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.

Objective:To analyze the accuracy of lung ultrasound and chest X-ray in the diagnosis of neonatal pulmonary disease.Methods:We prospectively collected newborns that needed chest X-ray examination to diagnose pulmonary disease from twelve neonatal intensive care units across the country between June 2019 and April 2020.Each newborn was examined by lung ultrasound within two hours after chest X-ray examination.All chest X-ray and lung ultrasound images were independently read by a radiologist and a sonographer.When there was a disagreement, a panel of two experienced physicians made a final diagnosis based on the clinical history, chest X-ray and lung ultrasound images.Results:A total of 1 100 newborns were enrolled in our study.The diagnostic agreement between chest X-ray and lung ultrasound(Cohen′s kappa coefficient=0.347) was fair.Lung ultrasound(area under the curve=0.778; 95% CI 0.753-0.803) performed significantly better than chest X-ray(area under the curve=0.513; 95% CI 0.483-0.543) in the diagnosis of transient tachypnea of the newborn( P<0.001). The accuracy of lung ultrasound in diagnosing neonatal respiratory distress syndrome, meconium aspiration syndrome, pneumonia and neonatal pulmonary atelectasis was similar to that of chest X-ray. Conclusion:Lung ultrasound, as a low-cost, simple and radiation-free auxiliary examination method, has a diagnostic accuracy close to or even better than that of chest X-ray, which may replace chest X-ray in the diagnosis of some neonatal lung diseases.It should be noted that both chest X-ray and lung ultrasound can only be used as auxiliary means for the diagnosis of lung diseases, and it is necessary to combine imaging with the clinical history and presentation.

As a leading cause of chronic kidney disease, clinical demand for noninvasive biomarkers of diabetic kidney disease (DKD) beyond proteinuria is increasing. Metabolomics is a popular method to identify mechanisms and biomarkers. We investigated urinary targeted metabolomics in DKD patients. Methods: We conducted a targeted metabolomics study of 26 urinary metabolites in consecutive patients with DKD stage 1 to 5 (n = 208) and healthy controls (n = 26). The relationships between estimated glomerular filtration rate (eGFR) or urine protein-creatinine ratio (UPCR) and metabolites were evaluated. Multivariate Cox analysis was used to estimate relationships between urinary metabolites and the target outcome, end-stage renal disease (ESRD). C statistics and time-dependent receiver operating characteristics (ROC) were used to assess diagnostic validity. Results: During a median 4.5 years of follow-up, 103 patients (44.0%) progressed to ESRD and 65 (27.8%) died. The median fold changes of nine metabolites belonged to monosaccharide and tricarboxylic acid (TCA) cycle metabolites tended to increase with DKD stage. Myo-inositol, choline, and citrates were correlated with eGFR and choline, while mannose and myo-inositol were correlated with UPCR. Elevated urinary monosaccharide and TCA cycle metabolites showed associations with increased morality and ESRD progression. The predictive power of ESRD progression was high, in the order of choline, myo-inositol, and citrate. Although urinary metabolites alone were less predictive than serum creatinine or UPCR, myo-inositol had additive effect with serum creatinine and UPCR. In time-dependent ROC, myo-inositol was more predictive than UPCR of 1-year ESRD progression prediction. Conclusion: Myo-inositol can be used as an additive biomarker of ESRD progression in DKD.