急性冠状动脉综合征入院患者早期心律失常风险的分层：机器学习衍生的“PRAISE评分”的作用

Stratification of Early Arrhythmic Risk in Patients Admitted for Acute Coronary Syndrome: The Role of the Machine Learning-Derived "PRAISE Score".

作者信息

Cumitini Luca, Giubertoni Ailia, Rossi Lidia, D'Amario Domenico, Grisafi Leonardo, Abbiati Paola, D'Ascenzo Fabrizio, De Ferrari Gaetano Maria, Patti Giuseppe

机构信息

Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy.

Division of Cardiology, Maggiore della Carità Hospital, Novara, Italy.

出版信息

Clin Cardiol. 2024 Dec;47(12):e70035. doi: 10.1002/clc.70035.

DOI:10.1002/clc.70035

PMID:39699030

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11656403/

Abstract

BACKGROUND

The PRAISE (PRedicting with Artificial Intelligence riSk aftEr acute coronary syndrome) score is a machine learning-based model for predicting 1-year adverse cardiovascular or bleeding events in patients with acute coronary syndrome (ACS). Its role in predicting arrhythmic complications in ACS remains unknown.

METHODS

Atrial fibrillation (AF) and ventricular arrhythmias (VA) were recorded by continuous electrocardiographic monitoring until discharge in a cohort of 365 participants with ACS prospectively enrolled. We considered two separate timeframes for VA occurrence: ≤ 48 and > 48 h. The objective was to evaluate the ability of the PRAISE score to identify ACS patients at higher risk of in-hospital arrhythmic complications.

RESULTS

ROC curve analysis indicated a significant association between PRAISE score and risk of both AF (AUC 0.89, p = 0.0001; optimal cut-off 5.77%) and VA (AUC 0.69, p = 0.0001; optimal cut-off 2.17%). Based on these thresholds, high/low AF PRAISE score groups and high/low VA PRAISE score groups were created, respectively. Patients with a high AF PRAISE score more frequently developed in-hospital AF (19% vs. 1%). Multivariate analysis showed a high AF PRAISE score risk as an independent predictor of AF (HR 4.30, p = 0.016). Patients with high VA PRAISE scores more frequently developed in-hospital VA (25% vs. 8% for VA ≤ 48 h; 33% vs. 3% for VA > 48 h). Multivariate analysis demonstrated a high VA PRAISE score risk as an independent predictor of both VA ≤ 48 h (HR 2.48, p = 0.032) and VA > 48 h (HR 4.93, p = 0.014).

CONCLUSION

The PRAISE score has a comprehensive ability to identify with high specificity those patients at risk for arrhythmic events during hospitalization for ACS.

摘要

背景

PRAISE（急性冠状动脉综合征后人工智能风险预测）评分是一种基于机器学习的模型，用于预测急性冠状动脉综合征（ACS）患者1年内的不良心血管或出血事件。其在预测ACS心律失常并发症方面的作用尚不清楚。

方法

对前瞻性纳入的365例ACS参与者进行连续心电图监测，记录房颤（AF）和室性心律失常（VA）直至出院。我们考虑了VA发生的两个不同时间范围：≤48小时和>48小时。目的是评估PRAISE评分识别住院期间发生心律失常并发症风险较高的ACS患者的能力。

结果

ROC曲线分析表明，PRAISE评分与AF风险（AUC 0.89，p = 0.0001；最佳截断值5.77%）和VA风险（AUC 0.69，p = 0.0001；最佳截断值2.17%）之间存在显著关联。基于这些阈值，分别创建了高/低AF PRAISE评分组和高/低VA PRAISE评分组。AF PRAISE评分高的患者住院期间发生AF的频率更高（19%对1%）。多变量分析显示，高AF PRAISE评分风险是AF的独立预测因素（HR 4.30，p = 0.016）。VA PRAISE评分高的患者住院期间发生VA的频率更高（VA≤48小时时为25%对8%；VA>48小时时为33%对3%）。多变量分析表明，高VA PRAISE评分风险是VA≤48小时（HR 2.48，p = 0.032）和VA>48小时（HR 4.93，p = 0.014）的独立预测因素。