BACKGROUND: Open surgical repair of abdominal aortic aneurysms was initiated by Dubost in 1952. Despite the rapid expansion of percutaneous endovascular repair, open surgical repair is still recognized for curative intent. We retrospectively analyzed surgical outcome, complications, and mortality-related factors for patients with abdominal aortic aneurysms over a 6 year period. MATERIAL AND METHOD: We analyzed 18 patients who underwent surgery for abdominal aortic aneurysms between March 2002 and March 2008. The indications for surgery were rupture, a maximal aortic diameter >60 mm, medically intractable hypertension, or pain. RESULT: The mean age was 66.6+/-9.3 years (range, 49~81 years). Twelve patients (66.7%) were males and 6 patients were females. Extension of the aneurysm superior to the renal artery existed in 6 patients (33.3%), and extension to the iliac artery existed in 13 patients (72.2%). Five patients (27.8%) had ruptured aortic aneurysms. The mean maximal diameter of the aorta was 72.2+/-12.9 mm (range, 58~109 mm). Surgery was performed by a midline laparotomy, and 6 patients underwent emergency surgery. The mean total ischemic time from aorta clamping to revascularization was 82+/-42 minutes (range, 35~180 minutes). The mortality rate was 16.7%; the mortality rate for patients with ruptured aneurysms was 60%, and the mortality rate for patients with unruptured aneurysms was 0%. The postoperative complications included one each of renal failure, femoral artery and vein occlusion, and wound infection. The patients who were discharged had a long-term survival of 34+/-26 months (range, 4~90 months). Rupture and emergency surgery had a statistically significant mortality-related factor (p<0.05). CONCLUSION: Emergency surgery for ruptured aortic aneurysms continues to have a high mortality, but unruptured cases are repaired with relative safety. Successfully operated patients had long-term survival. Even though endovascular aortic repair is the trend for abdominal aortic aneurysms, aggressive application should be determined with care. Experience and systemic support of each center is important in the treatment plan
Aneurysm ; Aneurysm, Ruptured ; Aorta ; Aorta, Abdominal ; Aortic Aneurysm, Abdominal ; Aortic Rupture ; Constriction ; Emergencies ; Female ; Femoral Artery ; Humans ; Hypertension ; Iliac Artery ; Laparotomy ; Male ; Postoperative Complications ; Renal Artery ; Renal Insufficiency ; Retrospective Studies ; Rupture ; Veins ; Wound Infection

Background/Aims: The first autologous peripheral blood stem cell transplantation (ASCT) in Korea was performed for a small-cell lung cancer patient at Asan Medical Center (AMC) in 1993. Recently, lymphoma and myeloma have been the main indications; there has been progress in the treatments for these lymphoid malignancies. We explored the real-world experience of ASCT for lymphoma and myeloma at AMC over a 25-year period. Methods: We used the AMC ASCT registry, which has collected ASCT data prospectively since January 1993. Data for Hodgkin lymphoma, non-Hodgkin lymphoma, and multiple myeloma patients were analyzed. Patients transplanted up to December 2018 were included to assess adequate survival data. The ASCT time period was divided arbitrarily into 1994-1999, 2000-2009, and 2010-2018. In cases of multiple myeloma, we analyzed the 1st ASCT data only. Results: Survival of these lymphoid malignancy patients after ASCT has progressively improved. The increase in survival may be related to advances in various medical skills supporting ASCT. However, overall survival has improved much more than progression-free survival. This suggests that better salvage therapies after ASCT failure have mainly affected the improvement in overall survival. The hematopoietic cell transplantation-specific comorbidity index could not be used as a survival indicator in this analysis. Conclusions This real-world experience study showed that the survival of lymphoid malignancy patients treated with ASCT has improved over the past 25 years.

Background/Aims: The incidence of multiple myeloma (MM) in Korea is rapidly increasing. The diagnostic criteria of MM have been updated and novel therapeutic agents are available. This study explored the features of MM patients registered at Asan Medical Center (AMC) and the outcomes over the past 15 years. Methods: Data were obtained from the AMC MM registry, which has been collecting the data of MM patients prospectively. The 774 MM patients included in our analysis were diagnosed from 2003, when thalidomide became available as a novel therapeutic agent, until April 2019. The 2-year survival rate of these patients was assessed. Patients were divided into two groups based on whether they were older or younger than 65 years, which is the cutoff age for the indication of autologous stem cell transplantation. Patients were also grouped according to the year of diagnosis: up to 2006, when bortezomib became available, and up to 2010, when the cost of lenalidomide was reimbursed. Results: Patients < 65 years of age had better prognostic features, including a better performance, less advanced disease stage, and fewer abnormalities in their fluorescent in-situ hybridization (FISH) analysis results. A comparison of our Korean patients with patients registered in the Myeloma Related Disorder Registry data of Australia and New Zealand, showed ethnic discrepancies. The median overall survival of all patients was 3.7 years, with a 5-year survival rate of 41.8% and a 10-year survival rate of 23.4%. Survival progressively improved in patients diagnosed later. Age, performance status, renal function, C-reactive protein level, lactate dehydrogenase level, and cytogenetic findings were identified as significant prognostic factors. Conclusions This real-world survey revealed the clinical features and survival rates of patients at a tertiary Korean Hospital who were diagnosed with MM at the beginning of 21st century.

Objectives: The aim of this study was to classify polysomnography (PSG)-based sleep disorders using actigraphy data using a convolutional neural network (CNN). Methods: Actigraphy data, PSG data, and diagnoses were obtained from 214 patients from a single-center sleep clinic. Patients diagnosed with circadian sleep disorders, narcolepsy, or periodic limb movement disorders were excluded. From the actigraphy data, three types of data were selected from the first 5 days, namely, sleep-wake status, activity count, and light exposure per epoch. The data were processed into a two-dimensional array with four instances, namely, 24-hour full-day data and data for 6, 8, and 10 hours timepoints after sleep onset, and then analyzed. Using a CNN, we attempted to classify the processed data into PSG-based diagnoses. Results: Overfitting of the training data was observed. The CNN showed near-perfect accuracy on the test data, but failed to classify the validation data (area under the curve: 24-hour full-day data: 0.6031, 6 hours after sleep onset: 0.5148, 8 hours: 0.6122, and 10 hours: 0.5769). Conclusions The lack and inaccuracy of data were responsible for the results. A higher sampling rate and additional ancillary data, such as PSG or heart rate variability data, are necessary for accurate classification. Additionally, alternative approaches to machine learning, such as transformers, should be considered in future studies.

Objectives: The aim of this study was to classify polysomnography (PSG)-based sleep disorders using actigraphy data using a convolutional neural network (CNN). Methods: Actigraphy data, PSG data, and diagnoses were obtained from 214 patients from a single-center sleep clinic. Patients diagnosed with circadian sleep disorders, narcolepsy, or periodic limb movement disorders were excluded. From the actigraphy data, three types of data were selected from the first 5 days, namely, sleep-wake status, activity count, and light exposure per epoch. The data were processed into a two-dimensional array with four instances, namely, 24-hour full-day data and data for 6, 8, and 10 hours timepoints after sleep onset, and then analyzed. Using a CNN, we attempted to classify the processed data into PSG-based diagnoses. Results: Overfitting of the training data was observed. The CNN showed near-perfect accuracy on the test data, but failed to classify the validation data (area under the curve: 24-hour full-day data: 0.6031, 6 hours after sleep onset: 0.5148, 8 hours: 0.6122, and 10 hours: 0.5769). Conclusions The lack and inaccuracy of data were responsible for the results. A higher sampling rate and additional ancillary data, such as PSG or heart rate variability data, are necessary for accurate classification. Additionally, alternative approaches to machine learning, such as transformers, should be considered in future studies.

Objectives: The aim of this study was to classify polysomnography (PSG)-based sleep disorders using actigraphy data using a convolutional neural network (CNN). Methods: Actigraphy data, PSG data, and diagnoses were obtained from 214 patients from a single-center sleep clinic. Patients diagnosed with circadian sleep disorders, narcolepsy, or periodic limb movement disorders were excluded. From the actigraphy data, three types of data were selected from the first 5 days, namely, sleep-wake status, activity count, and light exposure per epoch. The data were processed into a two-dimensional array with four instances, namely, 24-hour full-day data and data for 6, 8, and 10 hours timepoints after sleep onset, and then analyzed. Using a CNN, we attempted to classify the processed data into PSG-based diagnoses. Results: Overfitting of the training data was observed. The CNN showed near-perfect accuracy on the test data, but failed to classify the validation data (area under the curve: 24-hour full-day data: 0.6031, 6 hours after sleep onset: 0.5148, 8 hours: 0.6122, and 10 hours: 0.5769). Conclusions The lack and inaccuracy of data were responsible for the results. A higher sampling rate and additional ancillary data, such as PSG or heart rate variability data, are necessary for accurate classification. Additionally, alternative approaches to machine learning, such as transformers, should be considered in future studies.

Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.

Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.

Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.

Purpose: This study aims to evaluate a new method for the five times sit to stand test (FTSST), crucial for addressing frailty in an aging population. It utilizes a smart insole for plantar pressure analysis and a marker-less motion capture device for head height analysis. Materials and Methods: Thirty-five participants aged 50 years or older underwent FTSST assessment using three methods: manual measurement with a stopwatch (FTSST-M), plantar pressure analysis with smart insoles (FTSST-P), and head height analysis with a marker-less motion capture device (FTSST-H). Simultaneous measurements using three methods were done. Correlation between results of these methods were analyzed using intraclass correlation coefficient (ICC) and κ coefficient. Comprehensive clinical examinations were conducted with ethical approval. Results: Participants’ mean scores for FTSST-M, FTSST-P, and FTSST-H were 2.43±1.20, 2.43±1.29, and 2.37±1.31, respectively. Correlations of the times and corresponding scores between FTSST-P and FTSST-M, as well as FTSST-H and FTSST-M, exceeded 0.9 (ICC and κ coefficients, p<0.001). Using an FTSST score of 3 or less to indicate vulnerability, the κ value for vulnerability classification between two measurements was 0.886 (p<0.001). Conclusion This study showed strong correlation between FTSST results using smart insoles and marker-less motion capture, compared to conventional methods. These findings highlight the potential of these technologies for precise FTSST measurements, offering convenience and cost-effectiveness. Simultaneous use of these devices enables diverse analyses, enhancing our understanding of frailty.