摘要
Robotics&Machine Learning Daily News的一位新闻记者兼新闻编辑-根据基于预印摘要的新闻报道，我们的记者从Me drxiv.org获得了以下引文：“重要性：尽管有疾病矫正疗法，心力衰竭(HF)危险分层的可扩展策略仍然难以实现。能够记录单导联心电图(ECGs)的便携式设备可以实现大规模的社区风险评估。目的:评估人工智能(AI)算法预测噪音单导联心电图的心力衰竭风险。设计:多点研究。背景:耶鲁纽黑文综合卫生系统(YNHHS)和前瞻性PO脉冲队列中患有突发心电图的个体回顾性队列。(ELSA-Brasil).参与者：基线时无心力衰竭的个体。实验结果：AI-ECG定义的左室收缩功能障碍风险(LVSD).主要结果和测量：在有ECG的个体中，我们分离了I导联ECG，并部署了经过训练的噪声适应的AI-ECG模型来识别LVSD。我们评估了模型概率与新发HF的关联。定义为首次住院HF。我们使用Harrel9s C-统计量、综合判别改善(IDI)和净重新分类改善(NRI)比较了AI-ECG与合并队列方程的区分，以预防新发HF的HF(PCP-HF)评分。结果：有194340名YNHS患者(年龄56岁[IQR，41-69]，112082名女性[58%])，42741名UKB参与者(65岁[59-ELSA-Brasil参与者（56岁[41-69]，7348名女性[55%]）患有Baseline ECG。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from me drxiv.org: “Importance: Despite the availability of disease-modifying therapies, scalable s trategies for heart failure (HF) risk stratification remain elusive. Portable de vices capable of recording single-lead electrocardiograms (ECGs) can enable larg e-scale community-based risk assessment. Objective: To evaluate an artificial in telligence (AI) algorithm to predict HF risk from noisy single-lead ECGs. Design : Multicohort study.Setting: Retrospective cohort of individuals with outpatien t ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective po pulation-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of A dult Health (ELSA-Brasil). Participants: Individuals without HF at baseline. Exp osures: AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD). Mai n Outcomes and Measures: Among individuals with ECGs, we isolated lead I ECGs an d deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated t he association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel9s C- statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results: There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58% ]), 42,741 UKB participants (65 years [59- 71], 21,795 women [52%] ), and 13,454 ELSA-Brasil participants (56 years [41-69] , 7,348 women [55%]) with bas eline ECGs.
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