Improving Lyme disease diagnosis through bioinformatics

由于医生对莱姆病（LD）诊断标准的知识有限， 实验室检查、LD的过度诊断和过度治疗已成为一个重大的公共卫生问题。 这项拨款是两部分过程的第一阶段，该过程将开发一个生物信息学框架，以最大限度地提高生物信息学的效率。 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检测 通过全细胞EIA检测针对伯氏疏螺旋体的抗体，随后通过蛋白质印迹确认阳性或阴性抗体。 EIA结果不明确;虽然这种方法具有高度特异性，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，将LD的预测试风险与多抗体相结合 血清学以生成每个患者的后验概率。这种新算法已经证明了灵敏度 上级试点研究中的两步法。为了向算法提供比较器， 模型将同时使用部分ROC回归和逻辑线性回归开发， 惩罚似然函数。来自280名LD患者和559名对照的数据，已经使用两种- 分步方法和VISE、C6和pepdO ElAs将用于开发这些模型。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那些变量。 将根据现有临床数据估计每例患者和对照的LD试验前风险。部分 将为每种预测方法和两步法生成ROC曲线（至少80%特异性） 通过改变它们各自的后验概率截止值。每种方法的诊断能力将是 通过其部分ROC曲线下面积（AUC）确定，并与两步法进行比较 使用bootstrap技术。主要研究终点是确定至少一种新的预测方法 其性能等同于或上级两步法，从而证明II期研究是合理的。相 II将包括一项前瞻性多中心研究，从不同的数据集中收集血清和临床数据 LD患者和对照组。使用第一阶段开发的生物统计技术， 血清学预测模型将在II期开发。这种生物信息学方法有可能 使用标准化血清库整合临床和血清学诊断方法。

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 two-part process that will develop a bioinformatic framework to maximize the predictive power of new and existing laboratorytests. The current two-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 two-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 two- 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 two-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 two-step approach using a bootstrap technique. The primary study end-point is to identify at least one new predictive method with performance equivalent or superior to the two-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.