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神经退行性疾病中的共病现象:网络分析。

Multimorbidity in neurodegenerative diseases: a network analysis.

机构信息

Department of Information Systems, College of Business, California State University, Long Beach, California, USA.

Department of Management Science and Information Systems, Spears School of Business, Oklahoma State University, Stillwater, Oklahoma, USA.

出版信息

Inform Health Soc Care. 2024 Oct;49(3-4):212-226. doi: 10.1080/17538157.2024.2405869. Epub 2024 Oct 3.

DOI:10.1080/17538157.2024.2405869
PMID:39363570
Abstract

The socioeconomic costs of neurodegenerative diseases (NDs) are highly affected by comorbidities. This study aims to enhance our understanding of the prevalent complications of NDs through the lens of network analysis. A multimorbidity network (MN) was constructed based on a longitudinal EHR dataset of 93,647,498 diagnoses of 824,847 patients. The association between the conditions was measured by two metrics, i.e. Phi-correlation and Cosine Index (CI). Based on multiple network centrality measures, a fused ranking list of the prevalent multimorbidities was provided. Finally, class-level networks depicting the prevalence and strength of diseases in different classes were constructed. The general MN included 928 diseases and 337,253 associations. Considering a 99% confidence level, two networks of 575 relationships were constructed based on Phi-correlations (73 diseases) and CI (102 diseases). Five out of 19 ICD-9 categories did not appear in either of the networks. Also, ND's immediate MNs for the top 50% of the significant associations included 42 relationships, whereas the Phi-correlation and CI networks included 36 and 34 diseases, respectively. Thirteen diseases were identified as the most notable multimorbidities based on various centrality measures. The analysis framework helps practitioners toward better resource allocations, more effective preventive screenings, and improved quality of life for ND patients and caregivers.

摘要

神经退行性疾病(NDs)的社会经济成本受到合并症的高度影响。本研究旨在通过网络分析的视角,深入了解 NDs 的常见并发症。基于 824847 名患者的 93647498 次诊断的纵向电子健康记录数据集,构建了一个多合并症网络(MN)。使用两种度量标准(Phi 相关系数和余弦指数(CI))来衡量条件之间的关联。基于多个网络中心性度量标准,提供了一个常见的多合并症的融合排名列表。最后,构建了描绘不同类别中疾病的流行率和强度的类别级网络。一般 MN 包括 928 种疾病和 337253 种关联。考虑到 99%的置信水平,根据 Phi 相关系数(73 种疾病)和 CI(102 种疾病)构建了两个包含 575 种关系的网络。19 个 ICD-9 类别中的 5 个没有出现在这两个网络中的任何一个中。此外,前 50%的显著关联的 ND 即时 MN 包括 42 种关系,而 Phi 相关系数和 CI 网络分别包括 36 种和 34 种疾病。根据各种中心性度量标准,确定了 13 种疾病作为最显著的多合并症。该分析框架有助于实践人员更好地分配资源、进行更有效的预防性筛查,并提高 ND 患者和护理人员的生活质量。

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