评估数字心电图去噪对医护人员判读能力的影响。

IF 3.9 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS European heart journal. Digital health Pub Date : 2024-08-12 eCollection Date: 2024-09-01 DOI:10.1093/ehjdh/ztae063
Stacey McKenna, Naomi McCord, Jordan Diven, Matthew Fitzpatrick, Holly Easlea, Austin Gibbs, Andrew R J Mitchell
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引用次数: 0

摘要

目的:心电图(ECG)判读是多种医学学科的一项基本技能;然而,研究不断发现,医疗保健专业人员的判读能力不足与各种教育和技术因素有关。尽管噪声干扰与错误诊断之间存在既定的相关性,但评估数字去噪软件对临床心电图判读能力影响的研究还很缺乏:本研究前瞻性地招募了 48 名来自不同医学专业和经验水平的参与者。在使用基于云的强大心电图处理软件对一组具有挑战性的单导联心电图信号(42 × 10 秒)进行去噪前和去噪后判读过程中,采用顺序盲法和半盲法判读方案对参与者的常见心律分类能力进行了评估。当以可进行比较评估的组合格式查看原始信号和去噪信号时,参与者的心电图节律判读能力最强。合并查看后,平均心律分类准确率提高了 4.9%(原始:75.7% ± 14.5% vs. 合并:80.6% ± 12.5%,P = 0.0087),平均五点分级置信度提高了 6.2%(原始:4.05 ± 0.58 vs. 综合:4.30 ± 0.48,P < 0.001),与单独使用原始信号相比,不可诊断数据的平均比例降低了 9.7%(原始:14.2% ± 8.2% vs. 综合:4.5% ± 2.4%,P < 0.001)。参与者对去噪的看法也主要是积极的,因为它能揭示之前未发现的病变、提高心电图的可读性并缩短诊断时间:我们的研究结果表明,数字去噪软件提高了单导联心电图心律解读的效果,尤其是当原始信号和去噪信号以组合浏览的形式提供时。
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Evaluating the impacts of digital ECG denoising on the interpretive capabilities of healthcare professionals.

Aims: Electrocardiogram (ECG) interpretation is an essential skill across multiple medical disciplines; yet, studies have consistently identified deficiencies in the interpretive performance of healthcare professionals linked to a variety of educational and technological factors. Despite the established correlation between noise interference and erroneous diagnoses, research evaluating the impacts of digital denoising software on clinical ECG interpretation proficiency is lacking.

Methods and results: Forty-eight participants from a variety of medical professions and experience levels were prospectively recruited for this study. Participants' capabilities in classifying common cardiac rhythms were evaluated using a sequential blinded and semi-blinded interpretation protocol on a challenging set of single-lead ECG signals (42 × 10 s) pre- and post-denoising with robust, cloud-based ECG processing software. Participants' ECG rhythm interpretation performance was greatest when raw and denoised signals were viewed in a combined format that enabled comparative evaluation. The combined view resulted in a 4.9% increase in mean rhythm classification accuracy (raw: 75.7% ± 14.5% vs. combined: 80.6% ± 12.5%, P = 0.0087), a 6.2% improvement in mean five-point graded confidence score (raw: 4.05 ± 0.58 vs. combined: 4.30 ± 0.48, P < 0.001), and 9.7% reduction in the mean proportion of undiagnosable data (raw: 14.2% ± 8.2% vs. combined: 4.5% ± 2.4%, P < 0.001), relative to raw signals alone. Participants also had a predominantly positive perception of denoising as it related to revealing previously unseen pathologies, improving ECG readability, and reducing time to diagnosis.

Conclusion: Our findings have demonstrated that digital denoising software improves the efficacy of rhythm interpretation on single-lead ECGs, particularly when raw and denoised signals are provided in a combined viewing format, warranting further investigation into the impact of such technology on clinical decision-making and patient outcomes.

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