Comparative Effectiveness and Safety Outcomes Between Denosumab and Bisphosphonate in South Korea.

Both bisphosphonates and denosumab are the mainstays of treatment for osteoporosis to prevent fractures. However, there are still few trials directly comparing the prevention of fractures and the safety of two drugs in the treatment of osteoporosis. We aimed to compare the efficacy and safety between denosumab and bisphosphonates using a nationwide claims database. The database was covered with ten million, 20% of the whole Korean population sampled by age and sex stratification of the Health Insurance Review and Assessment Service in South Korea. Among 228,367 subjects who were over 50 years of age and taking denosumab or bisphosphonate from Jan 2018 to April 2022, the analysis was performed on 91,460 subjects after 1: 1 propensity score matching. The primary outcome was treatment effectiveness; total fracture, major osteoporotic fracture, femur fracture, pelvic fracture, vertebral fracture, adverse drug reactions; acute kidney injury, chronic kidney disease, and atypical femoral fracture. Total fracture and osteoporotic major fracture, as the main outcomes of efficacy, were comparable in the denosumab and bisphosphonate group (HR 1.06, 95% CI 0.98-1.15, p=0.14; HR 1.13, 95% CI 0.97-1.32, p=0.12, respectively). Safety for acute kidney injury, chronic kidney disease, and atypical femoral fracture also did not show any differences between the two groups. In subgroup analysis according to ages, the denosumab group under 70 years of age had a significantly lower risk for occurrences of acute kidney injury compared to the bisphosphonate group under 70 years of age (HR 0.53, 95% CI 0.29-0.93, p=0.03). In real-world data reflecting clinical practice, denosumab, and bisphosphonate showed comparable effectiveness for total fracture and osteoporosis major fracture and safety for acute kidney injury, chronic kidney disease, and atypical femoral fracture.

This study compared the effectiveness and safety of denosumab and bisphosphonates, two primary treatments for osteoporosis, using a large South Korean nationwide claims database. Analysis of data from 91,460 individuals over 50 years old showed no significant difference in preventing fractures or in safety outcomes such as kidney injury and atypical femoral fractures between the two drugs. However, among patients under 70, denosumab was associated with a lower risk of acute kidney injury. Overall, both medications demonstrated similar effectiveness and safety in the real-world treatment of osteoporosis.

Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing.

Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using structured electronic medical record data, brain morphometry, and natural language processing. In total, 247 patients with a new depressive episode were included. TRD-predictive models were developed based on the combination of following parameters: selected tabular dataset features, independent components-map weightings from brain T1-weighted magnetic resonance imaging (MRI), and topic probabilities from clinical notes. All models applied the extreme gradient boosting (XGBoost) algorithm via five-fold cross-validation. The model using all data sources showed the highest area under the receiver operating characteristic of 0.794, followed by models that used combined brain MRI and structured data, brain MRI and clinical notes, clinical notes and structured data, brain MRI only, structured data only, and clinical notes only (0.770, 0.762, 0.728, 0.703, 0.684, and 0.569, respectively). Classifications of TRD were driven by several predictors, such as previous exposure to antidepressants and antihypertensive medications, sensorimotor network, default mode network, and somatic symptoms. Our findings suggest that a combination of clinical data with neuroimaging and natural language processing variables improves the prediction of TRD.

Multi-Institutional Collaborative Research Using Ophthalmic Medical Image Data Standardized by Radiology Common Data Model (R-CDM).

Observational Medical Outcome Partners - Common Data Model (OMOP-CDM) is an international standard model for standardizing electronic medical record data. However, unstructured data such as medical image data which is beyond the scope of standardization by the current OMOP-CDM is difficult to be used in multi-institutional collaborative research. Therefore, we developed the Radiology-CDM (R-CDM) which standardizes medical imaging data. As a proof of concept, 737,500 Optical Coherence Tomography (OCT) data from two tertiary hospitals in South Korea is standardized in the form of R-CDM. The relationship between chronic disease and retinal thickness was analyzed by using the R-CDM. Central macular thickness and retinal nerve fiber layer (RNFL) thickness were significantly thinner in the patients with hypertension compared to the control cohort. It is meaningful in that multi-institutional collaborative research using medical image data and clinical data simultaneously can be conducted very efficiently.

Real-World Effectiveness of Statin Therapy in Adult Asthma.

BACKGROUND: Blood lipids affect airway inflammation in asthma. Although several studies have suggested anti-inflammatory effects of statins on asthmatic airways, further studies are needed to clarify the long-term effectiveness of statins on asthma control and whether they are an effective treatment option. OBJECTIVE: To evaluate the long-term effectiveness of statins in the chronic management of adult asthma in real-world practice. METHODS: Electronic medical record data spanning 28 years, collected from the Ajou University Medical Center in Korea, were used to conduct a retrospective study. Clinical outcomes were compared between patients with asthma who had maintained statin use (the statin group) and those not taking statins, whose blood lipid tests were always normal (the non-statin group). We performed propensity score matching and calculated hazard ratios with 95% CIs using the Cox proportional hazards model. Severe asthma exacerbation was the primary outcome; asthma exacerbation, asthma-related hospitalization, and new-onset type 2 diabetes mellitus and hypertension were secondary outcomes. RESULTS: After 1:1 propensity score matching, the statin and non-statin groups each included 545 adult patients with asthma. The risk of severe asthma exacerbations and asthma exacerbations was significantly lower in the statin group than in the non-statin group (hazard ratios [95% CI] = 0.57 [0.35-0.90] and 0.71 [0.52-0.96], respectively). There were no significant differences in the risk of asthma-related hospitalization or new-onset type 2 diabetes mellitus or hypertension between groups (0.76 [0.53-1.09], 2.33 [0.94-6.59], and 1.71 [0.95-3.17], respectively). CONCLUSION: Statin use is associated with a lower risk of asthma exacerbation, with better clinical outcomes in adult asthma.

Real-world treatment intensities and pathways of macular edema following retinal vein occlusion in Korea from Common Data Model in ophthalmology.

Despite many studies, optimal treatment sequences or intervals are still questionable in retinal vein occlusion (RVO) macular edema. The aim of this study was to examine the real-world treatment patterns of RVO macular edema. A retrospective analysis of the Observational Medical Outcomes Partnership Common Data Model, a distributed research network, of four large tertiary referral centers (n = 9,202,032) identified 3286 eligible. We visualized treatment pathways (prescription volume and treatment sequence) with sunburst and Sankey diagrams. We calculated the average number of intravitreal injections per patient in the first and second years to evaluate the treatment intensities. Bevacizumab was the most popular first-line drug (80.9%), followed by triamcinolone (15.1%) and dexamethasone (2.28%). Triamcinolone was the most popular drug (8.88%), followed by dexamethasone (6.08%) in patients who began treatment with anti-vascular endothelial growth factor (VEGF) agents. The average number of all intravitreal injections per person decreased in the second year compared with the first year. The average number of injections per person in the first year increased throughout the study. Bevacizumab was the most popular first-line drug and steroids were considered the most common as second-line drugs in patients first treated with anti-VEGF agents. Intensive treatment patterns may cause an increase in intravitreal injections.

Development and Validation of the Radiology Common Data Model (R-CDM) for the International Standardization of Medical Imaging Data.

PURPOSE: Digital Imaging and Communications in Medicine (DICOM), a standard file format for medical imaging data, contains metadata describing each file. However, metadata are often incomplete, and there is no standardized format for recording metadata, leading to inefficiency during the metadata-based data retrieval process. Here, we propose a novel standardization method for DICOM metadata termed the Radiology Common Data Model (R-CDM). MATERIALS AND METHODS: R-CDM was designed to be compatible with Health Level Seven International (HL7)/Fast Healthcare Interoperability Resources (FHIR) and linked with the Observational Medical Outcomes Partnership (OMOP)-CDM to achieve a seamless link between clinical data and medical imaging data. The terminology system was standardized using the RadLex playbook, a comprehensive lexicon of radiology. As a proof of concept, the R-CDM conversion process was conducted with 41.7 TB of data from the Ajou University Hospital. The R-CDM database visualizer was developed to visualize the main characteristics of the R-CDM database. RESULTS: Information from 2801360 cases and 87203226 DICOM files was organized into two tables constituting the R-CDM. Information on imaging device and image resolution was recorded with more than 99.9% accuracy. Furthermore, OMOP-CDM and R-CDM were linked to efficiently extract specific types of images from specific patient cohorts. CONCLUSION: R-CDM standardizes the structure and terminology for recording medical imaging data to eliminate incomplete and unstandardized information. Successful standardization was achieved by the extract, transform, and load process and image classifier. We hope that the R-CDM will contribute to deep learning research in the medical imaging field by enabling the securement of large-scale medical imaging data from multinational institutions.

Chronic Disease Prediction Using the Common Data Model: Development Study.

BACKGROUND: Chronic disease management is a major health issue worldwide. With the paradigm shift to preventive medicine, disease prediction modeling using machine learning is gaining importance for precise and accurate medical judgement. OBJECTIVE: This study aimed to develop high-performance prediction models for 4 chronic diseases using the common data model (CDM) and machine learning and to confirm the possibility for the extension of the proposed models. METHODS: In this study, 4 major chronic diseases-namely, diabetes, hypertension, hyperlipidemia, and cardiovascular disease-were selected, and a model for predicting their occurrence within 10 years was developed. For model development, the Atlas analysis tool was used to define the chronic disease to be predicted, and data were extracted from the CDM according to the defined conditions. A model for predicting each disease was built with 4 algorithms verified in previous studies, and the performance was compared after applying a grid search. RESULTS: For the prediction of each disease, we applied 4 algorithms (logistic regression, gradient boosting, random forest, and extreme gradient boosting), and all models show greater than 80% accuracy. As compared to the optimized model's performance, extreme gradient boosting presented the highest predictive performance for the 4 diseases (diabetes, hypertension, hyperlipidemia, and cardiovascular disease) with 80% or greater and from 0.84 to 0.93 in area under the curve standards. CONCLUSIONS: This study demonstrates the possibility for the preemptive management of chronic diseases by predicting the occurrence of chronic diseases using the CDM and machine learning. With these models, the risk of developing major chronic diseases within 10 years can be demonstrated by identifying health risk factors using our chronic disease prediction machine learning model developed with the real-world data-based CDM and National Health Insurance Corporation examination data that individuals can easily obtain.

Characterization of Medication Trends for Chronic Kidney Disease: Mineral and Bone Disorder Treatment Using Electronic Health Record-Based Common Data Model.

Chronic kidney disease-mineral bone disorder (CKD-MBD) is the most common complication in CKD patients. Although there is a consensus on treatment guidelines for CKD-MBD, it remains uncertain whether these treatment recommendations reflect actual practice. Therefore, the aim of this study was to investigate the CKD-MBD medication trend in real-world practice. This was a retrospective and observational study using a 12-year period database transformed into a common data model from three tertiary university hospitals. Study populations were subjects initially diagnosed as CKD. The date of diagnosis was designated as the index date. New patients were categorized year to year from 2008 to 2019 with a fixed observation period of 365 days to check the prescription of CKD-MBD medications including calcium-containing phosphate binder, noncalcium-containing phosphate binder, aluminium hydroxide, vitamin D receptor activator (VDRA), and cinacalcet. The numbers of CKD patients in the three hospitals were 7555, 2424, and 5351, respectively. The proportion for patients with CKD-MBD medication prescription decreased yearly regardless of hospital and CKD stage (p for trend < 0.05). The use of aluminium hydroxide disappeared steadily while the use of VDRA increased annually in all settings. Despite these changes in prescription patterns, the mean value for CKD-MBD-related serologic markers was almost within target range. The proportion of the population within the target value was not significantly changed. Irrespective of hospital and CKD stage, similar trends of prescription for CKD-MBD medications were observed in real-world practice. Further research with a distributed network study may be helpful to understand medication trends in CKD-MBD treatment.