开发和验证一种初步的多变量诊断模型,用于识别住院患者的异常感染
Aysun Tekin1, Mohammad Joghataee2, Lucrezia Rovati3
1Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
Biomolecules & biomedicine
|April 21, 2024
概括
早期诊断不寻常的感染,如真菌病和结核病至关重要. 一种新的电子健康记录 (EHR) 模型显示出在医院住院患者中识别这些罕见疾病的潜力,可能减少诊断延迟.
科学领域:
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
- 诊断建模 诊断建模
背景情况:
- 感染的诊断延迟,特别是不寻常的感染,导致患者的治疗结果较差.
- 在临床实践中,及时考虑罕见的致病原体往往是一个挑战.
- 电子健康记录 (EHR) 为开发预测诊断工具提供了丰富的数据来源.
研究的目的:
- 开发和验证一种初步的多变量诊断模型,用于在住院患者中早期识别异常真菌感染和结核病.
- 利用常规收集的EHR数据来提高罕见疾病的诊断准确性.
- 评估模型在多中心环境中的性能.
主要方法:
- 采用了双门病例控制研究设计.
- 这些病例包括被诊断患有特定真菌感染的成年患者 (芽细胞菌,密码菌,细胞体菌,粘膜菌,肺结核病) 或结核病.
- 从EHR数据中构建了一个后勤回归模型,使用基线和实验室变量,与随机和感染疾病对照进行验证.
主要成果:
- 该模型在导出数据集中实现了0.88的AUC,表明强大的歧视力.
- 验证数据集显示了可比性能,AUC为0.88和0.84.
- 该模型在导出集中显示出良好的校准准确性 (霍斯默-莱梅肖P = 0.623).
结论:
- 一个初步的基于EHR的诊断模型有效地在多个中心的住院患者中发现了不寻常的真菌感染和结核病.
- 开发的模型显示了减少这些罕见但严重疾病的诊断延迟的潜力.
- 建议进一步验证,将该工具纳入临床实践,以改善患者护理.
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