Temporal Patterns of Multiple Long-Term Conditions in Individuals with Intellectual Disability Living in Wales: An Unsupervised Clustering Approach to Disease Trajectories
This work addresses healthcare management for individuals with intellectual disabilities by uncovering specific disease trajectories, though it is incremental as it applies existing methods to a new dataset.
This study tackled the problem of identifying co-occurrence patterns of multiple long-term conditions in individuals with intellectual disabilities by applying an unsupervised clustering approach to electronic health records from 13,069 individuals in Wales, revealing distinct disease clusters such as neurological conditions dominating in males under 45 and circulatory conditions in older males.
Identifying and understanding the co-occurrence of multiple long-term conditions (MLTC) in individuals with intellectual disabilities (ID) is vital for effective healthcare management. These individuals often face earlier onset and higher prevalence of MLTCs, yet specific co-occurrence patterns remain unexplored. This study applies an unsupervised approach to characterise MLTC clusters based on shared disease trajectories using electronic health records (EHRs) from 13069 individuals with ID in Wales (2000-2021). Disease associations and temporal directionality were assessed, followed by spectral clustering to group shared trajectories. The population consisted of 52.3% males and 47.7% females, with an average of 4.5 conditions per patient. Males under 45 formed a single cluster dominated by neurological conditions (32.4%), while males above 45 had three clusters, the largest characterised circulatory (51.8%). Females under 45 formed one cluster with digestive conditions (24.6%) as most prevalent, while those aged 45 and older showed two clusters: one dominated by circulatory (34.1%), and the other by digestive (25.9%) and musculoskeletal (21.9%) system conditions. Mental illness, epilepsy, and reflux were common across groups. These clusters offer insights into disease progression in individuals with ID, informing targeted interventions and personalised healthcare strategies.