Network analysis of comorbidity patterns in Heart Failure patients using administrative data.

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. The aim of this paper is to show the potential and the usefulness of Network models when applied to the analysis of comorbidity patterns in HF, as a new methodological tool to be considered within the epidemiological investigation of this complex disease. 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, hypertension, arrythmia, renal and pulmonary diseases as the most relevant nodes related to death, in terms of prevalence and closeness/strength of the relationship. Moreover, some relevant clusters of nodes have been identified in all the cohorts, i.e. those related to cancer, lung diseases liver diseases and heart/circulation related problems. It seems that such patterns do not evolve along time (i.e., nor indexes of relevance computed on the nodes of the networks neither communities change significantly from one year/cohort to another), featuring HF comorbidity burden as stable over the years. 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.