Machine learning prediction of premature death from multimorbidity among people with inflammatory bowel disease: a population-based retrospective cohort study

Background: Multimorbidity, the co-occurrence of 2 or more chronic conditions, is important in patients with inflammatory bowel disease (IBD) given its association with complex care plans, poor health outcomes, and excess mortality. Our objectives were to describe premature death (age < 75 yr) among people with IBD and to identify patterns between multimorbidity and premature death among decedents with IBD. Methods: Using the administrative health data of people with IBD who died between 2010 and 2020 in Ontario, Canada, we conducted a population-based, retrospective cohort study. We described the proportion of premature deaths among people with IBD. We developed statistical and machine learning models to predict premature death from the presence of 17 chronic conditions and the patients' age at diagnosis. We evaluated models using accuracy, positive predictive value, sensitivity, F1 scores, area under the receiver operating curve (AUC), calibration plots, and explainability plots. Results: All models showed strong performance (AUC 0.81-0.95). The best performing was the model that incorporated age at diagnosis for each chronic condition developed at or before age 60 years (AUC 0.95, 95% confidence interval 0.94-0.96). Salient features for predicting premature death were young ages of diagnosis for mood disorder, osteo-and other arthritis types, other mental health disorders, and hypertension, as well as male sex. Interpretation: By comparing results from multiple approaches modelling the impact of chronic conditions on premature death among people with IBD, we showed that conditions developed early in life (age <= 60 yr) and their age of onset were important for predicting their health trajectory. Clinically, our findings emphasize the need for models of care that ensure people with IBD have access to high-quality, multidisciplinary health care.