针对最常见老年疾病的单一风险评估,在 10 项队列研究基础上开发并验证。

IF 7 1区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Md Hamidul Huque, Scherazad Kootar, Kim M Kiely, Craig S Anderson, Martin van Boxtel, Henry Brodaty, Perminder S Sachdev, Michelle Carlson, Annette L Fitzpatrick, Rachel A Whitmer, Miia Kivipelto, Louisa Jorm, Sebastian Köhler, Nicola T Lautenschlager, Oscar L Lopez, Jonathan E Shaw, Fiona E Matthews, Ruth Peters, Kaarin J Anstey
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引用次数: 0

摘要

背景:我们的目的是利用共同的风险因素,为年龄≥ 65 岁的成年人开发痴呆、中风、心肌梗死(MI)和糖尿病的风险工具:数据来自 10 个基于人群的队列(N = 41,755 人),痴呆、中风、心肌梗死和糖尿病的中位随访时间(年)分别为 6.2、7.0、6.8 和 7.4。研究人员纳入了基线无疾病的参与者,并评估了 22 个风险因素(社会人口学、医疗、生活方式、实验室生物标志物)。开发并验证了两种风险工具(DemNCD 和 DemNCD-LR,分别基于 Fine 和 Gray 子分布和逻辑回归 [LR])。这些风险工具的预测准确性采用哈雷尔 C 统计量、曲线下面积(AUC)和 95% 置信区间(CI)进行评估。使用 Hosmer-Lemeshow 拟合度检验和校准图进行模型校准:结果:DemNCD 和 DemNCD-LR 对每种结果都具有相似的预测准确性。男性痴呆、中风、心肌梗死和糖尿病风险工具的总体AUC(95% CI)分别为0-68(0-65,0-70)、0-58(0-54,0-61)、0-65(0-61,0-68)和0-68(0-64,0-72)。女性的这些数字分别为 0-65(0-63,0-67)、0-55(0-52,0-57)、0-65(0-62,0-68)和 0-61(0-57,0-65):DemNCD是首个利用综合风险因素预测痴呆症和多种心脑代谢疾病的工具,其预测准确性与现有的风险工具相似。它与针对该年龄组单一结果设计的工具具有相似的预测准确性。由于 DemNCD 包括自我报告和常规可用的临床措施,因此有可能在社区和临床环境中使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A single risk assessment for the most common diseases of ageing, developed and validated on 10 cohort studies.

Background: We aimed to develop risk tools for dementia, stroke, myocardial infarction (MI), and diabetes, for adults aged ≥ 65 years using shared risk factors.

Methods: Data were obtained from 10 population-based cohorts (N = 41,755) with median follow-up time (years) for dementia, stroke, MI, and diabetes of 6.2, 7.0, 6.8, and 7.4, respectively. Disease-free participants at baseline were included, and 22 risk factors (sociodemographic, medical, lifestyle, laboratory biomarkers) were evaluated. Two risk tools (DemNCD and DemNCD-LR based on Fine and Gray sub-distribution and logistic regression [LR], respectively) were developed and validated. Predictive accuracies of these risk tools were assessed using Harrel's C-statistics and area under the curve (AUC) and 95% confidence interval (CI). Model calibration was conducted using Hosmer-Lemeshow goodness of fit test along calibration plots.

Results: Both the DemNCD and DemNCD-LR resulted in similar predictive accuracy for each outcome. The overall AUC (95% CI) for dementia, stroke, MI, and diabetes risk tool were 0·68 (0·65, 0·70), 0·58 (0·54, 0·61), 0·65 (0·61, 0·68), and 0·68 (0·64, 0·72), respectively, for males. For females, these figures were 0·65 (0·63, 0·67), 0·55 (0·52, 0·57), 0·65 (0·62, 0·68), and 0·61 (0·57, 0·65).

Conclusions: The DemNCD is the first tool to predict both dementia and multiple cardio-metabolic diseases using comprehensive risk factors and provided similar predictive accuracy to existing risk tools. It has similar predictive accuracy as tools designed for single outcomes in this age-group. DemNCD has the potential to be used in community and clinical settings as it includes self-reported and routinely available clinical measures.

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来源期刊
BMC Medicine
BMC Medicine 医学-医学:内科
CiteScore
13.10
自引率
1.10%
发文量
435
审稿时长
4-8 weeks
期刊介绍: BMC Medicine is an open access, transparent peer-reviewed general medical journal. It is the flagship journal of the BMC series and publishes outstanding and influential research in various areas including clinical practice, translational medicine, medical and health advances, public health, global health, policy, and general topics of interest to the biomedical and sociomedical professional communities. In addition to research articles, the journal also publishes stimulating debates, reviews, unique forum articles, and concise tutorials. All articles published in BMC Medicine are included in various databases such as Biological Abstracts, BIOSIS, CAS, Citebase, Current contents, DOAJ, Embase, MEDLINE, PubMed, Science Citation Index Expanded, OAIster, SCImago, Scopus, SOCOLAR, and Zetoc.
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