Multimorbidity networks of chronic obstructive pulmonary disease and heart failure in men and women: Evidence from the EpiChron Cohort

Carmona-Pírez, Jonás; Poblador‐Plou, Beatriz; Díez-Manglano, Jesús; Morillo-Jiménez, Manuel Jesús; Trigo, José María Marín; Ioakeim-Skoufa, Ignatios; Gimeno‐Miguel, Antonio; Prados‐Torres, Alexandra

doi:10.1016/j.mad.2020.111392

Cited by 15 publications

(15 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The network's modularity was used to search for communities of patients within each network using the Louvain method 38 , as previously used in comorbidity and multimorbidity pattern studies 18 , 19 . Modularity calculates the density of links inside communities compared to the links between them 38 .…”

Section: Methodsmentioning

confidence: 99%

“…At the same time, it only included 7.58% of all possible combinations between patients (14,501,518 out of 191,198,218 possible combinations), saving computation memory. The network's modularity was used to search for communities of patients within each network using the Louvain method 38 , as previously used in comorbidity and multimorbidity pattern studies 18,19 . Modularity calculates the density of links inside communities compared to the links between them 38 .…”

Section: Study Variables and Data Sourcesmentioning

confidence: 99%

“…In this context, innovative data-mapping tools, such as network analysis, could help detect patterns from within the wide range of conditions and their potential combinations, discriminating between different high-risk profiles of infected individuals based on their underlying comorbidities. Network analysis applies community detection methods and has been used to study multimorbidity patterns through the correlations among diseases 18 , 19 . However, this is the first time to the extent of our knowledge that this method is applied to study the correlations between individuals based on common comorbidities, and more specifically in COVID-19 patients, in order to detect higher-risk severity profiles.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identifying multimorbidity profiles associated with COVID-19 severity in chronic patients using network analysis in the PRECOVID Study

Carmona-Pírez

Gimeno‐Miguel

Bliek-Bueno

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

A major risk factor of COVID-19 severity is the patient's health status at the time of the infection. Numerous studies focused on specific chronic diseases and identified conditions, mainly cardiovascular ones, associated with poor prognosis. However, chronic diseases tend to cluster into patterns, each with its particular repercussions on the clinical outcome of infected patients. Network analysis in our population revealed that not all cardiovascular patterns have the same risk of COVID-19 hospitalization or mortality and that this risk depends on the pattern of multimorbidity, besides age and sex. We evidenced that negative outcomes were strongly related to patterns in which diabetes and obesity stood out in older women and men, respectively. In younger adults, anxiety was another disease that increased the risk of severity, most notably when combined with menstrual disorders in women or atopic dermatitis in men. These results have relevant implications for organizational, preventive, and clinical actions to help meet the needs of COVID-19 patients.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Study Variables and Data Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying multimorbidity profiles associated with COVID-19 severity in chronic patients using network analysis in the PRECOVID Study

Carmona-Pírez

Gimeno‐Miguel

Bliek-Bueno

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…The patients were stratified by age and gender. The most common conditions were cardiovascular and metabolic diseases as in Lithuanian data; dyslipidemia does not cluster with cardiovascular diseases [ 17 , 56 ]. The Lithuanian data revealed similar clustering of the most prevalent comorbidities.…”

Section: Discussionmentioning

confidence: 99%

Epidemiology of Chronic Obstructive Pulmonary Disease (COPD) Comorbidities in Lithuanian National Database: A Cluster Analysis

Jurevičienė

Burneikaitė

Dambrauskas

et al. 2022

IJERPH

View full text Add to dashboard Cite

Various comorbidities and multimorbidity frequently occur in chronic obstructive pulmonary disease (COPD), leading to the overload of health care systems and increased mortality. We aimed to assess the impact of COPD on the probability and clustering of comorbidities. The cross-sectional analysis of the nationwide Lithuanian database was performed based on the entries of the codes of chronic diseases. COPD was defined on the code J44.8 entry and six-month consumption of bronchodilators. Descriptive statistics and odds ratios (ORs) for associations and agglomerative hierarchical clustering were carried out. 321,297 patients aged 40–79 years were included; 4834 of them had COPD. A significantly higher prevalence of cardiovascular diseases (CVD), lung cancer, kidney diseases, and the association of COPD with six-fold higher odds of lung cancer (OR 6.66; p < 0.0001), a two-fold of heart failure (OR 2.61; p < 0.0001), and CVD (OR 1.83; p < 0.0001) was found. Six clusters in COPD males and five in females were pointed out, in patients without COPD—five and four clusters accordingly. The most prevalent cardiovascular cluster had no significant difference according to sex or COPD presence, but a different linkage of dyslipidemia was found. The study raises the need to elaborate adjusted multimorbidity case management and screening tools enabling better outcomes.

show abstract

“…When clinical researchers decreased the minimum confidence and support values to include diseases with low prevalence, the number of combinations increased, and the model could not get the results. To address this challenge, other types of associative methods, such as factor, cluster and network analysis [ 25 , 26 , 27 ] could be explored and implemented in future works.…”

Section: Discussionmentioning

confidence: 99%

Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm

Carmona-Pírez

Poblador‐Plou

Poncel-Falcó

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

The current availability of electronic health records represents an excellent research opportunity on multimorbidity, one of the most relevant public health problems nowadays. However, it also poses a methodological challenge due to the current lack of tools to access, harmonize and reuse research datasets. In FAIR4Health, a European Horizon 2020 project, a workflow to implement the FAIR (findability, accessibility, interoperability and reusability) principles on health datasets was developed, as well as two tools aimed at facilitating the transformation of raw datasets into FAIR ones and the preservation of data privacy. As part of this project, we conducted a multicentric retrospective observational study to apply the aforementioned FAIR implementation workflow and tools to five European health datasets for research on multimorbidity. We applied a federated frequent pattern growth association algorithm to identify the most frequent combinations of chronic diseases and their association with mortality risk. We identified several multimorbidity patterns clinically plausible and consistent with the bibliography, some of which were strongly associated with mortality. Our results show the usefulness of the solution developed in FAIR4Health to overcome the difficulties in data management and highlight the importance of implementing a FAIR data policy to accelerate responsible health research.

show abstract

Multimorbidity networks of chronic obstructive pulmonary disease and heart failure in men and women: Evidence from the EpiChron Cohort

Cited by 15 publications

References 47 publications

Identifying multimorbidity profiles associated with COVID-19 severity in chronic patients using network analysis in the PRECOVID Study

Identifying multimorbidity profiles associated with COVID-19 severity in chronic patients using network analysis in the PRECOVID Study

Epidemiology of Chronic Obstructive Pulmonary Disease (COPD) Comorbidities in Lithuanian National Database: A Cluster Analysis

Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm

Contact Info

Product

Resources

About