A Fully decentralized, patient-led digital registry for ATTR-CM integrating multisystem EHR and wearable data: the DISCOVER-ATTR study

Background Traditional registries for rare cardiovascular diseases, such as transthyretin amyloid cardiomyopathy (ATTR-CM), face limitations that hinder comprehensive data capture. These include reliance on manual abstraction into case report forms, fragmented care across health systems, and limited interoperability of electronic health records (EHRs). Moreover, granular physiologic data from patient-owned wearable devices is not captured. US health policies now require patient access to their EHR, enabling direct sharing of EHR data for research. Additionally, innovations that enable the capture of wearable-derived physiologic data can enhance longitudinal phenotyping by capturing continuous health signals not available in routine care. Purpose To develop DISCOVER-ATTR, the first fully digital, patient-led registry integrating multisystem EHR and wearable physiologic data, and to evaluate the information available in the registry in its pilot deployment, comparing against manual EHR evaluation. Methods In a large US health system, 20 adults with clinician-confirmed ATTR-CM were remotely enrolled with electronic consent. Participants connected patient portals to enable automated multisite EHR retrieval via HL7 FHIR, capturing demographic, clinical, and encounter data (A). Participants were also invited to share wearable-derived data through a secure data-sharing application. Data were harmonized into longitudinal multimodal patient timelines within a secure research environment. Results All 20 patients completed enrollment, and 17 connected to at least one EHR. Median longitudinal EHR coverage was 10.6 (1.7–14.2) years, with 65 (21–347) encounters per participant, encompassing 292 (67–1,209) diagnoses, 15 (6–125) procedures, and 268 (70–1,325) clinical notes. The registry captured 34 hospitalizations, 25 ED visits, and 1,825 outpatient visits, with perfect concordance with manual EHR abstraction. Among hospitalizations and ED visits, 10 (17%) were for cardiovascular reasons. Wearable data sharing was established in six participants, providing continuous high-density data from Dec 2022 to Jan 2026, including a mean of 222K heart-rate measurements per participant over 1,085 days (98% coverage), with comparable step count coverage (98%), 1,637 oxygen saturation readings, and 50 single-lead ECGs per participant (C). In a single-patient case study, wearable-derived data showed a temporal relationship with EHR-based clinical events, demonstrating the feasibility of integrated multimodal patient timelines without site-based infrastructure (D). Conclusion A fully decentralized, patient-led digital registry can successfully aggregate longitudinal multisystem EHR data and wearable physiologic signals in ATTR-CM. Early results show substantial data yield and feasibility of longitudinal mapping of care trajectories and multimodal risk prediction, providing a blueprint for next-generation registries in rare cardiovascular diseases.Figure 1Figure 2