Establishment of the Optimal Common Data Model Environment for EMR Data Considering the Computing Resources of Medical Institutions

Electronic medical record (EMR) data vary between institutions. These data should be converted into a common data model (CDM) for multi-institutional joint research. To build the CDM, it is essential to integrate the EMR data of each hospital and load it according to the CDM model, considering the computing resources of each hospital. Accordingly, this study attempts to share experiences and recommend computing resource-allocation designs. Here, two types of servers were defined: combined and separated servers. In addition, three database (DB) setting types were selected: desktop application (DA), online transaction processing (OLTP), and data warehouse (DW). Scale, TPS, average latency, 90th percentile latency, and maximum latency were compared across various settings. Virtual memory (vmstat) and disk input/output (disk) statuses were also described. Transactions per second (TPS) decreased as the scale increased in all DB types; however, the average, 90th percentile and maximum latencies exhibited no tendency according to scale. When compared with the maximum number of clients (DA client = 5, OLTP clients = 20, DW clients = 10), the TPS, average latency, 90th percentile latency, and maximum latency values were highest in the order of OLTP, DW, and DA. In vmstat, the amount of memory used for the page cache field and free memory currently available for DA, OLTP, and DW were large compared to other fields. In the disk, DA, OLTP, and DW all recorded the largest value in the average size of write requests, followed by the largest number of write requests per second. In summary, this study presents recommendations for configuring CDM settings. The configuration must be tuned carefully, considering the hospital’s resources and environment, and the size of the database must consider concurrent client connections, architecture, and connections.

Common Data Model and Database System Development for the Korea Biobank Network

The importance of clinical information related to specimens is increasing due to the research on human biological specifications being conducted worldwide. In order to utilize data, it is necessary to define the range of data and develop a standardized system for collected resources. The purpose of this study is to establish clinical information standardization and to allow clinical information management systems to improve the utilization of biological specifications. The KBN CDM, consisting of 18 tables and 177 variables, was developed. The clinical information codes were mapped in standard terms. The 27 diseases in the group were collected from 17 biobanks, and all disorders not belonging to the group were standardized and loaded. We also developed a system that provides statistical visualization screens and data retrieval tools for data collection. This study developed a unified management system to model KBN CDM that collects standardized data, manages clinical information, and shares the information systematically. Through this system, all participating biobanks can be integrated into one system for integrated management and research.

Validation of Cardiovascular End Points Ascertainment Leveraging Multisource Electronic Health Records Harmonized Into a Common Data Model in the ADAPTABLE Randomized Clinical Trial

Background: The ADAPTABLE trial (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness) is the first randomized trial conducted within the National Patient-Centered Clinical Research Network to use the electronic health record data formatted into a common data model as the primary source of end point ascertainment, without confirmation by standard adjudication. The objective of this prespecified study is to assess the validity of nonfatal end points captured from the National Patient-Centered Clinical Research Network, using traditional blinded adjudication as the gold standard. Methods: A total of 15 076 participants with established atherosclerotic cardiovascular disease were randomized to two doses of aspirin (81 mg and 325 mg once daily). Nonfatal end points (hospitalization for nonfatal myocardial infarction, nonfatal stroke, and major bleeding requiring transfusion of blood products) were captured with the use of programming algorithms applied to National Patient-Centered Clinical Research Network data. A random subset of end points was independently reviewed by a disease-specific expert adjudicator. The positive predictive value of the programming algorithms were calculated separately for end points listed as primary and as nonprimary diagnoses. Results: A total of 225 end points were identified (91 myocardial infarction events, 89 stroke events, and 45 bleeding events), including 142 (63%) that were listed as primary diagnoses. Complete source documents were missing for 14% of events. The positive predictive value were 90%, 72%, and 93% for hospitalizations for myocardial infarction, stroke, and major bleeding, respectively, as compared to adjudication. When only primary diagnoses were considered, positive predictive value were 93%, 91%, and 97%, respectively. When only nonprimary diagnoses were considered, positive predictive value were 82%, 36%, and 71%. Conclusions: As compared with blinded adjudication, clinical end point ascertainment from queries of the National Patient-Centered Clinical Research Network distributed harmonized data was valid to identify hospitalizations for myocardial infarction in ADAPTABLE. The proportion of contradicted events was high for hospitalizations for bleeding and strokes when nonprimary diagnoses were analyzed, but not when only primary diagnoses were considered.

Coping with interoperability in the development of a federated research infrastructure: achievements, challenges and recommendations from the JA-InfAct

Background Information for Action! is a Joint Action (JA-InfAct) on Health Information promoted by the EU Member States and funded by the European Commission within the Third EU Health Programme (2014–2020) to create and develop solid sustainable infrastructure on EU health information. The main objective of this the JA-InfAct is to build an EU health information system infrastructure and strengthen its core elements by a) establishing a sustainable research infrastructure to support population health and health system performance assessment, b) enhancing the European health information and knowledge bases, as well as health information research capacities to reduce health information inequalities, and c) supporting health information interoperability and innovative health information tools and data sources. Methods Following a federated analysis approach, JA-InfAct developed an ad hoc federated infrastructure based on distributing a well-defined process-mining analysis methodology to be deployed at each participating partners’ systems to reproduce the analysis and pool the aggregated results from the analyses. To overcome the legal interoperability issues on international data sharing, data linkage and management, partners (EU regions) participating in the case studies worked coordinately to query their real-world healthcare data sources complying with a common data model, executed the process-mining analysis pipeline on their premises, and shared the results enabling international comparison and the identification of best practices on stroke care. Results The ad hoc federated infrastructure was designed and built upon open source technologies, providing partners with the capacity to exploit their data and generate dashboards exploring the stroke care pathways. These dashboards can be shared among the participating partners or to a coordination hub without legal issues, enabling the comparative evaluation of the caregiving activities for acute stroke across regions. Nonetheless, the approach is not free of a number of challenges that have been solved, and new challenges that should be addressed in the eventual case of scaling up. For that eventual case, 12 recommendations considering the different layers of interoperability have been provided. Conclusion The proposed approach, when successfully deployed as a federated analysis infrastructure, such as the one developed within the JA-InfAct, can concisely tackle all levels of the interoperability requirements from organisational to technical interoperability, supported by the close collaboration of the partners participating in the study. Any proposal for extension, should require further thinking on how to deal with new challenges on interoperability.

Characteristics and outcomes of patients with COVID-19 with and without prevalent hypertension: a multinational cohort study

ObjectiveTo characterise patients with and without prevalent hypertension and COVID-19 and to assess adverse outcomes in both inpatients and outpatients.Design and settingThis is a retrospective cohort study using 15 healthcare databases (primary and secondary electronic healthcare records, insurance and national claims data) from the USA, Europe and South Korea, standardised to the Observational Medical Outcomes Partnership common data model. Data were gathered from 1 March to 31 October 2020.ParticipantsTwo non-mutually exclusive cohorts were defined: (1) individuals diagnosed with COVID-19 (diagnosed cohort) and (2) individuals hospitalised with COVID-19 (hospitalised cohort), and stratified by hypertension status. Follow-up was from COVID-19 diagnosis/hospitalisation to death, end of the study period or 30 days.OutcomesDemographics, comorbidities and 30-day outcomes (hospitalisation and death for the ‘diagnosed’ cohort and adverse events and death for the ‘hospitalised’ cohort) were reported.ResultsWe identified 2 851 035 diagnosed and 563 708 hospitalised patients with COVID-19. Hypertension was more prevalent in the latter (ranging across databases from 17.4% (95% CI 17.2 to 17.6) to 61.4% (95% CI 61.0 to 61.8) and from 25.6% (95% CI 24.6 to 26.6) to 85.9% (95% CI 85.2 to 86.6)). Patients in both cohorts with hypertension were predominantly >50 years old and female. Patients with hypertension were frequently diagnosed with obesity, heart disease, dyslipidaemia and diabetes. Compared with patients without hypertension, patients with hypertension in the COVID-19 diagnosed cohort had more hospitalisations (ranging from 1.3% (95% CI 0.4 to 2.2) to 41.1% (95% CI 39.5 to 42.7) vs from 1.4% (95% CI 0.9 to 1.9) to 15.9% (95% CI 14.9 to 16.9)) and increased mortality (ranging from 0.3% (95% CI 0.1 to 0.5) to 18.5% (95% CI 15.7 to 21.3) vs from 0.2% (95% CI 0.2 to 0.2) to 11.8% (95% CI 10.8 to 12.8)). Patients in the COVID-19 hospitalised cohort with hypertension were more likely to have acute respiratory distress syndrome (ranging from 0.1% (95% CI 0.0 to 0.2) to 65.6% (95% CI 62.5 to 68.7) vs from 0.1% (95% CI 0.0 to 0.2) to 54.7% (95% CI 50.5 to 58.9)), arrhythmia (ranging from 0.5% (95% CI 0.3 to 0.7) to 45.8% (95% CI 42.6 to 49.0) vs from 0.4% (95% CI 0.3 to 0.5) to 36.8% (95% CI 32.7 to 40.9)) and increased mortality (ranging from 1.8% (95% CI 0.4 to 3.2) to 25.1% (95% CI 23.0 to 27.2) vs from 0.7% (95% CI 0.5 to 0.9) to 10.9% (95% CI 10.4 to 11.4)) than patients without hypertension.ConclusionsCOVID-19 patients with hypertension were more likely to suffer severe outcomes, hospitalisations and deaths compared with those without hypertension.

Thirty-day hospital readmission prediction model based on common data model with weather and air quality data

Although several studies have attempted to develop a model for predicting 30-day re-hospitalization, few attempts have been made for sufficient verification and multi-center expansion for clinical use. In this study, we developed a model that predicts unplanned hospital readmission within 30 days of discharge; the model is based on a common data model and considers weather and air quality factors, and can be easily extended to multiple hospitals. We developed and compared four tree-based machine learning methods: decision tree, random forest, AdaBoost, and gradient boosting machine (GBM). Above all, GBM showed the highest AUC performance of 75.1 in the clinical model, while the clinical and W-score model showed the best performance of 73.9 for musculoskeletal diseases. Further, PM10, rainfall, and maximum temperature were the weather and air quality variables that most impacted the model. In addition, external validation has confirmed that the model based on weather and air quality factors has transportability to adapt to other hospital systems.

Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study

Many patients with bipolar disorder (BD) are initially misdiagnosed with major depressive disorder (MDD) and are treated with antidepressants, whose potential iatrogenic effects are widely discussed. It is unknown whether MDD is a comorbidity of BD or its earlier stage, and no consensus exists on individual conversion predictors, delaying BD’s timely recognition and treatment. We aimed to build a predictive model of MDD to BD conversion and to validate it across a multi-national network of patient databases using the standardization afforded by the Observational Medical Outcomes Partnership (OMOP) common data model. Five “training” US databases were retrospectively analyzed: IBM MarketScan CCAE, MDCR, MDCD, Optum EHR, and Optum Claims. Cyclops regularized logistic regression models were developed on one-year MDD-BD conversion with all standard covariates from the HADES PatientLevelPrediction package. Time-to-conversion Kaplan-Meier analysis was performed up to a decade after MDD, stratified by model-estimated risk. External validation of the final prediction model was performed across 9 patient record databases within the Observational Health Data Sciences and Informatics (OHDSI) network internationally. The model’s area under the curve (AUC) varied 0.633–0.745 (µ = 0.689) across the five US training databases. Nine variables predicted one-year MDD-BD transition. Factors that increased risk were: younger age, severe depression, psychosis, anxiety, substance misuse, self-harm thoughts/actions, and prior mental disorder. AUCs of the validation datasets ranged 0.570–0.785 (µ = 0.664). An assessment algorithm was built for MDD to BD conversion that allows distinguishing as much as 100-fold risk differences among patients and validates well across multiple international data sources.

Preliminary feasibility assessment of CDM-based active surveillance using current status of medical device data in medical records and OMOP-CDM

In recent years, there has been an emerging interest in the use of claims and electronic health record (EHR) data for evaluation of medical device safety and effectiveness. In Korea, national insurance electronic data interchange (EDI) code has been used as a medical device data source for common data model (CDM). This study performed a preliminary feasibility assessment of CDM-based vigilance. A cross-sectional study of target medical device data in EHR and CDM was conducted. A total of 155 medical devices were finally enrolled, with 58.7% of them having EDI codes. Femoral head prosthesis was selected as a focus group. It was registered in our institute with 11 EDI codes. However, only three EDI codes were converted to systematized nomenclature of medicine clinical terms concept. EDI code was matched in one-to-many (up to 104) with unique device identifier (UDI), including devices classified as different global medical device nomenclature. The use of UDI rather than EDI code as a medical device data source is recommended. We hope that this study will share the current state of medical device data recorded in the EHR and contribute to the introduction of CDM-based medical device vigilance by selecting appropriate medical device data sources.