积极的血液培养的快速分类. 一个多变量算法的前景验证
1Division of General Medicine, Brigham and Women's Hospital, Boston, Mass. 02115.
JAMA
|April 8, 1992
概括
一个新的模型有助于确定阳性血液培养是否是真正的感染或污染. 这种工具有助于临床医生更快,更明智地做出关于患者护理的决定.
科学领域:
- 临床微生物学 临床微生物学
- 传染病 传染病 传染病
- 医疗信息学 医疗信息学
背景情况:
- 阳性血液培养对于诊断细菌病至关重要,但可以受到污染.
- 区分真正的细菌病与污染对于适当的患者管理和抗生素管理至关重要.
研究的目的:
- 开发和验证真正阳性血液培养的预测模型.
- 利用在初始培养结果报告时可用的信息.
主要方法:
- 具有导出和验证集的前性队列研究.
- 在最初的血液培养后24小时内收集的临床数据.
- 多变量分析确定了细菌病的预测因素.
主要成果:
- 关键预测因素包括生物体类型,阳性化时间,多种阳性培养和临床风险评分.
- 一个四个风险组模型在区分真实阳性和污染物方面表现出高准确性.
- 在衍生组中,最低风险组的92%是污染物,最高风险组的97%是真正阳性.
结论:
- 开发的模型有效量化了从阳性血液培养物中真正细菌病的可能性.
- 该工具支持在最初的实验室通知时进行临床决策.
- 在解释血液培养的准确性提高可以优化患者的治疗和抗生素的使用.
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