以数据为导向的方法来表征自体主导多囊性病的快速下降
John J Sim1,2,3, Yu-Hsiang Shu4, Simran K Bhandari5
1Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States of America.
PloS one
|June 5, 2024
概括
自体主导多囊性病 (ADPKD) 患者的功能迅速下降是使用临床数据识别的. 六个关键因素预测了这种下降,有助于早期检测和管理,以预防末期病 (ESKD).
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 遗传学 是一个遗传学.
- 数据科学在医学中的数据科学
背景情况:
- 自体主导多囊性病 (ADPKD) 在患者的进展中表现出显著的变化.
- 了解疾病轨迹对于及时干预延迟末期病 (ESKD) 至关重要.
研究的目的:
- 在ADPKD队列中识别功能快速下降 (RD) 的患者.
- 用数据驱动方法确定预测RD的临床因素.
主要方法:
- 对发生ADPKD患者 (2002-2018) 的回顾性队列研究.
- 隐性类混合模型以基于eGFR轨迹来定义RD.
- 组合特征选择方法 (物流,规范化,随机森林) 来识别预测因素.
主要成果:
- 分析了1744名患者,125名 (7%) 被确定为RD.
- 一个预测模型确定了六个关键预测因素:年龄,性别,高血压,脑血管疾病,血红蛋白和蛋白尿.
- 该模型实现了72%的灵敏度,70%的特异性和0.77AUC用于RD识别.
- 在RD (38%) 和非RD (7%) 组中,5年ESKD率显著更高.
结论:
- 常规的临床数据可以有效地识别ADPKD患者,他们面临迅速功能下降的高风险.
- 已识别的六个预测因素为ADPKD管理中的早期风险分层提供了一个实用的工具.
- 早期识别RD可能会改变疾病的进程,并减少ESKD的进展.
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