基于机器学习的风险分层对严重大动脉反患者的死亡率
Vidhu Anand1, Hanwen Hu2, Alexander D Weston2
1Department of Cardiovascular Medicine, Mayo Clinic Rochester Minnesota, 200 First Street SW, Rochester, MN 55905, USA.
European heart journal. Digital health
|June 2, 2023
概括
机器学习模型可以预测严重的大动脉吐 (AR) 患者的死亡风险,独立于大动脉置换 (AVR). 这种方法可以识别高风险个体,以便及时进行干预,从而有可能改善生存结果.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 目前针对严重大动脉反 (AR) 的指导方针建议根据症状或心室功能障碍进行干预.
- 新出现的证据表明,目前的指导方针可能会推迟干预,可能会错过最佳治疗窗口.
- 在AR早期干预对于改善患者的预后和结果至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测严重AR患者的死亡风险.
- 识别那些可能受益于早期干预的高风险患者,不论他们的大动脉置换 (AVR) 状态如何.
- 评估ML算法在优化AR患者管理中的实用性.
主要方法:
- 训练有素的ML模型,包括条件随机生存森林,对1035名严重AR患者的数据集进行训练.
- 在207名患者的独立数据集上验证了模型性能.
- 利用五倍交叉验证,并为最终的预测模型选择了19个关键变量 (例如年龄,射出分数,NYHA类).
主要成果:
- 最佳的ML模型展示了强大的生存预测性能.
- 生存预测的一致性指数为0.84在1年,0.86在2年,和0.87整体.
- 关键预测因素包括年龄,体重指数,血压,NYHA类,AVR状态,并发症和心声学参数.
结论:
- 机器学习模型有效地预测严重AR患者的生存率,使用常见的临床和心声回声数据.
- 这种基于ML的方法可以识别那些可能受益于早期干预的高风险个体.
- 实施ML工具可能会改善严重AR治疗患者的治疗结果.
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