Understanding the Relationship Between Comorbidities, Medication Nonadherence, Activities of Daily Living, and Heart Condition Status Among Older Adults in the United States: A Regression Analysis and Machine Learning Approach.

IF 1.7 4区 医学 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS
Suebsarn Ruksakulpiwat, Witchuda Thongking, Naveen Kannan, Ellis Wright, Atsadaporn Niyomyart, Chitchanok Benjasirisan, Chantira Chiaranai, Christine Smothers, Heba M Aldossary, Carolyn Harmon Still
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引用次数: 0

Abstract

Background: Nonadherence to medication among patients with heart disease poses serious risks, including worsened heart failure and increased mortality rates.

Objective: This study aims to explore the complex interplay between comorbidities, medication nonadherence, activities of daily living, and heart condition status in older American adults, using both traditional statistical methods and machine learning.

Methods: Data from 326 older adults with heart conditions, drawn from the Health and Retirement Study, were analyzed. Descriptive statistics characterized demographic profiles and comorbidities, whereas logistic regression, multiple regression analyses, and decision tree models were used to address our research inquiries. In addition, a machine learning approach, specifically decision tree models, was integrated to enhance predictive accuracy.

Results: Our analysis showed that factors like age, gender, hypertension, and stroke history were significantly linked to worsening heart conditions. Notably, depression emerged as a robust predictor of medication nonadherence. Further adjusted analyses underscored significant correlations between stroke and challenges in basic activities such as dressing, bathing, and eating. Depression correlated significantly with difficulties in dressing, bed mobility, and toileting, whereas lung disease was associated with bathing hindrances. Intriguingly, our decision tree model revealed that patients experiencing dressing challenges, but not toileting difficulties, were more prone to report no improvement in heart condition status over the preceding 2 years.

Conclusions: Blending traditional statistics with machine learning in this study reveals significant implications for crafting personalized interventions to improve patients' depression, leading to increased activities of daily living, medication adherence, reduced severity of comorbidities, and ultimately better management of heart conditions.

了解美国老年人的合并症、不遵医嘱用药、日常生活活动和心脏状况之间的关系:回归分析和机器学习方法》。
背景:心脏病患者不坚持服药会带来严重风险,包括心力衰竭恶化和死亡率升高:本研究旨在利用传统统计方法和机器学习方法,探讨美国老年人的合并症、不坚持服药、日常生活活动和心脏病状况之间复杂的相互作用:方法: 分析了健康与退休研究(Health and Retirement Study)中 326 名患有心脏病的老年人的数据。描述性统计描述了人口统计学特征和合并症,而逻辑回归、多元回归分析和决策树模型则用于解决我们的研究问题。此外,我们还采用了机器学习方法,特别是决策树模型,以提高预测的准确性:结果:我们的分析表明,年龄、性别、高血压和中风史等因素与心脏状况恶化密切相关。值得注意的是,抑郁症是不遵医嘱用药的有力预测因素。进一步的调整分析显示,中风与穿衣、洗澡和进食等基本活动之间存在明显的相关性。抑郁与穿衣、床上活动和如厕困难明显相关,而肺部疾病则与洗澡障碍相关。耐人寻味的是,我们的决策树模型显示,经历过穿衣困难而非如厕困难的患者更容易报告在过去两年中心脏状况没有改善:本研究将传统统计学与机器学习相结合,对制定个性化干预措施以改善患者抑郁状况具有重要意义,可提高患者的日常生活活动能力、服药依从性、减轻合并症的严重程度,并最终改善心脏病的管理。
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来源期刊
CiteScore
3.30
自引率
10.00%
发文量
154
审稿时长
>12 weeks
期刊介绍: Official journal of the Preventive Cardiovascular Nurses Association, Journal of Cardiovascular Nursing is one of the leading journals for advanced practice nurses in cardiovascular care, providing thorough coverage of timely topics and information that is extremely practical for daily, on-the-job use. Each issue addresses the physiologic, psychologic, and social needs of cardiovascular patients and their families in a variety of environments. Regular columns include By the Bedside, Progress in Prevention, Pharmacology, Dysrhythmias, and Outcomes Research.
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