ArticleComputers in biology and medicine2025
Characterizing patients at higher cardiovascular risk for prescribed stimulants: Learning from health records data with predictive analytics and data mining techniques.
Article in Computers in biology and medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveGiven the significantly increased number of individuals prescribed stimulants in the past decade, there has been growing concern regarding the risk of cardiovascular events among adults on stimulant therapy. We aimed to quantify the added risk of cardiovascular events by prescription stimulant use and characterize patients who were adversely affected.
methodsUsing electronic health records of adults with Attention-Deficit/Hyperactivity Disorder from TriNetX Research Network in 2010-2020, we developed and compared different machine learning models to predict one-year cardiovascular risk based on individual's prescription stimulant use, demographics, and comorbidities for four separate age groups. With the trained risk prediction models, we estimated added risk of cardiovascular events and utilized association rule mining (ARM) to identify clinical characteristics of patients adversely affected by prescription stimulant use.
resultsThe study cohort consisted of 219,965 adults, including 102,138 (46.4 %) persons on stimulant therapy. All prediction models achieved high areas under receiver operating characteristic curve of 0.77-0.84 in predicting one-year cardiovascular risk across all age groups. Of patients with 25 % highest added risks, ARM identified critical features in major categories including common risk factors of cardiovascular events, prior cardiovascular events, substance use disorders, and psychological disorders. A watch list of comorbidities was constructed and validated for each age group to show added risk of prescribing stimulants to patients with these conditions. DISCUSSION AND
conclusionWe integrated predictive modeling and data mining to characterize patients adversely affected by prescription stimulant use. Future research is needed to externally validate identified features to guide safer stimulant prescribing.
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