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The Danish Lymphoid Cancer Research (DALY-CARE) Data Resource: The Basis for Developing Data-Driven Hematology
Christian Brieghel1,2, Mikkel Werling1,2, Casper Møller Frederiksen1,2
1Department of Hematology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
Insights
A new Danish data resource, DALY-CARE, provides comprehensive lymphoid-cancer (LC) data for research. This enables data-driven hematology and the development of AI tools to improve patient care.
Area of Science:
- Hematology
- Oncology
- Medical Artificial Intelligence (mAI)
Background:
- Lymphoid-lineage cancers (LC) share epidemiological and clinical features, making them suitable for meta-learning in medical AI.
- Limited access to large, shared datasets hinders medical AI research in LC.
Purpose of the Study:
- To create a large-scale data repository for patients with LC to facilitate data-driven hematology research.
- To develop a comprehensive data resource for Danish LC Research (DALY-CARE).
Main Methods:
- Gathered electronic health data from 10 nationwide registers, EHR, and laboratory data for Danish adults diagnosed with LC since 2002.
- Developed open-source processing pipelines and a secure cloud-based research environment for DALY-CARE.
- Included extensive data on treatments, diagnoses, procedures, and survival for 65,774 LC patients.
Main Results:
- The DALY-CARE resource integrates diverse data types, including treatments, comorbidities, pathology, and survival data for a large LC cohort.
- Demonstrated the utility of DALY-CARE for molecular studies, real-world evidence evaluation, and mAI development.
- Established a secure research environment on a high-powered cloud computer.
Conclusions:
- The DALY-CARE data resource supports the development of real-time decision-support tools for LC patient care.
- Facilitates extrapolation of clinical trial results to clinical practice, improving LC patient outcomes.
- Streamlines health data infrastructure across different cohorts and medical specialties.
Background:
Lymphoid-lineage cancers (LC; International Classification of Diseases, 10th 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.
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