Improving Stroke Risk Prediction in the General Population: A Comparative Assessment of Common Clinical Rules, a New Multimorbid Index, and Machine-Learning-Based Algorithms

Abstract

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 US cohort of 3435224 patients from medical databases in a 2-year investigation. Stroke outcomes were examined in relationship to diverse multi-morbid conditions, demographic variables and other inputs, with ML accounting for the dynamic nature of changing multimorbidity risk factors, 2 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; CHA2DS2-VASc: c index 0.809). A clinical multimorbid index had higher discriminant validity values for both the training/external validation samples (validation: c-index 0.850). The machine learning (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). 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 best. 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.

A New Paradigm of “Real-Time” Stroke Risk Prediction and Integrated Care Management in the Digital Health Era: Innovations Using Machine Learning and Artificial Intelligence Approaches

“…In this issue of Thrombosis and Haemostasis, Lip et al report on stroke risk prediction, using two common clinical rules (CHADS 2 , CHA 2 DS 2 -VASc scores), a clinical multimorbid index and a ML approach accounting for the complex relationships among variables, using a prospective U.S. cohort of 3,435,224 patients from medical databases. 27 This is a first large-scale investigation, with respect to the progressive risk factors for stroke, the difference between traditional statistical methods and ML-based algorithms in predicting stroke risk, together with the comparison of different AI ML approaches. The authors found that a clinical multimorbid index had higher discriminant validity values than common clinical rules, perhaps unsurprisingly given that more clinical variables were used.…”

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“…Integrating the ever-expanding information available, sifting through the risk categories and defining the risk trajectory for AFACS is becoming more of a challenge for a single individual and more appropriate for a clinical decision support system based on machine learning, especially given new advances in the latter for the prediction of AF and stroke. 28 Conflict of Interest None declared.…”

Thrombosis and Haemostasis 2021 Editors' Choice Papers

“…54 Machine-learning approaches which account for risk factors' dynamic nature and multimorbidity may improve clinical stroke risk assessment over clinical scores, as demonstrated by a comparative study based on a large real-world data set. 55 Jones et al investigated the economic aspects of service interventions and put into light the cost-effectiveness of anticoagulation clinics, in particular targeting patients at high-risk and/or with suboptimal treatment, 56 a subject less often studied but nonetheless crucial in improving management praxis. While we require to acknowledge the dynamics of stroke risks and implement management flexibility accordingly, identifying specific groups of patients whose risks and benefits need to be weighted, also determines the success of anticoagulation strategies.…”