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

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 (n = 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.

Keywords: associate rules analysis; cosine index; idiopathic cardiomyopathy; multimorbidity analysis; network.

Figure 2

Participants’ selection and baseline information description. (A) performed the inclusion and exclusion process of participants;(B) the gender distribution in both selected groups; (C) the comorbid burden in each group and ICM obtained heavier burden; (D) the variation of comorbid burden among seven age groups and between gender.

Figure 1

Research Flow chart. The whole process of comorbidities analysis was displayed, including population selection, comorbidity definition, network settlement, and association with heart failure.

Figure 3

The feather of age-specific comorbidity network. (A) The variation of nodes in age-specific networks in both groups; (B) The variation of edges in age-specific networks in both groups; (C) the average of Salton cosine index in different age groups, ICM has lower SCI value than control groups in all age groups; (D) the cumulative SCI sum of central nodes in age groups; (E) the cumulative SCI sum of hub nodes in age groups; (F) the cumulative SCI sum of authority nodes in age groups, all “typical” nodes possessed stronger connectivity than the other “non-typical” nodes.

Figure 4

Comorbidity networks, central diseases, hubs, and authorities. (A) contained the network of ICM group(B) contained the network of control groups, the size of nodes represents the prevalence of diseases and width of edges stand for the correlative strength between diseases by the SCI value of each connection; (C) Central nodes in age-specific network. The central diseases were defined as the top 10 percentiles with PageRank in each network; (D) Hub nodes in age-specific network. The hub diseases were defined as the top 10 percentiles with hub value in each network;(E) Authority nodes in age-specific network. The authority diseases were defined as the top 10 percentiles with authority value in each network and have the similar effect with central nodes.