 1.20–4.71) and 6.39 (3.45–11.86) in the observe and rule-in groups, respectively. ED stays shortened in the order of rule-out, observe, and rule-in groups (P < 0.001). Conclusions The 2020 ESC 0-hr/1-hr algorithm demonstrates excellent diagnostic accuracy without false rule-outs and effective risk stratification, and contributes to efficient ED throughput, supporting its clinical utility in real-world emergency settings.

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.

The heel is the second most common area for pressure injuries, which can lead to serious and threatening extremity infections. The late stages of deep hindfoot ulcers, where osteomyelitis commonly coexists, can result in an extended therapeutic window. Standard treatments for hindfoot ulcers complicated by osteomyelitis encompass debridement and flap surgery. In severe cases, below-knee amputation is also considered. Recent studies have reported the efficacy of negative pressure wound therapy (NPWT) in addressing refractory foot ulcers. The authors developed a temporary tension suture (TTS) in combination with NPWT to shorten the treatment duration of refractory foot ulcers, providing additional appropriate tension for wound coverage. The authors report for the first time a case of a 73-year-old female patient who had been experiencing an intractable hindfoot ulcer with chronic osteomyelitis for 18 months. NPWT and TTSs were applied concurrently with favorable outcomes.

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.

The heel is the second most common area for pressure injuries, which can lead to serious and threatening extremity infections. The late stages of deep hindfoot ulcers, where osteomyelitis commonly coexists, can result in an extended therapeutic window. Standard treatments for hindfoot ulcers complicated by osteomyelitis encompass debridement and flap surgery. In severe cases, below-knee amputation is also considered. Recent studies have reported the efficacy of negative pressure wound therapy (NPWT) in addressing refractory foot ulcers. The authors developed a temporary tension suture (TTS) in combination with NPWT to shorten the treatment duration of refractory foot ulcers, providing additional appropriate tension for wound coverage. The authors report for the first time a case of a 73-year-old female patient who had been experiencing an intractable hindfoot ulcer with chronic osteomyelitis for 18 months. NPWT and TTSs were applied concurrently with favorable outcomes.

The heel is the second most common area for pressure injuries, which can lead to serious and threatening extremity infections. The late stages of deep hindfoot ulcers, where osteomyelitis commonly coexists, can result in an extended therapeutic window. Standard treatments for hindfoot ulcers complicated by osteomyelitis encompass debridement and flap surgery. In severe cases, below-knee amputation is also considered. Recent studies have reported the efficacy of negative pressure wound therapy (NPWT) in addressing refractory foot ulcers. The authors developed a temporary tension suture (TTS) in combination with NPWT to shorten the treatment duration of refractory foot ulcers, providing additional appropriate tension for wound coverage. The authors report for the first time a case of a 73-year-old female patient who had been experiencing an intractable hindfoot ulcer with chronic osteomyelitis for 18 months. NPWT and TTSs were applied concurrently with favorable outcomes.

The heel is the second most common area for pressure injuries, which can lead to serious and threatening extremity infections. The late stages of deep hindfoot ulcers, where osteomyelitis commonly coexists, can result in an extended therapeutic window. Standard treatments for hindfoot ulcers complicated by osteomyelitis encompass debridement and flap surgery. In severe cases, below-knee amputation is also considered. Recent studies have reported the efficacy of negative pressure wound therapy (NPWT) in addressing refractory foot ulcers. The authors developed a temporary tension suture (TTS) in combination with NPWT to shorten the treatment duration of refractory foot ulcers, providing additional appropriate tension for wound coverage. The authors report for the first time a case of a 73-year-old female patient who had been experiencing an intractable hindfoot ulcer with chronic osteomyelitis for 18 months. NPWT and TTSs were applied concurrently with favorable outcomes.

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.

Multiple myeloma (MM), a hematological malignancy, is characterized by malignant plasma cell proliferation in the bone marrow. Recent treatment advances have significantly improved patient outcomes associated with MM.In this study, we aimed to develop comprehensive, evidence-based guidelines for the diagnosis, prognosis, and treat‑ ment of MM. We identified 12 key clinical questions essential for MM management, guiding the extensive literature review and meta-analysis of the study. Our guidelines provide evidence-based recommendations by integrating patient preferences with survey data. These recommendations include current and emerging diagnostic tools, thera‑ peutic agents, and treatment strategies. By prioritizing a patient-centered approach and rigorous data analysis, these guidelines were developed to enhance MM management, both in Korea and globally.