自动药敏试验的统计学方法：根据生长曲线预测贝叶斯最小抑菌浓度
Statistical methods for automated drug susceptibility testing: Bayesian minimum inhibitory concentration prediction from growth curves
摘要：
确定防止微生物生长的药物的最低抑菌浓度（MIC）是管理感染患者的重要步骤。在本文中，我们提出了一种新的概率方法，准确地估计MIC的基础上面板的多个曲线反映细菌生长的功能。我们开发了一个概率模型，用于确定给定的抗菌剂稀释度是否是MIC，给出了随时间推移的生长曲线的特征。由于潜在的大量特征集合，我们利用贝叶斯模型选择将预测变量的集合缩小到最重要的变量。除了MIC的点估计值外，我们还能够根据观察到的生长曲线提供每个稀释度为MIC的后验概率。该方法易于自动化，并已被纳入Becton-Dickinson PHOENIX自动化药敏系统，该系统可快速准确地对临床样本中大量微生物的耐药性进行分类。迄今为止，已有超过75项研究表明，这种新方法比现有方法更好地估计了MIC。
英文摘要：
Determination of the minimum inhibitory concentration (MIC) of a drug that prevents microbial growth is an important step for managing patients with infections. In this paper we present a novel probabilistic approach that accurately estimates MICs based on a panel of multiple curves reflecting features of bacterial growth. We develop a probabilistic model for determining whether a given dilution of an antimicrobial agent is the MIC given features of the growth curves over time. Because of the potentially large collection of features, we utilize Bayesian model selection to narrow the collection of predictors to the most important variables. In addition to point estimates of MICs, we are able to provide posterior probabilities that each dilution is the MIC based on the observed growth curves. The methods are easily automated and have been incorporated into the Becton--Dickinson PHOENIX automated susceptibility system that rapidly and accurately classifies the resistance of a large number of microorganisms in clinical samples. Over seventy-five studies to date have shown this new method provides improved estimation of MICs over existing approaches.
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