Ranking sets of morbidities using hypergraph centrality

• DOI: 10.1016/j.jbi.2021.103916
• Publication Date: 2021-09-15T02:35:43Z
• Authors: James Rafferty; Alan Watkins; Jane Lyons; Ronan A. Lyons; Ashley Akbari; Niels Peek; Farideh Jalali-na...; Thamer Ba Dhafari; Alexander Pate; Glen P. Martin; Rowena Bailey
• Keywords: Adult; Multi-morbidity; 3. Good health; Cohort Studies; 03 medical and health sciences; 0302 clinical medicine; Hypergraph; Chronic Disease; Diabetes Mellitus; Electronic Health Records; Humans; Network analysis; Morbidity; Original Research

Multi-morbidity, the health state of having two or more concurrent chronic conditions, is becoming more common as populations age, but is poorly understood. Identifying and understanding commonly occurring sets of diseases is important to inform clinical decisions to improve patient services and outcomes. Network analysis has been previously used to investigate multi-morbidity, but a classic application only allows for information on binary sets of diseases to contribute to the graph. We propose the use of hypergraphs, which allows for the incorporation of data on people with any number of conditions, and also allows us to obtain a quantitative understanding of the centrality, a measure of how well connected items in the network are to each other, of both single diseases and sets of conditions. Using this framework we illustrate its application with the set of conditions described in the Charlson morbidity index using data extracted from routinely collected population-scale, patient level electronic health records (EHR) for a cohort of adults in Wales, UK. Stroke and diabetes were found to be the most central single conditions. Sets of diseases featuring diabetes; diabetes with Chronic Pulmonary Disease, Renal Disease, Congestive Heart Failure and Cancer were the most central pairs of diseases. We investigated the differences between results obtained from the hypergraph and a classic binary graph and found that the centrality of diseases such as paraplegia, which are connected strongly to a single other disease is exaggerated in binary graphs compared to hypergraphs. The measure of centrality is derived from the weighting metrics calculated for disease sets and further investigation is needed to better understand the effect of the metric used in identifying the clinical significance and ranked centrality of grouped diseases. These initial results indicate that hypergraphs can be used as a valuable tool for analysing previously poorly understood relationships and information available in EHR data.