Background: Flow cytometric immunophenotyping of hematolymphoid neoplasms (FCIHLN) is essential for diagnosis, classification, and minimal residual disease (MRD) monitoring. FCI-HLN is typically performed using in-house protocols, raising the need for standardization. Therefore, we surveyed the current status of FCI-HLN in Korea to obtain fundamental data for quality improvement and standardization. Methods: Eight university hospitals actively conducting FCI-HLN participated in our survey.We analyzed responses to a questionnaire that included inquiries regarding test items, reagent antibodies (RAs), fluorophores, sample amounts (SAs), reagent antibody amounts (RAAs), acquisition cell number (ACN), isotype control (IC) usage, positiveegative criteria, and reporting. Results: Most hospitals used acute HLN, chronic HLN, plasma cell neoplasm (PCN), and MRD panels. The numbers of RAs were heterogeneous, with a maximum of 32, 26, 12, 14, and 10 antibodies used for acute HLN, chronic HLN, PCN, ALL-MRD, and multiple myeloma-MRD, respectively. The number of fluorophores ranged from 4 to 10. RAs, SAs, RAAs, and ACN were diverse. Most hospitals used a positive criterion of 20%, whereas one used 10% for acute and chronic HLN panels. Five hospitals used ICs for the negative criterion. Positiveegative assignments, percentages, and general opinions were commonly reported. In MRD reporting, the limit of detection and lower limit of quantification were included. Conclusions This is the first comprehensive study on the current status of FCI-HLN in Korea, confirming the high heterogeneity and complexity of FCI-HLN practices. Standardization of FCI-HLN is urgently needed. The findings provide a reference for establishing standard FCI-HLN guidelines.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Background and Objectives: The number of people with heart failure (HF) is increasing worldwide, and the social burden is increasing as HF has high mortality and morbidity. We aimed to provide updated trends on the epidemiology of HF in Korea to shape future social measures against HF. Methods: We used the National Health Information Database of the National Health Insurance Service to determine the prevalence, incidence, hospitalization rate, mortality rate, comorbidities, in-hospital mortality, and healthcare cost of patients with HF from 2002 to 2020 in Korea. Results: The prevalence of HF in the total Korean population rose from 0.77% in 2002 to 2.58% (1,326,886 people) in 2020. Although the age-standardized incidence of HF decreased over the past 18 years, the age-standardized prevalence increased. In 2020, the hospitalization rate for any cause in patients with HF was 1,166 per 100,000 persons, with a steady increase from 2002. In 2002, the HF mortality was 3.0 per 100,000 persons, which rose to 15.6 per 100,000 persons in 2020. While hospitalization rates and in-hospital mortality for patients with HF increased, the mortality rate for patients with HF did not (5.8% in 2020), and the one-year survival rate from the first diagnosis of HF improved. The total healthcare costs for patients with HF were approximately $2.4 billion in 2020, a 16-fold increase over the $0.15 billion in 2002. Conclusions The study’s results underscore the growing socioeconomic burden of HF in Korea, driven by an aging population and increasing HF prevalence.

Polyarteritis nodosa (PAN) is a systemic necrotizing vasculitis predominantly involving medium- or small-sized arteries, typically of the kidneys and other internal organs. Given the rarity of PAN and the variable clinical presentation, diagnosis is challenging and, to date, no definitive diagnostic marker has been identified. A patient diagnosed with immunoglobulin A nephropathy was observed to exhibit deterioration in renal function. To determine whether new structural abnormalities had developed, computed tomography scans of the kidneys, ureters, and bladder were obtained. Both kidneys exhibited multiple cortical defects, and a renal angiogram was performed to determine the cause. Angiography revealed partial obliteration of the left distal renal artery branches and multifocal extensive infarctions in both kidneys, and the patient was diagnosed with renal-limited PAN. Following steroid monotherapy, an improvement in renal function was observed. We believe that this case report may be helpful to physicians who assess and treat patients with suspected renal-limited PAN.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Polyarteritis nodosa (PAN) is a systemic necrotizing vasculitis predominantly involving medium- or small-sized arteries, typically of the kidneys and other internal organs. Given the rarity of PAN and the variable clinical presentation, diagnosis is challenging and, to date, no definitive diagnostic marker has been identified. A patient diagnosed with immunoglobulin A nephropathy was observed to exhibit deterioration in renal function. To determine whether new structural abnormalities had developed, computed tomography scans of the kidneys, ureters, and bladder were obtained. Both kidneys exhibited multiple cortical defects, and a renal angiogram was performed to determine the cause. Angiography revealed partial obliteration of the left distal renal artery branches and multifocal extensive infarctions in both kidneys, and the patient was diagnosed with renal-limited PAN. Following steroid monotherapy, an improvement in renal function was observed. We believe that this case report may be helpful to physicians who assess and treat patients with suspected renal-limited PAN.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Polyarteritis nodosa (PAN) is a systemic necrotizing vasculitis predominantly involving medium- or small-sized arteries, typically of the kidneys and other internal organs. Given the rarity of PAN and the variable clinical presentation, diagnosis is challenging and, to date, no definitive diagnostic marker has been identified. A patient diagnosed with immunoglobulin A nephropathy was observed to exhibit deterioration in renal function. To determine whether new structural abnormalities had developed, computed tomography scans of the kidneys, ureters, and bladder were obtained. Both kidneys exhibited multiple cortical defects, and a renal angiogram was performed to determine the cause. Angiography revealed partial obliteration of the left distal renal artery branches and multifocal extensive infarctions in both kidneys, and the patient was diagnosed with renal-limited PAN. Following steroid monotherapy, an improvement in renal function was observed. We believe that this case report may be helpful to physicians who assess and treat patients with suspected renal-limited PAN.

Background: The US Food and Drug Administration (FDA) and European Medicines Agency (EMA) approved empagliflozin for reducing cardiovascular mortality and heart failure (HF) hospitalization in patients with both HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF). However, limited data are available on the generalizability of empagliflozin to clinical practice. Therefore, we evaluated real-world eligibility and potential cost-effectiveness based on a nationwide prospective HF registry. Methods: A total of 3,108 HFrEF and 2,070 HFpEF patients from the Korean Acute Heart Failure (KorAHF) registry were analyzed. Eligibility was estimated by inclusion and exclusion criteria of EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Reduced Ejection Fraction (EMPEROR-Reduced) and EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Preserved Ejection Fraction (EMPEROR-Preserved) trials and by FDA & EMA label criteria. The cost-utility analysis was done using a Markov model to project the lifetime medical cost and quality-adjusted life year (QALY). Results: Among the KorAHF patients, 91.4% met FDA & EMA label criteria, while 44.7% met the clinical trial criteria. The incremental cost-effectiveness ratio of empagliflozin was calculated at US$6,764 per QALY in the overall population, which is far below a threshold of US$18,182 per QALY. The cost-effectiveness benefit was more evident in patients with HFrEF (US$5,012 per QALY) than HFpEF (US$8,971 per QALY). Conclusion There is a large discrepancy in real-world eligibility for empagliflozin between FDA & EMA labels and clinical trial criteria. Empagliflozin is cost-effective in HF patients regardless of ejection fraction in South Korea health care setting. The efficacy and safety of empagliflozin in real-world HF patients should be further investigated for a broader range of clinical applications.