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

IF 3.4 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Clinical Epidemiology Pub Date : 2025-02-20 eCollection Date: 2025-01-01 DOI:10.2147/CLEP.S479672

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

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引用次数: 0

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.

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丹麦淋巴癌研究（DALY-CARE）数据资源：发展数据驱动血液学的基础。

背景：淋巴系癌症(LC；国际疾病分类第十版[ICD10] C81.x-C90。x, C91.1-C91.9, C95.1, C95.7, C95.9, D47.2, D47.9B和E85.8A)具有许多流行病学和临床特征，在开发医疗人工智能（mAI）时有利于元学习。然而，对大型共享数据集的访问在很大程度上是缺失的，这限制了mAI的研究。目的：为LC患者创建一个大规模的数据存储库，以发展数据驱动的血液学。方法：我们收集电子健康数据并创建开源处理管道，为丹麦LC研究（DALY-CARE）创建一个全面的数据资源，该资源被批准用于流行病学、分子和数据驱动的研究。结果：我们纳入了自2002年以来登记为LC诊断的所有丹麦成年人（n=65,774），并将10个全国登记册、电子健康记录（EHR）和高性能云计算机上的实验室数据结合起来，以建立一个安全的研究环境。其中包括治疗（即21,750个细胞减少治疗方案，21.3万个门诊处方和12.7万个住院管理）、生化分析（77.3万个）、合并症（148万个ICD10代码）、病理代码（4.5万个）、治疗程序（8.3万个）、手术程序（1.0万个）、放射检查（3.3万个）、生命体征（18.3万个值）和生存数据。我们在此描述了数据基础设施，并举例说明了DALY-CARE如何用于分子研究、真实世界的证据来评估护理的有效性，以及mAI如何直接部署到EHR系统中。结论：DALY-CARE数据资源允许开发接近实时的决策支持工具，并将临床试验结果外推到临床实践中，从而改善对LC患者的护理，同时促进跨队列和医学专业的健康数据基础设施的简化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Epidemiology Medicine-Epidemiology

CiteScore

6.30

自引率

5.10%

发文量

169

审稿时长

16 weeks

期刊介绍： Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment. Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews. Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews. When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes. The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.