Machine learning identifies clusters of multimorbidity among decedents with inflammatory bowel disease

Background — Multimorbidity is the co-occurrence of two or more chronic conditions in one person. Providing quality, patient-centered care requires understanding multimorbidity. Our objective was to identify patterns of multimorbidity that occur prior to death among people with inflammatory bowel disease (IBD).

Methods — Using a retrospective population-based matched cohort derived from linked health administrative data of individuals with and without IBD who died between 2010 and 2020 in Ontario, Canada, we compared multimorbidity accumulation and leveraged unsupervised machine learning to identify multimorbidity clusters.

Results — Here we show decedents with IBD have a greater prevalence of complex multimorbidity (42% vs 34% with 8+ conditions, standardized difference: 22%). Among those with IBD at death, IBD is commonly developed as their first condition. At death, people with IBD have high prevalences of osteo- and other arthritis (77%), hypertension (73%), mood disorders (69%), renal failure (50%) and cancer (46%). Among those with IBD, we identify 3 clusters: (α) mood disorder and/or osteo- and other arthritis; (β) cancer and low multimorbidity; ($\gamma$) cardiovascular comorbidities. The clusters that we identify are stable across numerous validation techniques, including re-derivation and sex-specific clustering.

Conclusions — These findings can inform future research and potential multimorbidity care in populations with IBD. Consideration of these clusters also lends to the need for further research, guidelines, and care programs to manage distinct subgroups of comorbidities among those with IBD, highlighting avenues for greater personalized care.