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

Autor: Khan LA; Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA., Shaikh FH; Mercy St Vincent Medical Center, Toledo, OH, USA., Khan MS; Department of Medicine, Calderdale and Huddersfield NHS Foundation Trust, Halifax, United Kingdom. Electronic address: msami96@gmail.com., Zafar B; Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan., Farooqi M; Dow Medical College, Dow University of Health Sciences, Karachi, Pakistan., Bold B; Radiology Department, Intermed Hospital, Ulaanbaatar, Mongolia., Aslam HM; Mohtarma Benazir Bhutto Shaheed Medical College, Mirpur, Azad Jammu Kashmir, Pakistan., Essam N; Jinnah Sindh Medical University, Karachi, Pakistan., Noor I; West Suffolk Hospital NHS Foundation Trust, United Kingdom., Siddique A; Faisalabad Medical Univeristy, Faisalabad, Pakistan., Shakil S; Islamabad Medical and Dental College, Islamabad, Pakistan., Keen MA; Khyber Medical College, Peshawar, Pakistan.
Jazyk: angličtina
Zdroj: Current problems in cardiology [Curr Probl Cardiol] 2024 Dec; Vol. 49 (12), pp. 102860. Date of Electronic Publication: 2024 Sep 19.
DOI: 10.1016/j.cpcardiol.2024.102860
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier Inc.)
Databáze: MEDLINE
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