Purpose: Due to chronic toxicity, tacrolimus-sparing is an important issue in kidney transplant recipients (KTRs). Several studies have shown that bisphosphonate use is associated with favorable graft outcomes in KTRs. We investigated whether the association between tacrolimus trough levels (TTLs) and graft outcomes differed according to bisphosphonate use in KTRs. Materials and Methods: We conducted a retrospective study encompassing 1441 KTRs who were administered tacrolimus-based immunosuppressants. The primary exposure was a time-dependent cross-product of TTLs (low TTLs vs. normal-high TTLs with a reference of 6 ng/mL) and bisphosphonate use. Two primary outcomes were evaluated: overall graft loss (death or conversion to kidney replacement) and an estimated glomerular filtration rate (eGFR) <30 mL/min/1.73 m 2 . Results: During the median follow-up of 6.1 (3.4–9.7) years, overall graft loss occurred in 157 (10.9%) patients. Cox regression revealed that normal-high TTLs without bisphosphonate use were associated with a reduced risk of overall graft loss [adjusted hazard ratio (aHR), 0.65; 95% confidence interval (CI), 0.45–0.95] compared to low TTLs without bisphosphonate use. The use of bisphosphonate in conjunction with normal-high TTLs correlated with an even lower risk of overall graft loss (aHR, 0.25; 95% CI, 0.08–0.80) compared with low TTLs without bisphosphonate use. In patients with low TTLs, bisphosphonate use was associated with a reduced risk of overall graft loss compared with non-use (aHR, 0.20; 95% CI, 0.09–0.43). Similar trends were observed in the eGFR outcome. Conclusion The use of bisphosphonate was associated with favorable graft outcomes, even with low TTLs. Incorporating bisphosphonate into a conventional immunosuppressant regimen may potentially reduce tacrolimus requirement.

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.

Background: Tumor-induced osteomalacia (TIO) is a rare paraneoplastic syndrome characterized by hypophosphatemia and osteomalacia, resulting from excessive production of fibroblast growth factor 23 by mesenchymal tumors. Although various imaging techniques are used to localize the tumor, the rarity of the disease poses significant challenges in tumor detection. Methods: After retrospectively analyzing 25 TIO patients who visited the Severance bone and mineral clinic between 2004 and 2024, we added all reported TIO cases in Korea by searching PubMed, Embase, and Web of Science. Using the PRISMA method, an additional 14 reports comprising 22 patients were included. Results: Among the 47 patients, 25 (53.2%) were male, with a median age of 52 years. Tumors were most commonly located in the lower extremities (57.5%), with a median size of 1.9 cm. Surgical resection, including radiofrequency ablation, was performed in 45 patients; reoperation was required in 26.7% (12/45) due to residual tumors. After gallium scans were covered by insurance, 83.3% (25/30) of patients underwent gallium-based imaging for tumor localization with 77.3% of tumors successfully localized in initial scan. Of the five patients with negative initial scans, four (80%) showed positive findings on a second scan conducted 1 to 2 years later. Conclusions Although gallium scans are the most commonly employed imaging modality for detecting tumors responsible for TIO, localization remains challenging due to the small tumor size, potential for widespread anatomical distribution, and prolonged diagnostic delay. Repeating gallium imaging can yield positive results in previously negative cases, and additional imaging modalities may be necessary to facilitate accurate localization.

Background: This review explores the discriminative ability of fracture risk assessment tool (FRAX) in major osteoporotic fracture (MOF) and hip fracture (HF) risk prediction and the densitometric diagnosis of osteoporosis in Asian populations. Methods: We systematically searched the EMBASE, Cochrane, and PubMed databases from the earliest indexing date to January 2024. Studies were included if FRAX was used to identify future osteoporotic fractures or a densitometric diagnosis of osteoporosis in an Asian population and reported the area under the curve (AUC) values. Meta-analyses were conducted after quality assessment for AUC with 95% confidence intervals across the following categories: standard FRAX without/with bone mineral density (BMD), adjusted FRAX, and BMD alone for fracture prediction, as well as standard FRAX for densitometric diagnosis of osteoporosis. Results: A total of 42 studies were included. The AUC values for predicting fracture risk using FRAX-MOF with BMD (0.73 [0.70–0.77]) was highest compared to FRAX-MOF without BMD (0.72 [0.66–0.77]), and adjusted FRAX-MOF (0.71 [0.65–0.77]). The AUC values for predicting fracture risk using FRAX-HF with BMD (0.77 [0.71–0.83]) was highest compared to FRAX-HF without BMD (0.72 [0.65–0.80]), and adjusted FRAX-HF (0.75 [0.63–0.86]). The AUC values for BMD alone (0.68 [0.62–0.73]) was lowest for fracture prediction. The AUC values for identifying a densitometric diagnosis of osteoporosis was 0.77 [0.70–0.84] and 0.76 [0.67-0.86] using FRAX-MOF and FRAX-HF, respectively. Conclusions FRAX with BMD tends to perform more reliably in predicting HF compared to MOF in Asia. However, its accuracy in predicting fracture risk in Asian populations can be improved through region-specific, long-term epidemiological data.

Background: This review explores the discriminative ability of fracture risk assessment tool (FRAX) in major osteoporotic fracture (MOF) and hip fracture (HF) risk prediction and the densitometric diagnosis of osteoporosis in Asian populations. Methods: We systematically searched the EMBASE, Cochrane, and PubMed databases from the earliest indexing date to January 2024. Studies were included if FRAX was used to identify future osteoporotic fractures or a densitometric diagnosis of osteoporosis in an Asian population and reported the area under the curve (AUC) values. Meta-analyses were conducted after quality assessment for AUC with 95% confidence intervals across the following categories: standard FRAX without/with bone mineral density (BMD), adjusted FRAX, and BMD alone for fracture prediction, as well as standard FRAX for densitometric diagnosis of osteoporosis. Results: A total of 42 studies were included. The AUC values for predicting fracture risk using FRAX-MOF with BMD (0.73 [0.70–0.77]) was highest compared to FRAX-MOF without BMD (0.72 [0.66–0.77]), and adjusted FRAX-MOF (0.71 [0.65–0.77]). The AUC values for predicting fracture risk using FRAX-HF with BMD (0.77 [0.71–0.83]) was highest compared to FRAX-HF without BMD (0.72 [0.65–0.80]), and adjusted FRAX-HF (0.75 [0.63–0.86]). The AUC values for BMD alone (0.68 [0.62–0.73]) was lowest for fracture prediction. The AUC values for identifying a densitometric diagnosis of osteoporosis was 0.77 [0.70–0.84] and 0.76 [0.67-0.86] using FRAX-MOF and FRAX-HF, respectively. Conclusions FRAX with BMD tends to perform more reliably in predicting HF compared to MOF in Asia. However, its accuracy in predicting fracture risk in Asian populations can be improved through region-specific, long-term epidemiological data.