基于机器学习的风险评分用于预测感染性内心炎在患有黄金葡萄球菌病的患者中 - SABIER评分
Christopher Koon-Chi Lai1,2, Eman Leung3, Yinan He3
1Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
The Journal of infectious diseases
|February 29, 2024
概括
一个新的风险评分有助于在患有黄金杆菌病 (SAB) 的患者中早期识别黄金杆菌感染性内心炎 (SA-IE) 风险. 这种工具有助于在主观判断之前做出临床决策,改善患者分层.
科学领域:
- 传染性疾病 传染性疾病
- 心脏病学 心脏病学
- 数据科学数据科学数据科学
背景情况:
- 在S. aureus bacteremia (SAB) 患者中,分层化金黄色葡萄球菌感染性内心炎 (SA-IE) 风险对于及时的临床管理至关重要.
- 现有的风险评估方法通常依赖于主观的临床判断,因此需要客观的工具来进行早期风险分层.
研究的目的:
- 开发和验证一种新的,客观的风险评分,用于预测SA-IE在SAB患者中.
- 风险评分旨在在血培养阳性时早期应用,独立于临床判断.
主要方法:
- 对住院SAB患者 (2009-2019) 进行了回顾性大数据分析.
- 一个随机森林模型确定了SA-IE.的关键预测变量.
- 数据被分为导出和验证队列,并计算了AUCROCs.
主要成果:
- 该研究包括15741名SAB患者,其中4.18%被诊断为SA-IE.
- 开发的风险得分达到0.74的AUCROC,证明了良好的预测性能.
- 确定的主要预测因素是年龄,先前感染性内心炎,膜性心脏病和社区发病.
结论:
- 在SAB患者中成功开发了SA-IE的新型客观风险评分.
- 风险评分的表现与现有方法可比,可以在患者管理的早期应用.
- 这种工具有助于早期风险分层,有助于在SAB诊断时的临床决策.
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