Objective:To analyze the current status of red blood cell transfusion in very preterm infants (VPI) (gestational age at birth <32 weeks) from Chinese Neonatal Network (CHNN) in 2022.Methods:This cross-sectional study was based on the CHNN VPI cohort. It included 6 985 VPI admitted to CHNN 89 participating centers within 24 hours after birth in 2022. VPI with major congenital anomalies or those transferred to non-CHNN centers for treatment or discharged against medical advice were excluded. VPI were categorized based on whether they received red blood cell transfusions, their gestational age at birth, the type of respiratory support received during transfusion, and whether the pre-transfusion hemoglobin levels exceeded the thresholds. General characteristics, red blood cell transfusion rates, number of transfusions, timing of the first transfusion, and pre-transfusion hemoglobin levels were compared among different groups. The incidence of adverse outcomes between the group of VPI who received transfusions above the threshold and those who received transfusions below the threshold were compared. Comparison among different groups was conducted using χ2 tests, Kruskal-Wallis H tests, Mann-Whitney U test, and so on. Trends by gestational age at birth were evaluated by Cochran-Armitage tests and Jonckheere-Terpstra tests for trend. Results:Among the 6 985 VPI, 3 865 cases(55.3%) were male, with a gestational age at birth of 30.0 (28.6, 31.0) weeks and a birth weight of (1 302±321) g. Overall, 3 617 cases (51.8%) received red blood cell transfusion, while 3 368 cases (48.2%) did not. The red blood cell transfusion rate was 51.8% (3 617/6 985), with rates of 77.7% (893/1 150) for those born before 28 weeks gestational age and 46.7% (2 724/5 835) for those born between 28 and 31 weeks gestational age. A total of 9 616 times red blood cell transfusions were administered to 3 617 VPI, with 632 times missing pre-transfusion hemoglobin data, and 8 984 times included in the analysis. Of the red blood cell transfusions, 25.6% (2 459/9 616) were administered when invasive respiratory support was required, 51.3% (4 934/9 616) were receiving non-invasive respiratory support, while 23.1% (2 223/9, 616) were given when no respiratory support was needed. Compared to the non-transfusion group, the red blood cell transfusion group had a higher rate of pregnancy-induced hypertension in mothers, lower rates of born via cesarean section and mother′s antenatal steroid administration, smaller gestational age, lower birth weight, a higher proportion of small-for-gestational-age, multiple births, and proportions of Apgar score at the 5 th minute after birth ≤3 (all P<0.05). They were also less likely to be female, born in hospital or undergo delayed cord clamping (all P<0.01). Additionally, higher transport risk index of physiologic stability score at admission were observed in the red blood cell transfusion group ( P<0.001). The number of red blood cell transfusion was 2 (1, 3) times, with the first transfusion occurring at an age of 18 (8, 29) days, and a pre-transfusion hemoglobin level of 97 (86, 109) g/L. For VPI ≤7 days of age, the pre-transfusion hemoglobin levels for invasive respiratory support, non-invasive respiratory support, or no respiratory support, respectively, with no statistically significant differences between groups ( H=5.59, P=0.061). For VPI aged 8 to 21 days and≥22 days, the levels with statistically differences between groups (both P<0.01). Red blood cell transfusions above recommended thresholds were observed in all respiratory support categories at different stages of life, with the highest prevalence in infants aged 8 to 21 days and≥22 days who did not require respiratory support, at 90.1% (264/273) and 91.1%(1 578/1 732), respectively. The rate of necrotizing enterocolitis was higher in the above-threshold group ( χ2=10.59, P=0.001), and the duration of hospital stay was longer in the above-threshold group ( Z=4.67, P<0.001) compared to the below-threshold group. Conclusions:In 2022, the red blood cell transfusion rate was relatively high among VPI from CHNN. Pre-transfusion hemoglobin levels frequently exceeded recommended transfusion thresholds.

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.

Analyzing polysorbate 20(PS20)composition and the impact of each component on stability and safety is crucial due to formulation variations and individual tolerance.The similar structures and polarities of PS20 components make accurate separation,identification,and quantification challenging.In this work,a high-resolution quantitative method was developed using single-dimensional high-performance liquid chromatography(HPLC)with charged aerosol detection(CAD)to separate 18 key components with multiple esters.The separated components were characterized by ultra-high-performance liquid chro-matography-quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS)with an identical gradient as the HPLC-CAD analysis.The polysorbate compound database and library were expanded over 7-time compared to the commercial database.The method investigated differences in PS20 samples from various origins and grades for different dosage forms to evaluate the composition-process relationship.UHPLC-Q-TOF-MS identified 1329 to 1511 compounds in 4 batches of PS20 from different sources.The method observed the impact of 4 degradation conditions on peak components,identifying stable components and their tendencies to change.HPLC-CAD and UHPLC-Q-TOF-MS results provided insights into fingerprint differences,distinguishing quasi products.

Objectives: This study aimed to investigate the risk of osteoarthritis associated with menopausal hormone therapy (MHT). Methods: This population-based retrospective cohort study used a database of Korean health insurance claims (2007–2020). Females aged ≥ 40 who initiated menopause-related healthcare visits between 2011 and 2014 were identified. The MHT group comprised females aged ≥ 40 who initiated MHT for ≥ 6 months during this period. The non-MHT group comprised females aged ≥ 40 who attended menopause-related healthcare visits but did not receive MHT. To account for potential confounding factors, the two groups were matched at a 1:1 ratio using propensity score matching. Results: A cohort of 453,040 postmenopausal females aged ≥ 40 years was identified, with 26,354 assigned to either the MHT or nonMHT group after propensity matching. The median age was 49 years, and the median follow-up was 8.2 years. The Cox proportional hazards model demonstrated an elevated risk of osteoarthritis with MHT (hazard ratio [HR], 1.154; 95% confidence interval [CI], 1.117–1.193) for knee (HR, 1.148; 95% CI, 1.102–1.195) and other arthritis (HR, 1.205; 95% CI, 1.151–1.261), although not statistically significant for hip arthritis. Tibolone (HR, 1.211; 95% CI, 1.161–1.263), estrogen–progestogen therapy (EPT) (HR, 1.092; 95% CI, 1.048– 1.137), and estrogen therapy (ET) (HR, 1.235; 95% CI, 1.148–1.329) were associated with a higher risk of osteoarthritis compared to nonMHT users. Conclusions MHT was associated with an increased risk of osteoarthritis, consistently observed across tibolone, EPT, and ET, particularly affecting joints other than the hip, with a trend toward an elevated risk of hip osteoarthritis.

Background and Objectives: Pathophysiological changes of right ventricle (RV) after repair of tetralogy of Fallot (TOF) are coupled with a highly compliant low-pressure pulmonary artery (PA) system. This study aimed to determine whether pulmonary vascular function was associated with RV parameters and exercise capacity, and its impact on RV remodeling after pulmonary valve replacement. Methods: In a total of 48 patients over 18 years of age with repaired TOF, pulmonary arterial elastance (Ea), RV volume data, and RV-PA coupling ratio were calculated and analyzed in relation to exercise capacity. Results: Patients with a low Ea showed a more severe pulmonary regurgitation volume index, greater RV end-diastolic volume index, and greater effective RV stroke volume (p=0.039, p=0.013, and p=0.011, respectively). Patients with a high Ea had lower exercise capacity than those with a low Ea (peak oxygen consumption [peak VO2 ] rate: 25.8±7.7 vs. 34.3±5.5 mL/kg/min, respectively, p=0.003), while peak VO2 was inversely correlated with Ea and mean PA pressure (p=0.004 and p=0.004, respectively). In the univariate analysis, a higher preoperative RV end-diastolic volume index and RV end-systolic volume index, left ventricular end-systolic volume index, and higher RV-PA coupling ratio were risk factors for suboptimal outcomes. Preoperative RV volume and RV-PA coupling ratio reflecting the adaptive PA system response are important factors in optimal postoperative results. Conclusions We found that PA vascular dysfunction, presenting as elevated Ea in TOF, may contribute to exercise intolerance. However, Ea was inversely correlated with pulmonary regurgitation (PR) severity, which may prevent PR, RV dilatation, and left ventricular dilatation in the absence of significant pulmonary stenosis.

INTRODUCTION: Singapore instituted lockdown measures from 7 February 2020 to 1 June 2020 in response to the coronavirus disease 2019 (COVID-19) pandemic. METHODS: A retrospective analysis of cases from the national trauma registry was carried out comparing the lockdown period (from 7 February 2020 to 1 June 2020) to the pre-lockdown period (from 7 February 2019 to 1 June 2019). Data extracted included the volume of Tier 1 (injury severity score [ISS] >15) and Tier 2 (ISS 9-15) cases and epidemiology. Subgroup analysis was performed for Tier 1 patient outcomes. RESULTS: Trauma volume decreased by 19.5%, with a 32% drop in Tier 1 cases. Road traffic and workplace accidents decreased by 50% (P < 0.01), while interpersonal violence showed an increase of 37.5% (P = 0.34). There was an 18.1% decrease in usage of trauma workflows (P = 0.01), with an increase in time to intervention for Tier 1 patients from 88 to 124 min (P = 0.22). Discharge to community facilities decreased from 31.4% to 17.1% (P < 0.05). There was no increase in inpatient mortality, length of stay in critical care or length of stay overall. CONCLUSION There was an overall decrease in major trauma cases during the lockdown period, particularly road traffic accidents and worksite injuries, and a relative increase in interpersonal violence. Redeployment of manpower and hospital resources may have contributed to decreased usage of trauma workflows and community facilities. In the event of further lockdowns, it is necessary to plan for trauma coverage and maintain the use of workflows to facilitate early intervention.
Humans ; COVID-19/epidemiology* ; Trauma Centers ; Retrospective Studies ; Singapore/epidemiology* ; Workload ; Communicable Disease Control