Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Objective: To demonstrate the noninferiority of the novel hemostatic agent, Hemofence (BMI Korea Co., Ltd., thrombin cross-linked sodium hyaluronate gel matrix) compared to the established agent, Floseal Hemostatic Matrix (Baxter, thrombin-gelatin matrix) in achieving hemostasis for spinal surgeries, with secondary objectives to assess additional efficacy and safety. Methods: This clinical trial was a multicenter, randomized, subject-blinded, active-controlled, parallel-group, phase 3 study. Investigational drugs were administered to the first and second bleeding sites of each participant (or only to the first site if a second site was absent), evaluating hemostasis success rate within 10 minutes and the time to achieve hemostasis. Subsequent visits were conducted for safety assessments. For noninferiority test, a 97.5% one-sided confidence interval (CI) was used; the test group was deemed noninferior if the lower limit exceeded -10%. Results: This trial showed a 97.10% success rate in the test group and 96.05% in the control group for primary efficacy. The 95% CI (-4.90% to 7.44%) confirmed the test drug’s noninferiority. Time to hemostasis showed no significant difference between groups. All adverse events, adverse drug reactions, and serious adverse events were statistically similar between groups (p=1.000, p=0.243, and p=0.966, respectively). Conclusion A novel hemostatic agent, Hemofence, demonstrated an efficacy and safety profile comparable to that of Floseal.

Objective: To demonstrate the noninferiority of the novel hemostatic agent, Hemofence (BMI Korea Co., Ltd., thrombin cross-linked sodium hyaluronate gel matrix) compared to the established agent, Floseal Hemostatic Matrix (Baxter, thrombin-gelatin matrix) in achieving hemostasis for spinal surgeries, with secondary objectives to assess additional efficacy and safety. Methods: This clinical trial was a multicenter, randomized, subject-blinded, active-controlled, parallel-group, phase 3 study. Investigational drugs were administered to the first and second bleeding sites of each participant (or only to the first site if a second site was absent), evaluating hemostasis success rate within 10 minutes and the time to achieve hemostasis. Subsequent visits were conducted for safety assessments. For noninferiority test, a 97.5% one-sided confidence interval (CI) was used; the test group was deemed noninferior if the lower limit exceeded -10%. Results: This trial showed a 97.10% success rate in the test group and 96.05% in the control group for primary efficacy. The 95% CI (-4.90% to 7.44%) confirmed the test drug’s noninferiority. Time to hemostasis showed no significant difference between groups. All adverse events, adverse drug reactions, and serious adverse events were statistically similar between groups (p=1.000, p=0.243, and p=0.966, respectively). Conclusion A novel hemostatic agent, Hemofence, demonstrated an efficacy and safety profile comparable to that of Floseal.

Background: Osteoporosis and fragility fractures are crucial musculoskeletal complications in long-term survivors of gastric cancer. However, the relationship between changes in body composition after gastrectomy and bone loss has not been investigated. Therefore, this study aimed to explore whether computed tomography (CT)-derived body composition parameters are associated with bone loss after gastrectomy in patients with gastric cancer. Methods: We retrospectively reviewed medical records and abdomen CT scans of patients who underwent gastrectomy at Yonsei University Severance Hospital between 2009 and 2018. Patients with non-metastatic gastric adenocarcinoma and preoperative and postoperative non-contrast CT scans were analyzed. Section area of skeletal muscle (SMA), visceral fat (VFA), and subcutaneous fat (SFA) were assessed using semi-automatic segmentation software. Changes in trabecular bone attenuation of L1 mid-vertebra level (L1 Hounsfield units [HU]) were measured. Results: Fifty-seven patients (mean age, 65.5±10.6; 70.2% males) were analyzed, and the median duration was 31 months. Fortyseven patients (82.5%) lost weight after gastrectomy. Baseline SMA and VFA did not differ between the bone loss and preserved groups; however, baseline SFA was significantly higher in the bone preserved group than in the bone loss group (P=0.020). In a multivariable linear regression model adjusted for confounding factors, one standard deviation higher VFA at baseline was associated with greater annualized L1 HU loss (%) (P=0.034). However, higher preoperative SFA was associated with protection against bone loss after gastrectomy (P=0.025). Conclusion Higher preoperative SFA exhibited a protective effect against bone loss after gastrectomy in patients with non-metastatic gastric cancer, whereas VFA exhibited a negative effect.

The integrity of the high cervical spine, the transition zone from the brainstem to the spinal cord, is crucial for survival and daily life. The region protects the enclosed neurovascular structure and allows a substantial portion of the head motion. Injuries of the high cervical spine are frequent, and the fractures of the C2 vertebra account for approximately 17–25% of acute cervical fractures. We review the two major types of C2 vertebral fractures, odontoid fracture and Hangman’s fracture. For both types of fractures, favorable outcomes could be obtained if the delicately selected conservative treatment is performed. In odontoid fractures, as the most common fracture on the C2 vertebrae, anterior screw fixation is considered first for type II fractures, and C1–2 fusion is suggested when nonunion is a concern or occurs. Hangman's fractures are the second most common fracture. Many stable extension type I and II fractures can be treated with external immobilization, whereas the predominant flexion type IIA and III fractures require surgical stabilization. No result proves that either anterior or posterior surgery is superior, and the surgeon should decide on the surgical method after careful consideration according to each clinical situation. This review will briefly describe the basic principles and current treatment concepts of C2 fractures.

Objective: To demonstrate the noninferiority of the novel hemostatic agent, Hemofence (BMI Korea Co., Ltd., thrombin cross-linked sodium hyaluronate gel matrix) compared to the established agent, Floseal Hemostatic Matrix (Baxter, thrombin-gelatin matrix) in achieving hemostasis for spinal surgeries, with secondary objectives to assess additional efficacy and safety. Methods: This clinical trial was a multicenter, randomized, subject-blinded, active-controlled, parallel-group, phase 3 study. Investigational drugs were administered to the first and second bleeding sites of each participant (or only to the first site if a second site was absent), evaluating hemostasis success rate within 10 minutes and the time to achieve hemostasis. Subsequent visits were conducted for safety assessments. For noninferiority test, a 97.5% one-sided confidence interval (CI) was used; the test group was deemed noninferior if the lower limit exceeded -10%. Results: This trial showed a 97.10% success rate in the test group and 96.05% in the control group for primary efficacy. The 95% CI (-4.90% to 7.44%) confirmed the test drug’s noninferiority. Time to hemostasis showed no significant difference between groups. All adverse events, adverse drug reactions, and serious adverse events were statistically similar between groups (p=1.000, p=0.243, and p=0.966, respectively). Conclusion A novel hemostatic agent, Hemofence, demonstrated an efficacy and safety profile comparable to that of Floseal.