开发并验证弗洛里痴呆症风险评分网络工具，以便在初级保健中筛查阿尔茨海默氏症。

IF 9.6 1区医学 Q1 MEDICINE, GENERAL & INTERNAL EClinicalMedicine Pub Date : 2024-09-17 eCollection Date: 2024-10-01 DOI:10.1016/j.eclinm.2024.102834

Yijun Pan, Chenyin Chu, Yifei Wang, Yihan Wang, Guangyan Ji, Colin L Masters, Benjamin Goudey, Liang Jin

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引用次数: 0

摘要

背景：据估计，60%的阿尔茨海默病（AD）患者未被发现或诊断，在医疗资源有限的中低收入地区，诊断不足率更高。为了促进健康公平，我们开发了一种基于网络的工具，利用易于收集的临床数据来提高基层医疗机构的阿兹海默症检出率：本研究利用了从澳大利亚成像、生物标记和生活方式（AIBL）研究和宗教团体研究与记忆和老龄化项目（ROSMAP）参与者那里收集到的数据。研究包括三个阶段：(1) 在回顾性队列数据（1407 名 AIBL 参与者）上构建和评估模型；(2) 进行模拟试验以评估模型的准确性（30 名 AIBL 参与者）和数据缺失的可容忍性（30 名 AIBL 参与者）；(3) 使用非澳大利亚数据集（500 名 ROSMAP 参与者）进行外部评估。采用自动评分机器学习算法开发了弗洛里痴呆风险评分（FDRS）。所有模拟试验和评估均使用基于网络的 FDRS 工具进行：FDRS的曲线下面积（AUC）约为0.82 [95% CI, 0.75-0.88]，灵敏度为0.74 [0.60-0.86]，特异度为0.73 [0.70-0.79]。模拟试验对 30 名有完整记录的 AIBL 参与者的准确率为 87%（26/30 正确），而对另外 30 名有一两个数据缺失的 AIBL 参与者的准确率仅略有下降（80.0-83.3%，取决于估算方法）。在 500 名 ROSMAP 参与者中，FDRS 的 AUC 为 0.82 [0.77-0.86]：FDRS工具为基层医疗机构的AD筛查提供了一种潜在的低成本解决方案。本研究表明，未来有必要对FDRS进行优化试验，并确认其在更多样化人群中的通用性，尤其是低收入国家的人群：经费来源：澳大利亚国家健康与医学研究委员会（GNT2007912）和美国阿尔茨海默氏症协会（23AARF-1020292）。

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Development and validation of the Florey Dementia Risk Score web-based tool to screen for Alzheimer's disease in primary care.

Background: It is estimated that ∼60% of people with Alzheimer's disease (AD) are undetected or undiagnosed, with higher rates of underdiagnosis in low-to middle-income areas with limited medical resources. To promote health equity, we have developed a web-based tool that utilizes easy-to-collect clinical data to enhance AD detection rate in primary care settings.

Methods: This study was leveraged on the data collected from participants of the Australian Imaging, Biomarker & Lifestyle (AIBL) study and the Religious Orders Study and Memory and Aging Project (ROSMAP). The study included three phases: (1) constructing and evaluating a model on retrospective cohort data (1407 AIBL participants), (2) performing simulated trials to assess model accuracy (30 AIBL participants) and missing data tolerability (30 AIBL participants), and (3) external evaluation using a non-Australian dataset (500 ROSMAP participants). The auto-score machine learning algorithm was employed to develop the Florey Dementia Risk Score (FDRS). All the simulated trials and evaluation were performed using a web-based FDRS tool.

Findings: FDRS achieved an area under the curve (AUC) of approximately 0.82 [95% CI, 0.75-0.88], with a sensitivity of 0.74 [0.60-0.86] and a specificity of 0.73 [0.70-0.79]. The accuracy of the simulated pilot trial for 30 AIBL participants with complete record was 87% (26/30 correct), while it only slightly decreased (80.0-83.3%, depending on imputation methods) for another 30 AIBL participants with one or two missing data. FDRS achieved an AUC of 0.82 [0.77-0.86] of 500 ROSMAP participants.

Interpretation: The FDRS tool offers a potential low-cost solution to AD screening in primary care. The present study warrants future trials of FDRS for optimization and to confirm its generalizability across a more diverse population, especially people in low-income countries.

Funding: National Health and Medical Research Council, Australia (GNT2007912) and Alzheimer's Association, USA (23AARF-1020292).

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来源期刊

EClinicalMedicine Medicine-Medicine (all)

CiteScore

18.90

自引率

1.30%

发文量

506

审稿时长

22 days

期刊介绍： eClinicalMedicine is a gold open-access clinical journal designed to support frontline health professionals in addressing the complex and rapid health transitions affecting societies globally. The journal aims to assist practitioners in overcoming healthcare challenges across diverse communities, spanning diagnosis, treatment, prevention, and health promotion. Integrating disciplines from various specialties and life stages, it seeks to enhance health systems as fundamental institutions within societies. With a forward-thinking approach, eClinicalMedicine aims to redefine the future of healthcare.