Increase transparency and reproducibility of real-world evidence in rare diseases through disease-specific Federated Data Networks.

PURPOSE: In rare diseases, real-world evidence (RWE) generation is often restricted due to small patient numbers and global geographic distribution. A federated data network (FDN) approach brings together multiple data sources harmonized for collaboration to increase the power of observational research. In this paper, we review how to increase reproducibility and transparency of RWE studies in rare diseases through disease-specific FDNs. METHOD: To be successful, a multiple stakeholder scientific FDN collaboration requires a strong governance model in place. In such a model, each database owner remains in full control regarding the use of and access to patient-level data and is responsible for data privacy, ethical, and legal compliance. Provided that all this is well documented and good database descriptions are in place, such a governance model results in increased transparency, while reproducibility is achieved through data curation and harmonization, and distributed analytical methods. RESULTS: Leveraging the OHDSI community set of methods and tools, two rare disease-specific FDNs are discussed in more detail. For multiple myeloma, HONEUR-the Haematology Outcomes Network in Europe-has built a strong community among the data partners dedicated to scientific exchange and research. To advance scientific knowledge in pulmonary hypertension (PH) an FDN, called PHederation, was established to form a partnership of research institutions with PH databases coming from diverse origins.

European Health Data & Evidence Network-learnings from building out a standardized international health data network.

OBJECTIVE: Health data standardized to a common data model (CDM) simplifies and facilitates research. This study examines the factors that make standardizing observational health data to the Observational Medical Outcomes Partnership (OMOP) CDM successful. MATERIALS AND METHODS: Twenty-five data partners (DPs) from 11 countries received funding from the European Health Data Evidence Network (EHDEN) to standardize their data. Three surveys, DataQualityDashboard results, and statistics from the conversion process were analyzed qualitatively and quantitatively. Our measures of success were the total number of days to transform source data into the OMOP CDM and participation in network research. RESULTS: The health data converted to CDM represented more than 133 million patients. 100%, 88%, and 84% of DPs took Surveys 1, 2, and 3. The median duration of the 6 key extract, transform, and load (ETL) processes ranged from 4 to 115 days. Of the 25 DPs, 21 DPs were considered applicable for analysis of which 52% standardized their data on time, and 48% participated in an international collaborative study. DISCUSSION: This study shows that the consistent workflow used by EHDEN proves appropriate to support the successful standardization of observational data across Europe. Over the 25 successful transformations, we confirmed that getting the right people for the ETL is critical and vocabulary mapping requires specific expertise and support of tools. Additionally, we learned that teams that proactively prepared for data governance issues were able to avoid considerable delays improving their ability to finish on time. CONCLUSION: This study provides guidance for future DPs to standardize to the OMOP CDM and participate in distributed networks. We demonstrate that the Observational Health Data Sciences and Informatics community must continue to evaluate and provide guidance and support for what ultimately develops the backbone of how community members generate evidence.

Identification of PsA phenotypes with machine learning analytics using data from two phase III clinical trials of guselkumab in a bio-naïve population of patients with PsA.

OBJECTIVES: Psoriatic arthritis (PsA) phenotypes are typically defined by their clinical components, which may not reflect patients' overlapping symptoms. This post hoc analysis aimed to identify hypothesis-free PsA phenotype clusters using machine learning to analyse data from the phase III DISCOVER-1/DISCOVER-2 clinical trials. METHODS: Pooled data from bio-naïve patients with active PsA receiving guselkumab 100 mg every 8/4 weeks were retrospectively analysed. Non-negative matrix factorisation was applied as an unsupervised machine learning technique to identify PsA phenotype clusters; baseline patient characteristics and clinical observations were input features. Minimal disease activity (MDA), disease activity index for psoriatic arthritis (DAPSA) low disease activity (LDA) and DAPSA remission at weeks 24 and 52 were evaluated. RESULTS: Eight clusters (n=661) were identified: cluster 1 (feet dominant), cluster 2 (male, overweight, psoriasis dominant), cluster 3 (hand dominant), cluster 4 (dactylitis dominant), cluster 5 (enthesitis, large joints), cluster 6 (enthesitis, small joints), cluster 7 (axial dominant) and cluster 8 (female, obese, large joints). At week 24, MDA response was highest in cluster 2 and lowest in clusters 3, 5 and 6; at week 52, it was highest in cluster 2 and lowest in cluster 5. At weeks 24 and 52, DAPSA LDA and remission were highest in cluster 2 and lowest in clusters 4 and 6, respectively. All clusters improved with guselkumab treatment over 52 weeks. CONCLUSIONS: Unsupervised machine learning identified eight PsA phenotype clusters with significant differences in demographics, clinical features and treatment responses. In the future, such data could help support individualised treatment decisions.

Haematology Outcomes Network in Europe (HONEUR)-A collaborative, interdisciplinary platform to harness the potential of real-world data in hematology.

INTRODUCTION: There remains a need to optimize treatments and improve outcomes among patients with hematologic malignancies. The timely synthesis and analysis of real-world data could play a key role. OBJECTIVES: The Haematology Outcomes Network in Europe (HONEUR) is a federated data network (FDN) that aims to overcome the challenges of heterogenous data collected from different registries, hospitals, and other databases in different countries. It has the functionality required to analyze data from various sources in a time efficient manner, while preserving local data security and governance. With this, research studies can be performed that can increase knowledge and understanding of the management of patients with hematologic malignancies. METHODS: HONEUR uses the Observational Medical Outcomes Partnership (OMOP) common data model, which allows analysis scripts to be run by multiple sites using their own data, ultimately generating aggregated results. Furthermore, distributed analytics can be used to run statistical analyses across multiple sites, as if data were pooled. The external governance model ensures high-quality standards, while data ownership is retained locally. Twenty partners from nine countries are now participating, with data from more than 26 000 patients available for analysis. Research questions that can be addressed through HONEUR include assessments of natural disease history, treatment patterns, and clinical effectiveness. CONCLUSIONS: The HONEUR FDN marks an important step forward in increasing the value of information routinely captured by individual hospitals, registries and other database holders, thus enabling larger-scale studies to be undertaken rapidly and efficiently.

Opioid use, postoperative complications, and implant survival after unicompartmental versus total knee replacement: a population-based network study.

BACKGROUND: There is uncertainty around whether to use unicompartmental knee replacement (UKR) or total knee replacement (TKR) for individuals with osteoarthritis confined to a single compartment of the knee. We aimed to emulate the design of the Total or Partial Knee Arthroplasty Trial (TOPKAT) using routinely collected data to assess whether the efficacy results reported in the trial translate into effectiveness in routine practice, and to assess comparative safety. METHODS: We did a population-based network study using data from four US and one UK health-care database, part of the Observational Health Data Sciences and Informatics network. The inclusion criteria were the same as those for TOPKAT; briefly, we identified patients aged at least 40 years with osteoarthritis who had undergone UKR or TKR and who had available data for at least one year prior to surgery. Patients were excluded if they had evidence of previous knee arthroplasty, knee fracture, knee surgery (except diagnostic), rheumatoid arthritis, infammatory arthropathies, or septic arthritis. Opioid use from 91-365 days after surgery, as a proxy for persistent pain, was assessed for all participants in all databases. Postoperative complications (ie, venous thromboembolism, infection, readmission, and mortality) were assessed over the 60 days after surgery and implant survival (as measured by revision procedures) was assessed over the 5 years after surgery. Outcomes were assessed in all databases, except for readmission, which was assessed in three of the databases, and mortality, which was assessed in two of the databases. Propensity score matched Cox proportional hazards models were fitted for each outcome. Calibrated hazard ratios (cHRs) were generated for each database to account for observed differences in control outcomes, and cHRs were then combined using meta-analysis. FINDINGS: 33 867 individuals who received UKR and 557 831 individuals who received TKR between Jan 1, 2005, and April 30, 2018, were eligible for matching. 32 379 with UKR and 250 377 with TKR were propensity score matched and informed the analyses. UKR was associated with a reduced risk of postoperative opioid use (cHR from meta-analysis 0·81, 95% CI 0·73-0·90) and a reduced risk of venous thromboembolism (0·62, 0·36-0·95), whereas no difference was seen for infection (0·85, 0·51-1·37) and readmission (0·79, 0·47-1·25). Evidence was insufficient to conclude whether there was a reduction in risk of mortality. UKR was also associated with an increased risk of revision (1·64, 1·40-1·94). INTERPRETATION: UKR was associated with a reduced risk of postoperative opioid use compared with TKR, which might indicate a reduced risk of persistent pain after surgery. UKR was associated with a lower risk of venous thromboembolism but an increased risk of revision compared with TKR. These findings can help to inform shared decision making for individuals eligible for knee replacement surgery. FUNDING: EU/European Federation of Pharmaceutical Industries and Associations Innovative Medicines Initiative (2) Joint Undertaking (EHDEN).

Nine Principles of Semantic Harmonization.

Medical data is routinely collected, stored and recorded across different institutions and in a range of different formats. Semantic harmonization is the process of collating this data into a singular consistent logical view, with many approaches to harmonizing both possible and valid. The broad scope of possibilities for undertaking semantic harmonization do lead however to the development of bespoke and ad-hoc systems; this is particularly the case when it comes to cohort data, the format of which is often specific to a cohort's area of focus. Guided by work we have undertaken in developing the 'EMIF Knowledge Object Library', a semantic harmonization framework underpinning the collation of pan-European Alzheimer's cohort data, we have developed a set of nine generic guiding principles for developing semantic harmonization frameworks, the application of which will establish a solid base for constructing similar frameworks.