Abstract
Background There are few large studies examining and predicting the diversified cardiovascular/noncardiovascular
comorbidity relationships with stroke. We investigated stroke risks in a very large
prospective cohort of patients with multimorbidity, using two common clinical rules,
a clinical multimorbid index and a machine-learning (ML) approach, accounting for
the complex relationships among variables, including the dynamic nature of changing
risk factors.
Methods We studied a prospective U.S. cohort of 3,435,224 patients from medical databases
in a 2-year investigation. Stroke outcomes were examined in relationship to diverse
multimorbid conditions, demographic variables, and other inputs, with ML accounting
for the dynamic nature of changing multimorbidity risk factors, two clinical risk
scores, and a clinical multimorbid index.
Results Common clinical risk scores had moderate and comparable c indices with stroke outcomes
in the training and external validation samples (validation—CHADS2 : c index 0.812, 95% confidence interval [CI] 0.808–0.815; CHA2 DS2 -VASc: c index 0.809, 95% CI 0.805–0.812). A clinical multimorbid index had higher
discriminant validity values for both the training/external validation samples (validation:
c index 0.850, 95% CI 0.847–0.853). The ML-based algorithms yielded the highest discriminant
validity values for the gradient boosting/neural network logistic regression formulations
with no significant differences among the ML approaches (validation for logistic regression:
c index 0.866, 95% CI 0.856–0.876). Calibration of the ML-based formulation was satisfactory
across a wide range of predicted probabilities. Decision curve analysis demonstrated
that clinical utility for the ML-based formulation was better than that for the two
current clinical rules and the newly developed multimorbid tool. Also, ML models and
clinical stroke risk scores were more clinically useful than the “treat all” strategy.
Conclusion Complex relationships of various comorbidities uncovered using a ML approach for
diverse (and dynamic) multimorbidity changes have major consequences for stroke risk
prediction. This approach may facilitate automated approaches for dynamic risk stratification
in the significant presence of multimorbidity, helping in the decision-making process
for risk assessment and integrated/holistic management.
Keywords machine learning - cardiovascular/noncardiovascular multimorbidity - stroke
