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基于表型疾病网络的特发性心肌病患者合并症分析及出院记录

Phenotypic Disease Network-Based Multimorbidity Analysis in Idiopathic Cardiomyopathy Patients with Hospital Discharge Records.

作者信息

Wang Lei, Jin Ye, Zhou Jingya, Pang Cheng, Wang Yi, Zhang Shuyang

机构信息

State Key Laboratory of Complex Severe and Rare Diseases, Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.

Medical Research Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.

出版信息

J Clin Med. 2022 Nov 25;11(23):6965. doi: 10.3390/jcm11236965.

DOI:10.3390/jcm11236965
PMID:36498544
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9736397/
Abstract

BACKGROUND

Idiopathic cardiomyopathy (ICM) is a rare disease affecting numerous physiological and biomolecular systems with multimorbidity. However, due to the small sample size of uncommon diseases, the whole spectrum of chronic disease co-occurrence, especially in developing nations, has not yet been investigated. To grasp the multimorbidity pattern, we aimed to present a multidimensional model for ICM and differences among age groups.

METHODS

Hospital discharge records were collected from a rare disease centre of ICM inpatients ( = 1036) over 10 years (2012 to 2021) for this retrospective analysis. One-to-one matched controls were also included. First, by looking at the first three digits of the ICD-10 code, we concentrated on chronic illnesses with a prevalence of more than 1%. The ICM and control inpatients had a total of 71 and 69 chronic illnesses, respectively. Second, to evaluate the multimorbidity pattern in both groups, we built age-specific cosine-index-based multimorbidity networks. Third, the associated rule mining (ARM) assessed the comorbidities with heart failure for ICM, specifically.

RESULTS

The comorbidity burden of ICM was 78% larger than that of the controls. All ages were affected by the burden, although those over 50 years old had more intense interactions. Moreover, in terms of disease connectivity, central, hub, and authority diseases were concentrated in the metabolic, musculoskeletal and connective tissue, genitourinary, eye and adnexa, respiratory, and digestive systems. According to the age-specific connection, the impaired coagulation function was required for raising attention (e.g., autoimmune-attacked digestive and musculoskeletal system disorders) in young adult groups (ICM patients aged 20-49 years). For the middle-aged (50-60 years) and older (≥70 years) groups, malignant neoplasm and circulatory issues were the main confrontable problems. Finally, according to the result of ARM, the comorbidities and comorbidity patterns of heart failure include diabetes mellitus and metabolic disorder, sleeping disorder, renal failure, liver, and circulatory diseases.

CONCLUSIONS

The main cause of the comorbid load is aging. The ICM comorbidities were concentrated in the circulatory, metabolic, musculoskeletal and connective tissue, genitourinary, eye and adnexa, respiratory, and digestive systems. The network-based approach optimizes the integrated care of patients with ICM and advances our understanding of multimorbidity associated with the disease.

摘要

背景

特发性心肌病(ICM)是一种罕见疾病,会影响众多生理和生物分子系统,并伴有多种合并症。然而,由于罕见病样本量小,慢性病共病的全貌,尤其是在发展中国家,尚未得到研究。为了掌握共病模式,我们旨在提出一个针对ICM的多维模型以及不同年龄组之间的差异。

方法

收集了一家ICM罕见病中心10年(2012年至2021年)间1036例ICM住院患者的出院记录用于此次回顾性分析。还纳入了一对一匹配的对照组。首先,通过查看ICD - 10编码的前三位数字,我们聚焦于患病率超过1%的慢性病。ICM组和对照组住院患者分别共有71种和69种慢性病。其次,为了评估两组的共病模式,我们构建了基于年龄特异性余弦指数的共病网络。第三,关联规则挖掘(ARM)专门评估了ICM患者心力衰竭的合并症。

结果

ICM的共病负担比对照组大78%。所有年龄段都受到共病负担的影响,不过50岁以上人群的相互作用更为强烈。此外,在疾病关联性方面,中心性、枢纽性和权威性疾病集中在代谢、肌肉骨骼和结缔组织、泌尿生殖、眼和附属器、呼吸及消化系统。根据年龄特异性关联,年轻成年组(20 - 49岁的ICM患者)需要关注凝血功能受损(例如,自身免疫攻击导致的消化系统和肌肉骨骼系统疾病)。对于中年(50 - 60岁)和老年(≥70岁)组,恶性肿瘤和循环系统问题是主要面临的问题。最后,根据ARM结果,心力衰竭的合并症及共病模式包括糖尿病和代谢紊乱、睡眠障碍、肾衰竭、肝脏疾病和循环系统疾病。

结论

共病负担的主要原因是衰老。ICM的合并症集中在循环、代谢、肌肉骨骼和结缔组织、泌尿生殖、眼和附属器、呼吸及消化系统。基于网络的方法优化了ICM患者的综合护理,并增进了我们对与该疾病相关的共病的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c60/9736397/91315c7b0965/jcm-11-06965-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c60/9736397/922678036c50/jcm-11-06965-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c60/9736397/922e63cc9e8e/jcm-11-06965-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c60/9736397/eb0be3b7e7bc/jcm-11-06965-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c60/9736397/91315c7b0965/jcm-11-06965-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c60/9736397/922678036c50/jcm-11-06965-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c60/9736397/922e63cc9e8e/jcm-11-06965-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c60/9736397/eb0be3b7e7bc/jcm-11-06965-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c60/9736397/91315c7b0965/jcm-11-06965-g004.jpg

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