在源不明的栓塞性中风后检测心房动:开发和验证CATCH-AF得分
Lucio D'Anna1,2, Francesco Favruzzo3, Claudio Baracchini3
1Department of Stroke and Neuroscience, Charing Cross Hospital, Imperial College London NHS Healthcare Trust, London, United Kingdom (UK).
概括
在CATCH-AF得分有效预测心房动 (AF) 风险在患有源不明的栓塞性中风 (ESUS) 的患者. 这种工具有助于有针对性的监测,以获得更好的患者结果.
科学领域:
- 心脏病学 心脏病学
- 神经学 神经学
- 医疗信息学 医疗信息学
背景情况:
- 源不明的栓塞性中风 (ESUS) 占缺血性中风的很大一部分,通常与未检测到的心房动 (AF) 相关.
- 植入式心脏监测器 (ICM) 增强了AF检测,但在成本和资源分配方面面临限制.
- 目前的AF预测模型缺乏强大的纵向验证和临床适用性.
研究的目的:
- 开发和验证CATCH-AF得分,这是评估ESUS患者早期和长期AF风险的简单工具.
- 为了实现准确的风险分层,为成本效益高的节律监测策略提供准确的风险分层.
主要方法:
- 对543名ESUS患者的分析,这些患者使用ICMs进行监测,使用LASSO处罚的Cox回归来进行变量选择.
- 通过依赖时间的ROC曲线,受限平均存活时间 (RMST) 分析和10倍交叉验证进行性能评估.
- 在七个中心进行内部-外部交叉验证,以确保地理通用性和模型稳定性.
主要成果:
- 在随访期间,22%的患者出现了新的AF.
- 在CATCH-AF得分中,在4.5年内显示出优秀且稳定的歧视 (AUC为0.85).
- 与低风险患者相比,高风险患者 (≥5分) 的AF检测风险增加了19倍.
结论:
- CATCH-AF评分是预测ESUS患者AF的可靠,可解释和实用的工具.
- 它支持有针对性的实施节奏监测,优化资源使用.
- 这一分数有助于为预防中风制定个性化管理策略.
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