Artificial intelligence-enhanced electrocardiogram for the diagnosis of cardiac amyloidosis: A systemic review and meta-analysis

IF 3 3区医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS

Current Problems in Cardiology Pub Date : 2024-09-19 DOI:10.1016/j.cpcardiol.2024.102860

Laibah Arshad Khan MBBS , Fahad Hassan Shaikh MBBS , Muhammad Sami Khan MBBS , Bayan Zafar MBBS , Maheera Farooqi MBBS , Bayarbaatar Bold MD , Hafiza Madiha Aslam MBBS , Nabeeha Essam MBBS , Isma Noor MBBS , Amber Siddique MBBS , Saad Shakil MBBS , Mahnoor Asghar Keen MBBS

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引用次数: 0

Abstract

Background

Diagnosis of cardiac amyloidosis (CA) is often delayed due to variability in clinical presentation. The electrocardiogram (ECG) is one of the most common and widely available tools for assessing cardiovascular diseases. Artificial intelligence (AI) models analyzing ECG have recently been developed to detect CA, but their pooled accuracy is yet to be evaluated.

Methods

We searched the Scopus, MEDLINE, and Cochrane CENTRAL databases until April 2024 for studies assessing AI-enhanced ECG diagnosis of CA. Studies reporting findings from derivation and validation cohorts were included. Studies combining other diagnostic modalities, such as echocardiography, were excluded. The outcome of interest was the area under the receiver operating characteristic curve (AUC) for overall CA and subtypes transthyretin amyloidosis (ATTR) and light chain amyloidosis (AL). Analysis was done using RevMan 5.4.1 general inverse variance random effects model, pooling data for AUC and 95 % confidence intervals (CI).

Results

Five studies comprising seven cohorts met the eligibility criteria. The total derivation and validation cohorts were 8,639 and 3,843, respectively, although one study did not describe this data. The AUC was 0.89 (95 % CI, 0.86-0.91) for cardiac amyloidosis, 0.90 (95 % CI, 0.86-0.95) for ATTR amyloidosis, and 0.80 (95 % CI, 0.80-0.93) for AL amyloidosis.

Conclusion

AI-enhanced ECG models effectively detect CA and may provide a valuable tool for the early detection and intervention of this disease.

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人工智能增强心电图诊断心脏淀粉样变性：系统综述与元分析》。

背景：由于临床表现的多变性，心脏淀粉样变性（CA）的诊断常常被延迟。心电图（ECG）是评估心血管疾病最常见、最广泛使用的工具之一。分析心电图的人工智能（AI）模型最近已被开发出来用于检测CA，但其综合准确性还有待评估：我们在 Scopus、MEDLINE 和 Cochrane CENTRAL 数据库中检索了截至 2024 年 4 月的评估 AI 增强心电图诊断 CA 的研究。纳入了报告衍生队列和验证队列结果的研究。排除了结合其他诊断方式（如超声心动图）的研究。研究结果是总体CA和亚型转甲状腺素淀粉样变性（ATTR）和轻链淀粉样变性（AL）的接收者操作特征曲线下面积（AUC）。分析采用RevMan 5.4.1一般反方差随机效应模型，对AUC和95%置信区间（CI）数据进行汇总：共有五项研究、七个队列符合资格标准。推导队列和验证队列的总人数分别为 8,639 人和 3,843 人，但有一项研究未说明该数据。心脏淀粉样变性的AUC为0.89（95% CI，0.86-0.91），ATTR淀粉样变性为0.90（95% CI，0.86-0.95），AL淀粉样变性为0.80（95% CI，0.80-0.93）：结论：人工智能增强心电图模型可有效检测CA，是早期检测和干预该疾病的重要工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Current Problems in Cardiology 医学-心血管系统

CiteScore

4.80

自引率

2.40%

发文量

392

审稿时长

6 days

期刊介绍： Under the editorial leadership of noted cardiologist Dr. Hector O. Ventura, Current Problems in Cardiology provides focused, comprehensive coverage of important clinical topics in cardiology. Each monthly issues, addresses a selected clinical problem or condition, including pathophysiology, invasive and noninvasive diagnosis, drug therapy, surgical management, and rehabilitation; or explores the clinical applications of a diagnostic modality or a particular category of drugs. Critical commentary from the distinguished editorial board accompanies each monograph, providing readers with additional insights. An extensive bibliography in each issue saves hours of library research.