Amir Askarinejad, Tommaso Bucci, Niloofar Asgharzadeh, Zahra Amirjam, Enrico Tartaglia, Michele Rossi, Yang Chen, Yalin Zheng, Gregory Y H Lip, Majid Haghjoo
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引用次数: 0
Abstract
Background: Given the modest performance of available predictive models in estimating the risk of atrial fibrillation (AF) in patients with atrial high-rate episodes (AHREs) detected by cardiac implantable electronic devices (CIEDs), this study explores the potential use of machine learning (ML) algorithms in this context.
Purpose: To assess the ability of ML techniques in identifying patients with AHRE at high risk of AF.
Methods: In this prospective study, we enrolled patients without a prior history of AF who experienced at least one AHRE episode detected by CIEDs. ML techniques were applied to predict the 1-year risk of developing new-onset AF based on the following variables: age, BMI, sex, smoking, hypertension, diabetes, coronary artery disease, chronic kidney disease, dyslipidaema, history of stroke or transient ischaemic attack, vascular heart disease, left atrial enlargement (LAE) and congestive heart failure.
Results: Study population consists of 100 patients (48% male, mean age 66.0 ± 18.0 years), of whom 24 developed AF (24%) after 1-year follow-up. The CatBoost ML model achieved the highest AUC (.857, 95% CI .671-.999) when compared to other ML models and all clinical risk scores. The top four most influential predictors of AF in the CatBoost model were LAE, hypertension, diabetes and age.
Conclusions: ML techniques are robust in predicting AF in patients with AHREs. Further validation in larger, independent cohorts is warranted.
背景：鉴于可用的预测模型在估计心房高频率发作（AHREs）患者心房颤动（AF）风险方面的适度表现，本研究探讨了机器学习（ML）算法在这方面的潜在应用。目的：评估ML技术识别AF高风险AHRE患者的能力。方法：在这项前瞻性研究中，我们招募了没有AF病史且至少经历过一次cied检测到的AHRE发作的患者。基于以下变量，应用ML技术预测1年内发生新发房颤的风险：年龄、BMI、性别、吸烟、高血压、糖尿病、冠状动脉疾病、慢性肾脏疾病、血脂异常、中风或短暂性缺血发作史、血管性心脏病、左房扩大（LAE）和充血性心力衰竭。结果：研究人群包括100例患者（男性48%，平均年龄66.0±18.0岁），随访1年后发生房颤24例（24%）。CatBoost ML模型获得了最高的AUC(。857, 95% CI .671-.999)，与其他ML模型和所有临床风险评分相比。在CatBoost模型中，对房颤影响最大的4个预测因子是LAE、高血压、糖尿病和年龄。结论：ML技术在预测AHREs患者房颤方面是可靠的。需要在更大的独立队列中进一步验证。
期刊介绍：
EJCI considers any original contribution from the most sophisticated basic molecular sciences to applied clinical and translational research and evidence-based medicine across a broad range of subspecialties. The EJCI publishes reports of high-quality research that pertain to the genetic, molecular, cellular, or physiological basis of human biology and disease, as well as research that addresses prevalence, diagnosis, course, treatment, and prevention of disease. We are primarily interested in studies directly pertinent to humans, but submission of robust in vitro and animal work is also encouraged. Interdisciplinary work and research using innovative methods and combinations of laboratory, clinical, and epidemiological methodologies and techniques is of great interest to the journal. Several categories of manuscripts (for detailed description see below) are considered: editorials, original articles (also including randomized clinical trials, systematic reviews and meta-analyses), reviews (narrative reviews), opinion articles (including debates, perspectives and commentaries); and letters to the Editor.
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