Gregory Y H Lip1, Ash Genaidy2, George Tran3, Patricia Marroquin2, Cara Estes2, Sue Sloop2. 1. Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom. 2. Anthem Inc., Indianapolis, Indiana, United States. 3. IngenioRX, Indianapolis, Indiana, United States.
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; CHA2DS2-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. Thieme. All rights reserved.
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; CHA2DS2-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. Thieme. All rights reserved.