Network analysis of comorbidity patterns in heart failure patients using administrative data

Abstract

Background: Congestive Heart Failure (HF) is a widespread chronic disease characterized by a very high incidence in elder people. The high mortality and readmission rate of HF strongly depends on the complicated morbidity scenario often characterising it. Methods: Data were retrieved from the healthcare administrative datawarehouse of Lombardy, the most populated regional district in Italy. Network analysis techniques and community detection algorithms are applied to comorbidities registered in hospital discharge papers of HF patients, in 7 cohorts between 2006 and 2012.Results: The relevance network indexes applied to the 7 cohorts identified death, ipertension, arrythmia, renal and pulmonary diseases as the most relevant nodes related to HF, in terms of prevalence and closeness/strenght of the relationship. Moreover, 3 clusters of nodes have been identified in all the cohorts, i.e. those related to cancer, lung diseases and heart/circulation related problems.Conclusions: Network analysis can be a useful tool in epidemiologic framework when relational data are the objective of the investigation, since it allows to visualize and make inference on patterns of association among nodes (here HF comorbidities) by means of both qualitative indexes and clustering techniques.