Comorbidity Scoring with Causal Disease Networks

Jhee, Jong Ho; Bang, Sunjoo; Lee, Dong-Gi; Shin, Hyunjung

doi:10.1109/tcbb.2018.2812886

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…A pooled analysis was performed to quantify the frequency of comorbidities and clinical signs in COVID-19 patients. Pooled data were also used to calculate the relative risk (RR) of asthma, cardiovascular and/or cerebrovascular diseases, COPD, diabetes mellitus, and hypertension by matching the frequency of these comorbidities in COVID-19 hospitalized patients with those in the general population by using highquality large epidemiological reports [19][20][21][22][23][24][25][26][27][28][29] as gold standard for each comorbidity, as previously described [30]. Data on the general population included official National reports [19][20][21][22][23][24][25][26][27][28][29] that were selected to specifically cover the same geographical area in which patients were hospitalized to COVID-19, so that in turn the subjects hospitalized to COVID-19 were themselves included in the in the above reported high-quality large epidemiological reports.…”

Section: Discussionmentioning

confidence: 99%

Reduced risk of COVID-19 hospitalization in asthmatic and COPD patients: a benefit of inhaled corticosteroids?

Rogliani

Lauro

Daniele

et al. 2020

Expert Review of Respiratory Medicine

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Background : The comorbidities and clinical signs of coronavirus disease 2019 (COVID-19) patients have been reported mainly as descriptive statistics, rather than quantitative analysis even in very large investigations. The aim of this study was to identify specific patients’ characteristics that may modulate COVID-19 hospitalization risk. Research design and methods : A pooled analysis was performed on high-quality epidemiological studies to quantify the prevalence (%) of comorbidities and clinical signs in hospitalized COVID-19 patients. Pooled data were used to calculate the relative risk (RR) of specific comorbidities by matching the frequency of comorbidities in hospitalized COVID-19 patients with those of general population. Results : The most frequent comorbidities were hypertension, diabetes mellitus, and cardiovascular and/or cerebrovascular diseases. The RR of COVID-19 hospitalization was significantly (P < 0.05) reduced in patients with asthma (0.86, 0.77–0.97) or chronic obstructive pulmonary disease (COPD) (0.46, 0.40–0.52). The most frequent clinical signs were fever and cough. Conclusion : The clinical signs of hospitalized COVID-19 patients are similar to those of other infective diseases. Patients with asthma or COPD were at lower hospitalization risk. This paradoxical evidence could be related with the protective effect of inhaled corticosteroids that are administered worldwide to most asthmatic and COPD patients.

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Section: Discussionmentioning

confidence: 99%

Reduced risk of COVID-19 hospitalization in asthmatic and COPD patients: a benefit of inhaled corticosteroids?

Rogliani

Lauro

Daniele

et al. 2020

Expert Review of Respiratory Medicine

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“…Recent developments in network medicine have led to a proliferation of studies that used complex networks for medical research to explore the comorbidity of various diseases [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. For instance, Hidalgo et al [ 29 ] used network methods to study the disease progression.…”

Section: Introductionmentioning

confidence: 99%

Phenotypic Disease Network Analysis to Identify Comorbidity Patterns in Hospitalized Patients with Ischemic Heart Disease Using Large-Scale Administrative Data

et al. 2022

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Ischemic heart disease (IHD) exhibits elevated comorbidity. However, few studies have systematically analyzed the comorbid status of IHD patients with respect to the entire spectrum of chronic diseases. This study applied network analysis to provide a complete picture of physical and mental comorbidities in hospitalized patients with IHD using large-scale administrative data. Hospital discharge records from a provincial healthcare database of IHD inpatients (n = 1,035,338) and one-to-one matched controls were included in this retrospective analysis. We constructed the phenotypic disease networks in IHD and control patients and further assessed differences in comorbidity patterns. The community detection method was applied to cluster diagnoses within the comorbidity network. Age- and sex-specific patterns of IHD comorbidities were also analyzed. IHD inpatients showed 50% larger comorbid burden when compared to controls. The IHD comorbidity network consisted of 1941 significant associations between 71 chronic conditions. Notably, the more densely connected comorbidities in IHD patients were not within the highly prevalent ones but the rarely prevalent ones. Two highly interlinked communities were detected in the IHD comorbidity network, where one included hypertension with heart and multi-organ failures, and another included cerebrovascular diseases, cerebrovascular risk factors and anxiety. Males exhibited higher comorbid burden than females, and thus more complex comorbidity relationships were found in males. Sex-specific disease pairs were detected, e.g., 106 and 30 disease pairs separately dominated in males and females. Aging accounts for the majority of comorbid burden, and the complexity of the comorbidity network increased with age. The network-based approach improves our understanding of IHD-related comorbidities and enhances the integrated management of patients with IHD.

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“…Complex networks have recently been developed for a proliferation of medical research in comorbidity pattern discovery [ 20 , 21 , 22 ]. Hidalgo et al [ 23 ] utilized a network analysis to track the development of the illness.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Jin

Zhou

et al. 2022

JCM

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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.

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Comorbidity Scoring with Causal Disease Networks

Cited by 6 publications

References 15 publications

Reduced risk of COVID-19 hospitalization in asthmatic and COPD patients: a benefit of inhaled corticosteroids?

Reduced risk of COVID-19 hospitalization in asthmatic and COPD patients: a benefit of inhaled corticosteroids?

Phenotypic Disease Network Analysis to Identify Comorbidity Patterns in Hospitalized Patients with Ischemic Heart Disease Using Large-Scale Administrative Data

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

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