用于预测不同时间范围的新发心房颤动风险的计算器。

Risk calculator for incident atrial fibrillation across a range of prediction horizons.

机构信息

Wolfson Institute of Population Health, Queen Mary, University of London, UK.

Leeds Institute of Data Analytics, University of Leeds, UK; Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, UK; Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.

出版信息

Am Heart J. 2024 Jun;272:1-10. doi: 10.1016/j.ahj.2024.03.001. Epub 2024 Mar 6.

DOI:10.1016/j.ahj.2024.03.001

PMID:38458372

Abstract

BACKGROUND

The increasing burden of atrial fibrillation (AF) emphasizes the need to identify high-risk individuals for enrolment in clinical trials of AF screening and primary prevention. We aimed to develop prediction models to identify individuals at high-risk of AF across prediction horizons from 6-months to 10-years.

METHODS

We used secondary-care linked primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between January 2, 1998 and November 30, 2018; randomly divided into derivation (80%) and validation (20%) datasets. Models were derived using logistic regression from known AF risk factors for incident AF in prediction periods of 6 months, 1-year, 2-years, 5-years, and 10-years. Performance was evaluated using in the validation dataset with bootstrap validation with 200 samples, and compared against the CHADS-VASc and CHEST scores.

RESULTS

Of 2,081,139 individuals in the cohort (1,664,911 in the development dataset, 416,228 in the validation dataset), the mean age was 49.9 (SD 15.4), 50.7% were women, and 86.7% were white. New cases of AF were 7,386 (0.4%) within 6 months, 15,349 (0.7%) in 1 year, 38,487 (1.8%) in 5 years, and 79,997 (3.8%) by 10 years. Valvular heart disease and heart failure were the strongest predictors, and association of hypertension with AF increased at longer prediction horizons. The optimal risk models incorporated age, sex, ethnicity, and 8 comorbidities. The models demonstrated good-to-excellent discrimination and strong calibration across prediction horizons (AUROC, 95%CI, calibration slope: 6-months, 0.803, 0.789-0.821, 0.952; 1-year, 0.807, 0.794-0.819, 0.962; 2-years, 0.815, 0.807-0.823, 0.973; 5-years, 0.807, 0.803-0.812, 1.000; 10-years 0.780, 0.777-0.784, 1.010), and superior to the CHADS-VASc and CHEST scores. The models are available as a web-based FIND-AF calculator.

CONCLUSIONS

The FIND-AF models demonstrate high discrimination and calibration across short- and long-term prediction horizons in 2 million individuals. Their utility to inform trial enrolment and clinical decisions for AF screening and primary prevention requires further study.

摘要

背景

心房颤动（AF）负担不断增加，这强调了需要确定参加 AF 筛查和一级预防临床试验的高危个体。我们的目的是开发预测模型，以确定 6 个月至 10 年预测期内 AF 风险较高的个体。

方法

我们使用来自英国临床实践研究数据链接-GOLD 数据集的二级保健相关初级保健电子健康记录数据，该数据来自 1998 年 1 月 2 日至 2018 年 11 月 30 日期间年龄≥30 岁且无已知 AF 的个体；随机分为推导（80%）和验证（20%）数据集。在 6 个月、1 年、2 年、5 年和 10 年的预测期内，使用 logistic 回归从已知的 AF 风险因素中推导模型来预测 AF 发病。在验证数据集中使用 bootstrap 验证（200 个样本）进行性能评估，并与 CHADS-VASc 和 CHEST 评分进行比较。

结果

在队列中的 2081139 名个体中（开发数据集 1664911 名，验证数据集 416228 名），平均年龄为 49.9（SD 15.4），50.7%为女性，86.7%为白人。在 6 个月内新发生 AF 的病例为 7386（0.4%），1 年内为 15349（0.7%），5 年内为 38487（1.8%），10 年内为 79997（3.8%）。瓣膜性心脏病和心力衰竭是最强的预测因素，高血压与 AF 的关联在较长的预测期内增加。最佳风险模型纳入了年龄、性别、种族和 8 种合并症。该模型在各预测期均表现出良好到极好的区分度和强校准度（AUROC，95%CI，校准斜率：6 个月，0.803，0.789-0.821，0.952；1 年，0.807，0.794-0.819，0.962；2 年，0.815，0.807-0.823，0.973；5 年，0.807，0.803-0.812，1.000；10 年，0.780，0.777-0.784，1.010），优于 CHADS-VASc 和 CHEST 评分。这些模型可作为基于网络的 FIND-AF 计算器使用。