ABSTRACT
Objective: To implement a dynamic data management and control framework that meets the multiple demands of high data quality, rigorous information technology security, and flexibility to continuously incorporate new methodology for a large disease registry. Materials and Methods: Guided by relevant sections of the COBIT framework and ISO 27001 standard, we created a data control framework supporting high-quality real-world data (RWD) studies in multiple disease areas. We first mapped and described the entire data journey and identified potential risks for data loss or inconsistencies. Based on this map, we implemented a control framework adhering to best practices and tested its effectiveness through an analysis of random data samples. An internal strategy board was set up to regularly identify and implement potential improvements. Results: We herein describe the implementation of a data management and control framework for multiple sclerosis, one disease area in the NeuroTransData (NTD) registry that exemplifies the dynamic needs for high-quality RWD analysis. Regular manual and automated analysis of random data samples at multiple checkpoints guided the development and implementation of the framework and continue to ensure timely identification of potential threats to data accuracy. Discussion and conclusions: High-quality RWD, especially those derived from long-term disease registries, are of increasing importance from regulatory and reimbursement perspectives, requiring owners to provide data of comparable quality to clinical trials. The framework presented herein responds to the call for transparency in real-world analyses and allows doctors and patients to experience an immediate benefit of the collected data for individualized optimal care.
ABSTRACT
"Real-world evidence (RWE)" is becoming increasingly important in order to integrate the results of randomized studies into everyday clinical practice. The data collection of RWE is usually derived from large-scale national and international registries, often driven by academic centers. We have developed a digitalized doctor-patient platform called DESTINY (DatabasE-assiSted Therapy decIsioN support sYstem) that is utilized by NeuroTransData (NTD), a network of neurologists and psychiatrists throughout Germany. This platform can be integrated into everyday practice and, as well as being used for scientific evaluations in healthcare research, can also serve as an individual, personalized treatment application. Its various modules allow for a timely identification of side-effects or interactions of treatments, can involve patients via the "My NTC Health Guide" portal, and can collect data of individual disease histories that are integrated into innovative algorithms, e.g., for the prediction of treatment response [currently available for multiple sclerosis (MS), with other indications in the pipeline]. Here, we describe the doctor-patient platform DESTINY for outpatient neurological practices and its contribution to improved treatment success as well as reduction of healthcare costs. Platforms like DESTINY may facilitate the goal of personalized healthcare.
ABSTRACT
BACKGROUND: Comparative effectiveness (CE) research allows real-world treatment comparisons using outcome measurements important to physicians/patients. This German NeuroTransData registry-based analysis compared delayed-release dimethyl fumarate (DMF) effectiveness with interferons (IFN), glatiramer acetate (GA), teriflunomide (TERI), or fingolimod (FTY) in patients with relapsing-remitting multiple sclerosis (RRMS) using propensity score matching (PSM). METHODS: Data from registry patients aged ≥ 18 years with RRMS, ≥ 1 relapse, and Expanded Disability Status Scale (EDSS) assessment(s) after index therapy initiation underwent 1:1 PSM to match DMF with comparator populations baseline characteristics. Primary outcome measurement was time to first relapse (TTFR). Secondary outcome measurements included annualised relapse rate (ARR), proportion of patients relapse free at 12 and 24 months, time to index therapy discontinuation (TTD), and reasons for discontinuation. Exploratory analyses included time to 3- and 6-month EDSS confirmed disability progression (CDP). Non-pairwise censoring was the primary analysis method; pairwise censoring was the main sensitivity analysis method. FINDINGS: Post-matched cohorts were well-balanced. By non-pairwise censoring, TTFR and ARR were significantly lower in DMF populations versus matched IFN, GA, and TERI, but there was no evidence of difference between DMF and FTY. TTD was similar between DMF and IFN, GA, and TERI, but significantly shorter versus FTY. Time to CDP generally showed no evidence of difference between DMF and comparator populations. Pairwise censored analysis results confirmed the non-pairwise censoring results. INTERPRETATION: These results support previous CE studies in demonstrating relative improvement in real-world effectiveness with DMF versus first-line agents IFN, GA, and TERI, and similar effectiveness versus FTY.
