The Danish Lymphoid Cancer Research (DALY-CARE) Data Resource: The Basis for Developing Data-Driven Hematology

Christian Brieghel; Mikkel Werling; Casper Møller Frederiksen; Mehdi Parviz; Thomas Lacoppidan; Tereza Faitova; Rebecca Svanberg Teglgaard; Noomi Vainer; Caspar da Cunha-Bang; Emelie Curovic Rotbain; Rudi Agius; Carsten Utoft Niemann

doi:10.2147/CLEP.S479672

The Danish Lymphoid Cancer Research (DALY-CARE) Data Resource: The Basis for Developing Data-Driven Hematology

Clin Epidemiol. 2025 Feb 20:17:131-145. doi: 10.2147/CLEP.S479672. eCollection 2025.

Authors

Christian Brieghel^#^{1

2}, Mikkel Werling^#^{1

2}, Casper Møller Frederiksen^{1

2}, Mehdi Parviz^{1

3}, Thomas Lacoppidan¹, Tereza Faitova^{1

2}, Rebecca Svanberg Teglgaard^{1

4}, Noomi Vainer^{1

2}, Caspar da Cunha-Bang^{1

2}, Emelie Curovic Rotbain^{1

2}, Rudi Agius¹, Carsten Utoft Niemann^{1

2

3}

Affiliations

¹ Department of Hematology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
² Danish Cancer Institute, Copenhagen, Denmark.
³ Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
⁴ Department of Clinical Immunology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.

^# Contributed equally.

Abstract

Background: Lymphoid-lineage cancers (LC; International Classification of Diseases, 10^th edition [ICD10] C81.x-C90.x, C91.1-C91.9, C95.1, C95.7, C95.9, D47.2, D47.9B, and E85.8A) share many epidemiological and clinical features, which favor meta-learning when developing medical artificial intelligence (mAI). However, access to large, shared datasets is largely missing and limits mAI research.

Aim: Creating a large-scale data repository for patients with LC to develop data-driven hematology.

Methods: We gathered electronic health data and created open-source processing pipelines to create a comprehensive data resource for Danish LC Research (DALY-CARE) approved for epidemiological, molecular, and data-driven research.

Results: We included all Danish adults registered with LC diagnoses since 2002 (n=65,774) and combined 10 nationwide registers, electronic health records (EHR), and laboratory data on a high-powered cloud-computer to develop a secure research environment. Among other, data include treatments (ie 21,750 cytoreductive treatment plans, 21.3M outpatient prescriptions, and 12.7M in-hospital administrations), biochemical analyses (77.3M), comorbidity (14.8M ICD10 codes), pathology codes (4.5M), treatment procedures (8.3M), surgical procedures (1.0M), radiological examinations (3.3M), vital signs (18.3M values), and survival data. We herein describe the data infrastructure and exemplify how DALY-CARE has been used for molecular studies, real-world evidence to evaluate the efficacy of care, and mAI deployed directly into EHR systems.

Conclusion: The DALY-CARE data resource allows for the development of near real-time decision-support tools and extrapolation of clinical trial results to clinical practice, thereby improving care for patients with LC while facilitating streamlining of health data infrastructure across cohorts and medical specialties.

Keywords: chronic lymphocytic leukemia; data-driven medicine; large-scale database; lymphoma; machine learning; multiple myeloma.

Grants and funding

The project was funded by the Alfred Benzon Foundation, the Danish Cancer Society (grant R269-A15924), Sygesikring Danmark, and the CLL-CLUE project funded by the European Union. This work was based on data analyzed at the national infrastructure for personal medicine hosted at the Danish National Genome Center, which is supported by the Novo Nordisk Foundation (grant agreement NNF18SA0035348 and grant agreement NNF19SA0035486). This work was supported by the Danish Data Science Academy, which is funded by the Novo Nordisk Foundation (NNF21SA0069429) and VILLUM FONDEN (40516). The PERSIMUNE project contributed data and achieved funding from the Danish National Research Foundation (#126). WGS is achieved through collaboration with deCODE genetics (Reykjavík, Iceland).