Network Analysis of Autopsy Diagnoses: Insights into the “Cause of Death” from Unbiased Disease Clustering
Year: 2018
Type: article
Abstract: BACKGROUND: Autopsies usually serve to inform specific "causes of death" and associated mechanisms. However, multiple diseases can co-exist and interact leading to a final demise. We approached autopsy-produced data using network analysis in an unbiased fashion to inform about interaction among different diseases and identify possible targets of system-level health care. METHODS: < 0.01) were selected to construct a network in which each disease is represented by a node, and each link between the nodes represents significant co-occurrence. RESULTS: The network comprised 140 diseases connected by 419 links. The mean number of connections per node was 6. The most highly connected nodes ("hubs") represented infectious processes, whereas less connected nodes represented neoplasms and other chronic diseases. Eight clusters of biologically plausible associated diseases were identified. CONCLUSIONS: There is an unbiased relationship among autopsy-identified diseases. There were "hubs" (primarily infectious) with significantly more associations than others that could represent obligatory or important modulators of the final expression of other diseases. Clusters of co-occurring diseases, or "modules," suggest the presence of clinically relevant presentations of pathobiologically related entities which are until now considered individual diseases. These modules may occur together prior to death and be amenable to interventions during life.
Source: Journal of Pathology Informatics
Institutions: Yale University, Brigham and Women's Hospital, Greenwich Hospital
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,070
OpenAlex ID: W2896384271