Objective To investigate the clinical outcomes of an invasive strategy for elderly (aged≥75 years) patients with acute ST-segment elevation myocardial infarction (STEMI) complicated by cardiogenic shock (CS). Methods Data on 366 of 409 elderly CS patients from a total of 6,132 acute STEMI cases enrolled in the Korea Acute Myocardial Infarction Registry between January 2008 and June 2011, were collected and analyzed. In-hospital deaths and the 1-month and 1-year survival rates free from major adverse cardiac events (MACE;defined as all cause death, myocardial infarction, and target vessel revascularization) were reported for the patients who had undergone invasive (n=310) and conservative (n=56) treatment strategies. Results The baseline clinical characteristics were not significantly different between the two groups. There were fewer in-hospital deaths in the invasive treatment strategy group (23.5%vs. 46.4%, P＜0.001). In addition, the 1-year MACE-free survival rate after invasive treatment was significantly lower compared with the conservative treatment (51%vs. 66%, P=0.001). Conclusions In elderly patients with acute STEMI complicated by CS, the outcomes of invasive strategy are similar to those in younger patients at the 1-year follow-up.

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.

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.

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.

The most common limiting factor in arterio-venous fistula operation (AVF) is the lack of a suitable native vein. So preoperative assessment of venous system is essential for successful results. But simple physical examination alone is not enough to assess in many cases. To evaluate difference between venogram results with physical examination, 20 patients (group A) imaged with venogram preoperatively compared with 20 patients (group B) who examined only physically. In group A, all patients palpated thrill in immediate post operation and one patient revealed fistula occlusion within post operative 2 months. In group B 4 patients revealed failed fistula within 24 hours and 5 fistula failed in 2 months. Twelve patients (>50%) of group A showed different results in venogram compared with physical examination, which influenced type of operation. Physical examination alone was not enough to assess venous system and venogram provided valuable information in AVF constructive surgery.
Fistula ; Humans ; Physical Examination ; Veins

PURPOSE: Primary gastrointestinal lymphoma is the most common form of extranodal lymphoma. The clinical features, histological distributions, treatment results and prognosis of the primary intestinal lymphoma were evaluated. METHODS: A retrospective study was performed on 62 patients with primary intestinal lymphoma, as defined by Lewin's criteria, from May 1990 to February 2002. The WHO classification and Ann Arbor staging system were used for histological classification and staging, respectively. RESULTS: The sex ratio of the patients was 43: 19 (male: female), and the median age was 54 years. Abdominal pain, a palpable mass, and bleeding were the most frequent symptoms on presentation. The ileocecal area was the most frequent pathological site. Fifty-three cases were non- Hodgkin's lymphoma of B-cell origination; all of the remaining were T-cell originated. The mean survival period of B-cell and T-cell originated were 59.3 and 14.3 months, respectively (P<0.05). The 5 year survival rates of the patients in stage IE and IIE, and stage IIIE and IVE, were 52.4 and 32.6%, respectively (P=0.03). Six patients received surgery, 17 chemotherapy, and 39 surgery with adjuvant chemotherapy. Among the patients confined to stage IE and IIE, the 3 year survival rates of the surgery and surgery with adjuvant chemotherapy groups were 34 and 84%, respectively (P=0.0049). CONCLUSION: Primary gastrointestinal lymphoma of B-cell origination was predominant in relation to the WHO classification and revealed a better prognosis when compared to the T-cell originated lymphoma. For the patients with localized intestinal lymphoma, multimodality treatment (surgery with adjuvant chemotherapy) is preferred to the sole administration of chemotherapy.
Abdominal Pain ; B-Lymphocytes ; Chemotherapy, Adjuvant ; Classification ; Drug Therapy ; Hemorrhage ; Hodgkin Disease ; Humans ; Lymphoma* ; Lymphoma, Non-Hodgkin ; Prognosis ; Retrospective Studies ; Sex Ratio ; Survival Rate ; T-Lymphocytes

PURPOSE: ABO incompatibility had long been an obstacle in kidney transplantation. However, recent reports showed excellent outcomes. In this study, we evaluated the outcomes of ABO incompatible kidney transplantation with preconditioning protocol using rituximab and plasmapheresis. METHODS: The recipients who had an ABO-incompatible donor and underwent living donor kidney transplantation were enrolled. Preconditioning protocol was pretransplant single dose rituximab with plasmapheresis at pretransplantation 7-10 days. Immune suppression regimen consisted of tacrolimus, mycophenolate mofetil and steroid. Anti-A or anti-B antibody titer was monitored during preconditioning and post transplantation period. RESULTS: 37 patients underwent living donor ABO incompatible kidney transplantation. Median pre-treatment antibody titer was 1:64 and pre transplant antibody titer after 1-6 times of plasmapheresis was 1:2. Median follow-up duration was 332 days (range 156-681). One episode of acute T cell mediated rejection was observed. Mean serum creatinine at 2 weeks was 1.00+/-0.27 mg/dL and at 24 weeks was 1.21+/-0.37 mg/dL. CONCLUSION: ABO incompatible kidney transplantation with rituximab and plasmapheresis can be safely performed. It is therefore a valuable option for expanding donor pool and should be actively performed in Korea.
Antibodies, Monoclonal, Murine-Derived ; Creatinine ; Follow-Up Studies ; Humans ; Kidney ; Kidney Transplantation ; Korea ; Living Donors ; Mycophenolic Acid ; Plasmapheresis ; Rejection (Psychology) ; Tacrolimus ; Tissue Donors ; Transplants ; Rituximab

Congenital laryngeal atresia is a rare cause of airway obstruction that is almost always lethal within short period of time after birth unless diagnosed prenatally and emergency tracheostomy was performed. Other life-threatening anomalies such as tracheoesophageal fistula, gastrointestinal or urinary anomalies, and VATER syndrome are often associated with laryngeal atresia. Recently, we experienced a case of congenital laryngeal atresia with diaphragmatic hernia, ear and skull anomalies, not diagnosed prenatally, died of asphyxia due to intubation failure, and confirmed by autopsy. We report this case with a brief review of the literatures.
Airway Obstruction ; Asphyxia ; Autopsy ; Ear ; Emergencies ; Hernia, Diaphragmatic ; Intubation ; Parturition ; Skull ; Tracheoesophageal Fistula ; Tracheostomy