Arti fi cial Intelligence Electrocardiography to Predict Atrial Fibrillation in Patients With Chronic Lymphocytic Leukemia

被引:4
作者
Christopoulos, Georgios [1 ]
Attia, Zachi I. [1 ]
Achenbach, Sara J. [2 ]
Rabe, Kari G. [2 ]
Call, Timothy G. [3 ]
Ding, Wei [3 ]
Leis, Jose F. [4 ]
Muchtar, Eli [3 ]
Kenderian, Saad S. [3 ]
Wang, Yucai [3 ]
Hampel, Paul J. [3 ]
Koehler, Amber B. [3 ]
Kay, Neil E. [3 ]
Kapoor, Prashant [3 ]
Slager, Susan L. [2 ,3 ]
Shanafelt, Tait D. [5 ]
Noseworthy, Peter A. [1 ]
Friedman, Paul A. [1 ]
Herrmann, Joerg [1 ]
Parikh, Sameer A. [3 ]
机构
[1] Mayo Clin, Dept Cardiovasc Med, Rochester, MN 55905 USA
[2] Mayo Clin, Dept Quantitat Hlth Sci, Rochester, MN USA
[3] Mayo Clin, Div Hematol, Rochester, MN 55905 USA
[4] Mayo Clin, Div Hematol & Med Oncol, Scottsdale, AZ USA
[5] Stanford Univ, Dept Med, Palo Alto, CA USA
来源
JACC: CARDIOONCOLOGY | 2023年 / 6卷 / 02期
基金
美国国家卫生研究院;
关键词
arti fi cial intelligence; atrial fi brillation; chronic lymphocytic leukemia; electrocardiography; RISK;
D O I
10.1016/j.jaccao.2024.02.006
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
BACKGROUND The use of an arti ficial intelligence electrocardiography (AI-ECG) algorithm has demonstrated its reliability in predicting the risk of atrial fibrillation (AF) within the general population. OBJECTIVES This study aimed to determine the effectiveness of the AI-ECG score in identifying patients with chronic lymphocytic leukemia (CLL) who are at high risk of developing AF. METHODS We estimated the probability of AF based on AI-ECG among patients with CLL extracted from the Mayo Clinic CLL database. Additionally, we computed the Mayo Clinic CLL AF risk score and determined its ability to predict AF. RESULTS Among 754 newly diagnosed patients with CLL, 71.4% were male (median age = 69 years). The median baseline AI-ECG score was 0.02 (range = 0-0.93), with a value >= 0.1 indicating high risk. Over a median follow-up of 5.8 years, the estimated 10 -year cumulative risk of AF was 26.1%. Patients with an AI-ECG score of >= 0.1 had a signi ficantly higher risk of AF (HR: 3.9; 95% CI: 2.6-5.7; P < 0.001). This heightened risk remained signi ficant (HR: 2.5; 95% CI: 1.63.9; P < 0.001) even after adjusting for the Mayo CLL AF risk score, heart failure, chronic kidney disease, and CLL therapy. In a second cohort of CLL patients treated with a Bruton tyrosine kinase inhibitor (n = 220), a pretreatment AIECG score >= 0.1 showed a nonsigni ficant increase in the risk of AF (HR: 1.7; 95% CI: 0.8-3.6; P = 0.19). CONCLUSIONS An AI-ECG algorithm, in conjunction with the Mayo CLL AF risk score, can predict the risk of AF in patients with newly diagnosed CLL. Additional studies are needed to determine the role of AI-ECG in predicting AF risk in CLL patients treated with a Bruton tyrosine kinase inhibitor. (J Am Coll Cardiol CardioOnc 2024;6:251 -263) (c) 2024 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:251 / 263
页数:13
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