分类和回归树分析识别了住院后功能下降的高风险患者
Weihao Wang1, Wei Zhu1, Janos Hajagos2
1Department of Applied Mathematics, Stony Brook University, Stony Brook, NY, United States of America.
PloS one
|January 31, 2025
概括
在住院后预测功能下降至关重要. 这项研究发现,更长的住院时间和急性损伤显著增加了出院后估计膜过率 (eGFR) 下降的风险.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 在医疗保健中的数据科学.
- 流行病学 流行病学
背景情况:
- 估计的淋巴细胞过率 (eGFR) 降低与不良健康结果有关.
- 对于eGFR下降的预测模型,特别是COVID-19大流行期间住院后的预测模型,未得到充分利用.
- 鉴定出院后EGFR下降的高风险患者至关重要.
研究的目的:
- 为了确定在COVID-19大流行期间住院后EGFR下降风险最大的个体.
- 评估决策树算法在预测eGFR下降方面的实用性.
- 发现与快速EGFR下降相关的关键风险因素.
主要方法:
- 从2020年开始对1747名医院幸存者的回顾性队列研究,持续36个月.
- 利用随机森林 (RF) 来识别快速eGFR下降的顶级特征.
- 使用后勤回归 (LR) 和分类和回归树 (CART) 进行风险分层.
主要成果:
- 61.6%的患者经历了快速的EGFR下降.
- 与快速EGFR下降相关的因素包括年轻的年龄,更高的基线EGFR和急性损伤 (AKI).
- 卡特分析确定住院时间 (≥27天) 和AKI是高风险患者的主要风险因素.
结论:
- 分类和回归树 (CART) 有效地识别高风险的患者子组,以降低退院后的eGFR.
- 临床医生应优先监测住院时间较长的患者的功能.
- 住院时间和AKI是预测长期功能下降的关键因素.
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