Optimizing elderly care: A data-driven AI model for predicting polypharmacy risk in the elderly using SHARE data

IF 2.9 3区医学 Q2 NEUROSCIENCES

Neuroscience Pub Date : 2025-05-05 DOI:10.1016/j.neuroscience.2025.05.004

Aliaa A. Elhosseiny , Seif Eldawlatly , Eman Ramadan , Axel Börsch-Supan , Mohamed Salama

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引用次数: 0

Abstract

Background

Aging is frequently accompanied by multimorbidity, the presence of multiple chronic conditions, which contributes to declines in both cognitive and physical function and presents complex health challenges. One such challenge is Polypharmacy (PP), defined as the concurrent use of more than five medications.

Methods

We used data from participants older than 50 years who were present in wave 6 and at least one of the subsequent three waves of the SHARE study, aiming to predict PP risk at 2, 4, and 6-year intervals. We selected the predictor variables using LASSO regression and evaluated eight ML models using a rigorous cross-validation strategy to ensure robustness and reliability.

Findings

Our analysis reveals an upward trend in PP prevalence across the surveyed countries, with aggregate figures rising from 34.03% (95% CI 33.1-34.9) in wave 7 to 36.75% (95% CI 35.6-37.9) in wave 8, reaching 39.91% (95% CI 38.9-40.9) in wave 9. LASSO regression identified 17 key predictors of PP risk, which were related to socio-demographic factors, lifestyle factors, physical and mental health, and disease history. Among the models evaluated, the Categorical Boosting ML model performed best, yielding overall accuracies of 75.08%, 73.7%, and 71.65% and recall rates of 72.83%, 70.48%, and 67.96% for the 2, 4, and 6-year intervals, respectively.

Interpretation

This study uncovers a rising trend of PP. It demonstrated the potential of using longitudinal data and ML to predict PP. Moreover, our findings suggest that mental health is an important factor to consider when addressing PP.

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优化老年人护理：一个数据驱动的AI模型，用于使用SHARE数据预测老年人多药风险。

背景：衰老通常伴随着多种疾病，多种慢性疾病的存在，导致认知和身体功能下降，并带来复杂的健康挑战。其中一个挑战是多重用药（PP），即同时使用五种以上药物。方法：我们使用的数据来自年龄大于50岁 的参与者，他们出现在第6波和SHARE研究随后的三波中的至少一波，旨在预测PP风险在2年、4年和6年的间隔。我们使用LASSO回归选择预测变量，并使用严格的交叉验证策略评估8个ML模型，以确保稳健性和可靠性。结果：我们的分析显示，在被调查的国家中，PP患病率呈上升趋势，总数字从第7波的34.03% （95% CI 33.1-34.9）上升到第8波的36.75% (95% CI 35.6-37.9)，在第9波达到39.91% （95% CI 38.9-40.9）。LASSO回归确定了17个PP风险的关键预测因素，这些因素与社会人口因素、生活方式因素、身心健康状况和疾病史有关。在评估的模型中，分类Boosting ML模型表现最好，在2年、4年和6年的时间间隔内，其总体准确率分别为75.08%、73.7%和71.65%，召回率分别为72.83%、70.48%和67.96%。解释：这项研究揭示了PP的上升趋势。它证明了使用纵向数据和ML来预测PP的潜力。此外，我们的研究结果表明，心理健康是解决PP时要考虑的一个重要因素。

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

Neuroscience 医学-神经科学

CiteScore

6.20

自引率

0.00%

发文量

394

审稿时长

52 days

期刊介绍： Neuroscience publishes papers describing the results of original research on any aspect of the scientific study of the nervous system. Any paper, however short, will be considered for publication provided that it reports significant, new and carefully confirmed findings with full experimental details.