通过表值扩散模型预测CKD中的夜间高血压
Xiaoyu Cai1, Rui Li2, Zhao Liu1
1Division of Nephrology, Department of Medicine, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, China (X.C., Z.L., X.J., S.G., C.W.).
Hypertension (Dallas, Tex. : 1979)
|February 26, 2026
概括
一个新的7变量扩散模型准确地预测慢性病患者的夜间高血压 (NH). 该工具有助于有针对性的门诊血压监测,提高高风险个体的查效率.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
背景情况:
- 门诊血压监测 (ABPM) 在慢性病 (CKD) 患者的夜间高血压 (NH) 诊断中至关重要.
- 目前的ABPM方法昂贵且耗时,需要高风险NH的高效选工具.
- 紧急需要NH的预测模型,以优化CKD患者的管理.
研究的目的:
- 开发和验证一个预测模型,用于在非透析慢性病 (CKD) 患者中识别夜间高血压 (NH) 患者.
- 评估新型表值扩散模型在预测NH风险方面的有效性.
- 建立一个风险分层系统,用于针对性ABPM查CKD.
主要方法:
- 分析了5769名非透析CKD患者的大队伍,数据分为培训,内部和外部测试集.
- 开发了一个表值扩散模型,使用7个核心临床变量预测NH概率.
- 模型性能使用曲线下的面积 (AUC) 和科恩卡帕指数进行评估,对1006名患者进行了二次验证.
主要成果:
- 与物流模型相比,7变量扩散模型在内部 (AUC 0.870对0.807) 和外部 (AUC 0.854对0.792) 测试集中表现出优异的预测性能.
- 该模型在二次验证中显示稳定性 (AUC=0.869,科恩卡帕=0.560).
- 风险分层确定了高风险患者 (>0.692) 具有显著更高的不良脏/心血管事件发病率.
结论:
- 成功开发了一种简洁的7变量扩散模型,用于预测CKD患者的NH.
- 这种扩散模型支持有针对性的ABPM查,提高诊断效率.
- 该模型促进了风险分层,使得高风险CKD个体的不良结果的专注监测成为可能.
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