Characterizing patients at higher cardiovascular risk for prescribed stimulants: Learning from health records data with predictive analytics and data mining techniques

IF 6.3 2区医学 Q1 BIOLOGY

Computers in biology and medicine Pub Date : 2025-02-20 DOI:10.1016/j.compbiomed.2025.109870

Yifang Yan , Qiushi Chen , Rafay Nasir , Paul Griffin , Curtis Bone , Wen-Jan Tuan

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引用次数: 0

Abstract

Objective

Given 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.

Methods

Using 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.

Results

The 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 conclusion

We 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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表征服用处方兴奋剂的心血管风险较高的患者：利用预测分析和数据挖掘技术从健康记录数据中学习

鉴于在过去十年中服用兴奋剂的个体数量显著增加，人们越来越关注成人服用兴奋剂治疗后心血管事件的风险。我们的目的是量化处方兴奋剂使用增加的心血管事件风险，并描述不良影响的患者特征。方法使用来自TriNetX研究网络的2010-2020年患有注意力缺陷/多动障碍的成年人的电子健康记录，我们开发并比较了不同的机器学习模型，以基于个人处方兴奋剂使用、人口统计学和四个不同年龄组的合并症来预测一年的心血管风险。通过训练的风险预测模型，我们估计了心血管事件的增加风险，并利用关联规则挖掘（ARM）来识别处方兴奋剂使用对患者产生不利影响的临床特征。结果该研究队列包括219,965名成年人，其中102,138人（46.4%）接受兴奋剂治疗。在所有年龄组中，所有预测模型在受试者工作特征曲线下均达到0.77-0.84的高区域。在风险最高增加25%的患者中，ARM确定了主要类别的关键特征，包括心血管事件的常见危险因素、既往心血管事件、物质使用障碍和心理障碍。构建并验证了每个年龄组的合并症观察清单，以显示对这些情况的患者开兴奋剂处方的风险增加。讨论与结论我们将预测建模和数据挖掘相结合来描述处方兴奋剂使用对患者的不良影响。未来的研究需要从外部验证已识别的特征，以指导更安全的兴奋剂处方。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers in biology and medicine 工程技术-工程：生物医学

CiteScore

11.70

自引率

10.40%

发文量

1086

审稿时长

74 days

期刊介绍： Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.