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开发和验证一个温和的预测模型的积极的尿液培养在门诊诊所的访问
Ghadeer O Ghosheh1, Terrence Lee St John2, Pengyu Wang1
1NYU Abu Dhabi, Abu Dhabi, The United Arab Emirates.
PLOS digital health
|November 1, 2023
概括
一个新的预测模型使用最小的患者数据准确地识别了阳性尿液培养,改善了滴针测试. 这种工具提高了临床效率,特别是在资源有限的环境中.
科学领域:
- 医疗信息学 医疗信息学
- 临床诊断 临床诊断 临床诊断
- 传染病研究 传染病研究
背景情况:
- 尿液培养是诊断尿路感染的黄金标准,但缓慢且昂贵.
- 尿液测量棒测试提供了快速的结果,但往往缺乏准确性 (灵敏度/特异性).
- 需要有效,准确的尿道感染诊断工具.
研究的目的:
- 开发和验证积极的尿液培养的节预测模型.
- 为了评估模型的性能与仅测量棒分析相比.
- 评估该模型在护理点部署方面的潜力.
主要方法:
- 利用了17,572例门诊患者与尿液培养 (2015-2021) 的现实数据集.
- 开发了一个使用十个特征的预测模型:生命体征,患者病史和测量棒结果.
- 在5339次遭遇的测试组中,使用接收器运行特征曲线下的区域 (AUROC) 评估模型性能.
主要成果:
- 储蓄型模型的AUROC为0.828 (95% CI:0.810-0.844) 预测细菌数量≥105 CFU/ml.
- 这一结果优于仅使用测量棒的模型,该模型的AUROC为0.786 (95%CI:0.769-0.806).
- 该模型以最小的输入数据展示了强大的预测能力.
结论:
- 一个简单的,节的模型有效地预测了积极的尿液培养.
- 拟议的模型提供了优越的准确性,与单独的测量棒测试相比.
- 该工具具有显著的潜力,可以提高临床工作流程的效率,特别是在资源有限的环境中.
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