A main challenge for targeting chronic kidney disease (CKD) is the heterogeneity of its causes, co-morbidities and outcomes. Patients under nephrological care represent an important reference population, but knowledge about their characteristics is limited. We enrolled 5217 carefully phenotyped patients with moderate CKD [estimated glomerular filtration rate (eGFR) 30–60 mL/min per 1.73 m² or overt proteinuria at higher eGFR] under routine care of nephrologists into the German Chronic Kidney Disease (GCKD) study, thereby establishing the currently worldwide largest CKD cohort. The cohort has 60% men, a mean age (±SD) of 60 ± 12 years, a mean eGFR of 47 ± 17 mL/min per 1.73 m² and a median (IQR) urinary albumin/creatinine ratio of 51 (9–392) mg/g. Assessment of causes of CKD revealed a high degree of uncertainty, with the leading cause unknown in 20% and frequent suspicion of multifactorial pathogenesis. Thirty-five per cent of patients had diabetes, but only 15% were considered to have diabetic nephropathy. Cardiovascular disease prevalence was high (32%, excluding hypertension); prevalent risk factors included smoking (59% current or former smokers) and obesity (43% with BMI >30). Despite widespread use of anti-hypertensive medication, only 52% of the cohort had an office blood pressure <140/90 mmHg. Family histories for cardiovascular events (39%) and renal disease (28%) suggest familial aggregation. Patients with moderate CKD under specialist care have a high disease burden. Improved diagnostic accuracy, rigorous management of risk factors and unravelling of the genetic predisposition may represent strategies for improving prognosis.

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Summary: Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Similar to other large international data sharing networks (e.g. OHDSI, PCORnet, eMerge, RD-Connect) MIRACUM is a consortium of academic and hospital partners as well as one industrial partner in eight German cities which have joined forces to create interoperable data integration centres (DIC) and make data within those DIC available for innovative new IT solutions in patient care and medical research. Objectives: Sharing data shall be supported by common interoperable tools and services, in order to leverage the power of such data for biomedical discovery and moving towards a learning health system. This paper aims at illustrating the major building blocks and concepts which MIRACUM will apply to achieve this goal. Governance and Policies: Besides establishing an efficient governance structure within the MIRACUM consortium (based on the steering board, a central administrative office, the general MIRACUM assembly, six working groups and the international scientific advisory board), defining DIC governance rules and data sharing policies, as well as establishing (at each MIRACUM DIC site, but also for MIRACUM in total) use and access committees are major building blocks for the success of such an endeavor. Architectural Framework and Methodology: The MIRACUM DIC architecture builds on a comprehensive ecosystem of reusable open source tools (MIRACOLIX), which are linkable and interoperable amongst each other, but also with the existing software environment of the MIRACUM hospitals. Efficient data protection measures, considering patient consent, data harmonization and a MIRACUM metadata repository as well as a common data model are major pillars of this framework. The methodological approach for shared data usage relies on a federated querying and analysis concept. Use Cases: MIRACUM aims at proving the value of their DIC with three use cases: IT support for patient recruitment into clinical trials, the development and routine care implementation of a clinico-molecular predictive knowledge tool, and molecular-guided therapy recommendations in molecular tumor boards. Results: Based on the MIRACUM DIC release in the nine months conceptual phase first large scale analysis for stroke and colorectal cancer cohorts have been pursued. Discussion: Beyond all technological challenges successfully applying the MIRACUM tools for the enrichment of our knowledge about diagnostic and therapeutic concepts, thus supporting the concept of a Learning Health System will be crucial for the acceptance and sustainability in the medical community and the MIRACUM university hospitals.

Reduced kidney function is a risk factor for hyperuricaemia and gout, but limited information on the burden of gout is available from studies of patients with chronic kidney disease (CKD). We therefore examined the prevalence and correlates of gout in the large prospective observational German Chronic Kidney Disease (GCKD) study. Data from 5085 CKD patients aged 18–74 years with an estimated glomerular filtration rate (eGFR) of 30–<60 mL/min/1.73 m² or eGFR ≥60 and overt proteinuria at recruitment and non-missing values for self-reported gout, medications and urate measurements from a central laboratory were evaluated. The overall prevalence of gout was 24.3%, and increased from 16.0% in those with eGFR ≥60 mL/min/1.73 m² to 35.6% in those with eGFR <30. Of those with self-reported gout, 30.7% of individuals were not currently taking any gout medication and among gout patients on urate lowering therapy, 47.2% still showed hyperuricaemia. Factors associated with gout were serum urate, lower eGFR, advanced age, male sex, higher body mass index and waist-to-hip ratio, higher triglyceride and C-reactive protein (CRP) concentrations, alcohol intake and diuretics use. While lower eGFR categories showed significant associations with gout in multivariable-adjusted models (prevalence ratio 1.46 for eGFR <30 compared with eGFR ≥60, 95% confidence interval 1.21–1.77), associations between gout and higher urinary albumin-to-creatinine ratio in this CKD population were not significant. Self-reported gout is common among patients with CKD and lower GFR is strongly associated with gout. Pharmacological management of gout in patients with CKD is suboptimal. Prospective follow-up will show whether gout and hyperuricaemia increase the risk of CKD progression and cardiovascular events in the GCKD study.

Data from the electronic medical record comprise numerous structured but uncoded ele-ments, which are not linked to standard terminologies. Reuse of such data for secondary research purposes has gained in importance recently. However, the identification of rele-vant data elements and the creation of database jobs for extraction, transformation and loading (ETL) are challenging: With current methods such as data warehousing, it is not feasible to efficiently maintain and reuse semantically complex data extraction and trans-formation routines. We present an ontology-supported approach to overcome this challenge by making use of abstraction: Instead of defining ETL procedures at the database level, we use ontologies to organize and describe the medical concepts of both the source system and the target system. Instead of using unique, specifically developed SQL statements or ETL jobs, we define declarative transformation rules within ontologies and illustrate how these constructs can then be used to automatically generate SQL code to perform the desired ETL procedures. This demonstrates how a suitable level of abstraction may not only aid the interpretation of clinical data, but can also foster the reutilization of methods for un-locking it.

Computerized clinical trial recruitment support is one promising field for the application of routine care data for clinical research. The primary task here is to compare the eligibility criteria defined in trial protocols with patient data contained in the electronic health record (EHR). To avoid the implementation of different patient definitions in multi-site trials, all participating research sites should use similar patient data from the EHR. Knowledge of the EHR data elements which are commonly available from most EHRs is required to be able to define a common set of criteria. The objective of this research is to determine for five tertiary care providers the extent of available data compared with the eligibility criteria of randomly selected clinical trials. Each participating study site selected three clinical trials at random. All eligibility criteria sentences were broken up into independent patient characteristics, which were then assigned to one of the 27 semantic categories for eligibility criteria developed by Luo et al. We report on the fraction of patient characteristics with corresponding structured data elements in the EHR and on the fraction of patients with available data for these elements. The completeness of EHR data for the purpose of patient recruitment is calculated for each semantic group. 351 eligibility criteria from 15 clinical trials contained 706 patient characteristics. In average, 55% of these characteristics could be documented in the EHR. Clinical data was available for 64% of all patients, if corresponding data elements were available. The total completeness of EHR data for recruitment purposes is 35%. The best performing semantic groups were ‘age’ (89%), ‘gender’ (89%), ‘addictive behaviour’ (74%), ‘disease, symptom and sign’ (64%) and ‘organ or tissue status’ (61%). No data was available for 6 semantic groups. There exists a significant gap in structure and content between data documented during patient care and data required for patient eligibility assessment. Nevertheless, EHR data on age and gender of the patient, as well as selected information on his disease can be complete enough to allow for an effective support of the manual screening process with an intelligent preselection of patients and patient data.