Improving Lyme disease diagnosis through bioinformatics

描述(申请人提供)：由于医生对莱姆病(LD)诊断标准的了解有限，以及过度使用实验室测试，LD的过度诊断和过度治疗已成为一个严重的公共卫生问题。这笔赠款是一个由两部分组成的过程的第一阶段，该过程将开发一个生物信息学框架，以最大限度地发挥新的和现有的实验室测试的预测能力。目前的两步血清学方法是通过全细胞EIA检测伯氏疏螺旋体抗体，然后用免疫印迹法确认阳性或可疑的EIA结果；虽然这种方法具有高度的特异性，但对早期LD缺乏敏感性。我们将评估一种基于贝叶斯定理的新的专利算法，该算法将LD的预测风险与多抗体血清学相结合，以生成每个患者的后验概率。在一项初步研究中，这种新的算法表现出了优于两步法的灵敏度。为了给算法提供比较器，将同时使用部分ROC回归和Logistic-线性回归，并辅以惩罚似然函数来开发多变量模型。来自280名LD患者和559名对照的数据将被用来建立这些模型，这些数据已经使用两步法以及VISE、C6和PepdO ELA进行了测试。新算法将利用通过部分ROC回归选择的对预测模型有显著贡献的变量。将根据现有的临床数据对每个患者和对照组的LD的预测风险进行估计。通过改变各自的后验概率截止值，将为每种预测方法和两步法生成部分ROC曲线(至少80%特定)。每种方法的诊断能力将由其部分ROC曲线(AUC)下的面积来确定，并与使用Bootstrap技术的两步法进行比较。研究的主要终点是确定至少一种新的预测方法，其性能与两步法相当或更好，从而证明第二阶段研究的合理性。第二阶段将包括一项前瞻性的多中心研究，从一组不同的LD患者和对照组收集血清和临床数据。利用第一阶段开发的生物统计学技术，将在第二阶段开发新的临床和血清学预测模型。这种生物信息学方法具有使用标准化血清库整合临床和血清学诊断方法的潜力。

DESCRIPTION (provided by applicant): Due to limited physician knowledge of diagnostic criteria for Lyme disease (LD) and excessive utilization of laboratory tests, over-diagnosis and over-treatment of LD has become a significant public health problem. This grant is the first phase of a 2-part process that will develop a bioinformatic framework to maximize the predictive power of new and existing laboratory tests. The current 2-step serologic approach detects antibody to Borrelia burgdorferi by whole-cell EIA, followed by Western blot confirmation of positive or equivocal EIA results; while this approach is highly specific, it lacks sensitivity for early LD. We will evaluate a new, patented algorithm based on Bayes' theorem that combines the pretest risk of LD with multi-antibody serology to generate a posterior probability for each patient. This new algorithm has demonstrated sensitivity superior to the 2-step method in a pilot study. To provide comparators to the algorithm, multivariate models will be developed concurrently using partial ROC regression and logistic-linear regression, assisted by penalized likelihood functions. Data from 280 LD patients and 559 controls, already tested using the 2-step method and VIsE, C6, and pepdO ElAs, will be used to develop these models. The new algorithm will utilize those variables selected by partial ROC regression that contribute significantly to the predictive model. The pretest risk of LD will be estimated for each patient and control based on available clinical data. A partial ROC curve (at least 80% specific) will be generated for each predictive method and the 2-step approach by varying their respective posterior probability cutoffs. The diagnostic power of each method will be determined by the area under its partial ROC curve (AUC), and compared to that of the 2-step approach using a bootstrap technique. The primary study end-point is to identify at least 1 new predictive method with performance equivalent or superior to the 2-step approach, thereby justifying a Phase II study. Phase II will consist of a prospective multi-centered study to collect both serum and clinical data from a diverse set of LD patients and controls. Using the biostatistical techniques developed in Phase I, new clinical and serologic predictive models will be developed in Phase II. This bioinformatic approach has the potential for integrating clinical and serologic diagnostic approaches using a standardized serum bank.