Evaluating atrial fibrillation artificial intelligence for the ED: statistical and clinical implications

Kaminski, Ann; Albus, Michael; Ball, Colleen T.; White, Launia J.; Sheele, Johnathan M.; Attia, Zachi I.; Friedman, Paul A.; Adedinsewo, Demilade; Noseworthy, Peter A.

doi:10.1016/j.ajem.2022.04.032

Cited by 3 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of these studies, full texts for three studies were not retrieved. After reviewing the full texts of 126 studies, 16 studies [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] met the inclusion/exclusion criteria ( Figure S1 , Table S2 ). The 16 identified studies were conducted in seven different countries: Seven in the United States, three in Korea, two in the United Kingdom, and one in Japan, Lebanon, Germany, and Taiwan ( Figure 1 , Table S3 ).…”

Section: Resultsmentioning

confidence: 99%

Many Models, Little Adoption—What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection?

Kawamura,

Vafaei Sadr,

Abedi

et al. 2024

JCM

View full text Add to dashboard Cite

(1) Background: Atrial fibrillation (AF) is a major risk factor for stroke and is often underdiagnosed, despite being present in 13–26% of ischemic stroke patients. Recently, a significant number of machine learning (ML)-based models have been proposed for AF prediction and detection for primary and secondary stroke prevention. However, clinical translation of these technological innovations to close the AF care gap has been scant. Herein, we sought to systematically examine studies, employing ML models to predict incident AF in a population without prior AF or to detect paroxysmal AF in stroke cohorts to identify key reasons for the lack of translation into the clinical workflow. We conclude with a set of recommendations to improve the clinical translatability of ML-based models for AF. (2) Methods: MEDLINE, Embase, Web of Science, Clinicaltrials.gov, and ICTRP databases were searched for relevant articles from the inception of the databases up to September 2022 to identify peer-reviewed articles in English that used ML methods to predict incident AF or detect AF after stroke and reported adequate performance metrics. The search yielded 2815 articles, of which 16 studies using ML models to predict incident AF and three studies focusing on ML models to detect AF post-stroke were included. (3) Conclusions: This study highlights that (1) many models utilized only a limited subset of variables available from patients’ health records; (2) only 37% of models were externally validated, and stratified analysis was often lacking; (3) 0% of models and 53% of datasets were explicitly made available, limiting reproducibility and transparency; and (4) data pre-processing did not include bias mitigation and sufficient details, leading to potential selection bias. Low generalizability, high false alarm rate, and lack of interpretability were identified as additional factors to be addressed before ML models can be widely deployed in the clinical care setting. Given these limitations, our recommendations to improve the uptake of ML models for better AF outcomes include improving generalizability, reducing potential systemic biases, and investing in external validation studies whilst developing a transparent modeling pipeline to ensure reproducibility.

show abstract

Section: Resultsmentioning

confidence: 99%

Many Models, Little Adoption—What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection?

Kawamura,

Vafaei Sadr,

Abedi

et al. 2024

JCM

View full text Add to dashboard Cite

show abstract

“…Great breakthrough has been made in this field, especially during the COVID-19 epidemic [ 4 ]. Furthermore, it actively participates in the comprehensive management of CVD, including the utilization of artificial intelligence (AI) based electrocardiogram algorithm (AI-ECG) for promptly detection and diagnosis left atrial myopathy [ 5 ], AF events during sinus rhythm [ [6] , [7] , [8] , [9] , [10] ] and their subsequent effects [ 11 , 12 ] concealed long Q-T syndrome [ 7 , 13 ], aortic valve stenosis [ 14 ], asymptomatic left ventricular dysfunction (ALVD) [ 15 , 16 ], left ventricular systolic dysfunction [ [17] , [18] , [19] ], patients with low ejection fraction [ 12 ], Graves disease-associated AF, heart failure with reduced ejection fraction [ 20 ] and hyperkalemia [ 21 ], home blood pressure-centered innovative remote management for the control of hypertension [ [22] , [23] , [24] ]. The implementation of digital medicine interventions, such as mobile telephone text messaging and apps, for the remote management of patients with chronic diseases, especially coronary heart disease (CHD), has shown potential in enhancing lifestyle [ 17 , 21 , [25] , [26] , [27] , [28] ], controlling risk factors [ 26 ], and utilizing tele-rehabilitation [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Knowledge mapping of digital medicine in cardiovascular diseases from 2004 to 2022: A bibliometric analysis

Chen,

Xiao,

et al. 2024

Heliyon

View full text Add to dashboard Cite

Detecting Paroxysmal Atrial Fibrillation From an Electrocardiogram in Sinus Rhythm

Gruwez

Barthels

Haemers

et al. 2023

JACC: Clinical Electrophysiology

View full text Add to dashboard Cite

Evaluating atrial fibrillation artificial intelligence for the ED: statistical and clinical implications

Cited by 3 publications

References 15 publications

Many Models, Little Adoption—What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection?

Many Models, Little Adoption—What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection?

Knowledge mapping of digital medicine in cardiovascular diseases from 2004 to 2022: A bibliometric analysis

Detecting Paroxysmal Atrial Fibrillation From an Electrocardiogram in Sinus Rhythm

Contact Info

Product

Resources

About