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

Abstract

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.

Figure 1:

Overview of prediction pipeline and models used across prediction tasks. We specified 3 modelling tasks for predicting premature death among people with inflammatory bowel disease (IBD). We then used 3 types of models, namely logistic regression, random forest, and Extreme Gradient Boosting (XGBoost); XGBoost was the only model used for task 3 as it enabled direct modelling of missing data (those without conditions would have missing data). See Related Content for accessible version. Note: CCS = chronic coronary syndrome, CHF = congestive heart failure, COPD = chronic obstructive pulmonary disease, ED = emergency department, HTN = hypertension, MI = myocardial infarction, RA = rheumatoid arthritis.

Figure 2:

Feature importance for predicting premature death from (A) presence or absence of all chronic conditions (Extreme Gradient Boosting 1 [XGB1] model), (B) presence or absence of chronic conditions developed at or before age 60 years (XGB2 model), and (C) age of chronic conditions developed at or before age 60 years (XGB3 model), as denoted by the Shapley Additive Explanations (SHAP) analysis. Each dot represents a single patient in the data set. The X axis shows SHAP values, which quantify the impact of each predictor variable’s value on the model’s output. Positive SHAP values indicate a higher likelihood of premature death, while negative values indicate a lower likelihood. Colours represent the variable values for each condition across each patient in the data set. In subplots A and B, red values indicate the condition was present, while blue values indicate that the condition was absent. In subplot C, red corresponds to an older age of condition diagnosis (up to age 60 yr) and blue corresponds to younger age. In all panels, sex is distinguished such that red indicates males and blue indicates females. Grey values represent patients with missing values (e.g., no diagnosis of condition). The correlation between blue dots and positive SHAP values indicates that younger ages of diagnosis were primarily used by the model to predict premature death. The vertical dispersion of points for each feature reflects variability in its impact across patients. This visualization helps identify key predictors and their relative influence on model decisions. See Related Content for accessible version. Note: COPD: chronic obstructive pulmonary disease.