基于机器学习的预测,1年心律失常的复发后心室动脉缩切除在患有结构性心脏病的患者
Ferenc Komlósi1, Patrik Tóth1, Gyula Bohus1
1Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.
Bioengineering (Basel, Switzerland)
|December 23, 2023
概括
机器学习模型准确地预测了导管切除后心室性心力衰竭 (VT) 复发. 这些工具有助于识别患有静脉治疗高风险的患者,指导个性化后续策略.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习在医学中的应用
背景情况:
- 导管切除后静脉动脉 (VT) 复发是一个重要的临床问题.
- 精确的风险评估对于管理切除后静脉瘤至关重要.
- 机器学习 (ML) 为预测VT复发提供了一个有希望的方法.
研究的目的:
- 开发和验证ML模型,用于预测1个月和1年VT复发后的VT复发.
- 通过基于ML的特征选择来识别VT复发的关键预测因素.
主要方法:
- 利用了337名接受静脉切除术的患者的数据,包括31个临床,心声和程序参数.
- 应用基于ML的特征选择,以确定1个月 (6个特征) 和1年 (5个特征) 复发的最佳预测因素.
- 经过训练和评估的监督ML模型,包括随机森林,使用十倍交叉验证.
主要成果:
- 一个月VT复发 (18%) 预测AUC为0.73,使用血动力学不稳定性和LVEF等特征.
- 一年内静脉瘤复发 (35%) 预测平均AUC为0.71,其中包括血液动力学不稳定性和LV静脉缩直径.
- 随机森林模型在两个预测终点上都表现出卓越的性能.
结论:
- ML模型有效地预测了导管切除后静脉瘤的复发.
- 这些预测模型可以帮助识别高风险患者.
- 这些发现支持根据预测复发风险量身定制患者随访策略.
相关概念视频
Disturbances in Heart Rhythm
963
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
963
Mechanism of Cardiac Arrhythmias
925
Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
925


