机器学习与传统标准检测左心室肥厚及心电图预后的比较。

IF 4.4 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
European heart journal. Digital health Pub Date : 2025-02-11 eCollection Date: 2025-03-01 DOI:10.1093/ehjdh/ztaf003
Jui-Tzu Huang, Chih-Hsueh Tseng, Wei-Ming Huang, Wen-Chung Yu, Hao-Min Cheng, Hsi-Lu Chao, Chern-En Chiang, Chen-Huan Chen, Albert C Yang, Shih-Hsien Sung
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引用次数: 0

摘要

目的:左心室肥厚(LVH)具有重要的临床意义;目前的心电图(ECG)诊断标准不足以早期发现。本研究旨在开发一种基于人工智能(AI)的算法,以提高LVH检测的ECG标准的准确性和预后价值。方法与结果:共纳入42016例患者(64.3±16.5岁,男性55.3%)。通过超声心动图测量计算左室质量指数。左室肥厚筛查采用ECG标准,包括Sokolow-Lyon、Cornell product、Cornell/strain index、Framingham标准和Peguero-Lo Presti。使用CatBoost开发并验证了人工智能算法(训练数据集80%,测试数据集20%)。计算F1分数,反映准确率和召回率的调和平均值。死亡率数据是通过与国家死亡登记处的联系获得的。基于catboost的人工智能算法在LVH检测方面优于传统ECG标准,具有更高的灵敏度、特异性、阳性预测值、F1评分和曲线下面积。预测LVH的重要特征包括QRS和p波形态学。在10.1年的中位随访期间,测试数据集中发生了1655例死亡。Cox回归分析显示,人工智能算法识别的LVH(风险比和95%置信区间分别为1.587、1.309 ~ 1.924)、Sokolow-Lyon(1.19、1.038 ~ 1.365)、Cornell product(1.301、1.124 ~ 1.505)、Cornell/strain指数(1.306、1.185 ~ 1.439)、Framingham判据(1.174、1.062 ~ 1.298)、超声心动图确认的LVH(1.124、1.019 ~ 1.239)均与死亡率显著相关。值得注意的是,人工智能诊断的LVH比超声心动图证实的LVH更能预测死亡率。结论:基于人工智能的LVH诊断优于传统的ECG标准,与超声心动图证实的LVH相比,它是一个更好的死亡率预测指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comparison of machine learning and conventional criteria in detecting left ventricular hypertrophy and prognosis with electrocardiography.

Comparison of machine learning and conventional criteria in detecting left ventricular hypertrophy and prognosis with electrocardiography.

Comparison of machine learning and conventional criteria in detecting left ventricular hypertrophy and prognosis with electrocardiography.

Comparison of machine learning and conventional criteria in detecting left ventricular hypertrophy and prognosis with electrocardiography.

Aims: Left ventricular hypertrophy (LVH) is clinically important; current electrocardiography (ECG) diagnostic criteria are inadequate for early detection. This study aimed to develop an artificial intelligence (AI)-based algorithm to improve the accuracy and prognostic value of ECG criteria for LVH detection.

Methods and results: A total of 42 016 patients (64.3 ± 16.5 years, 55.3% male) were enrolled. LV mass index was calculated from echocardiographic measurements. Left ventricular hypertrophy screening utilized ECG criteria, including Sokolow-Lyon, Cornell product, Cornell/strain index, Framingham criterion, and Peguero-Lo Presti. An AI algorithm using CatBoost was developed and validated (training dataset 80% and testing dataset 20%). F1 scores, reflecting the harmonic mean of precision and recall, were calculated. Mortality data were obtained through linkage with the National Death Registry. The CatBoost-based AI algorithm outperformed conventional ECG criteria in detecting LVH, achieving superior sensitivity, specificity, positive predictive value, F1 score, and area under curve. Significant features to predict LVH involved QRS and P-wave morphology. During a median follow-up duration of 10.1 years, 1655 deaths occurred in the testing dataset. Cox regression analyses showed that LVH identified by AI algorithm (hazard ratio and 95% confidence interval: 1.587, 1.309-1.924), Sokolow-Lyon (1.19, 1.038-1.365), Cornell product (1.301, 1.124-1.505), Cornell/strain index (1.306, 1.185-1.439), Framingham criterion (1.174, 1.062-1.298), and echocardiography-confirmed LVH (1.124, 1.019-1.239) were all significantly associated with mortality. Notably, AI-diagnosed LVH was more predictive of mortality than echocardiography-confirmed LVH.

Conclusion: Artificial intelligence-based LVH diagnosis outperformed conventional ECG criteria and was a superior predictor of mortality compared to echocardiography-confirmed LVH.

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