Quantifying Microbial Keratitis to Predict Outcomes: An Imaging and Epidemiologic Approach

项目摘要/摘要 对于流行病学研究、未来的临床试验和个性化的患者护理，迫切需要 建立微生物角膜炎的风险分层系统。微生物角膜炎(MK)，一种使人虚弱的， 据估计，传染性角膜疾病是全球第四大致盲原因。米克 严重程度取决于患者、有机体和环境的复杂相互作用，导致 临床表现和治疗反应的谱系。临床表现表现为 独特的形态特征和临床症状。形态特征在角膜中可见，并且 症状是可以测量的。但大多数患者是用非特异性广谱治疗的。 抗菌剂，一种增加抗菌素耐药性的方法。这种非特定的治疗方法 方法与独特的MK演示缺乏一致性。迫切需要一种新的 MK个体化治疗和疗效评估的策略。使用量化的MK 根据患者的形态和临床特征，临床医生将拥有对患者进行风险分层的工具。长期的 目标是为MK患者制定快速、客观、个性化的治疗计划。这项建议是 目的是利用图像和电子健康来量化MK的动态形态和临床特征 记录(EHR)分析，然后建立与MK结果相关的风险分层评分系统。 这项拟议的研究将检验这样一种假设，即形态和临床特征准确地风险- 对患者的角膜和视力结果进行分层。我们的假设得到初步数据的支持 说明：(1)不同的生物产生不同的形态和临床特征；(2) 临床医生对形态的量化不如图像分析方法精确，(3)专家能够使用 MK特征以量身定制治疗；(4)量化特征的使用改善了其他 疾病，如糖尿病视网膜病变，通过帮助提供者量身定做治疗；(5)EHR数据可以 用于准确量化和分类临床疾病特征；以及(6)可以使用EHR数据 有效地对患者进行风险分层。目标1将开发客观的图像分析工具来测量特征 利用现有的临床设备。AIM 2将使用形态学方法评估MK治疗效果 来自前瞻性调查的图像分析和临床特征。目标3将对MK患者进行风险分层 通过图像分析和电子病历相结合的方法提取数据。预期结果如下：(1)特征 MK图像和相关联的临床数据的数据库，(2)量化MK特征 临床演示，(3)经过性能测试的开放源码成像算法和调查 (4)提出了一种新的风险分层模型和评分系统。由此产生的 这项工作将对临床医生具有重大价值。临床医生可以使用实用、低成本的技术和 易于获得的EHR数据，以量化MK特征和对患者进行风险分层，以便量身定制治疗。

PROJECT SUMMARY/ABSTRACT For epidemiological studies, future clinical trials, and personalized patient care, there is a critical need to create a risk-stratification system for microbial keratitis. Microbial keratitis (MK), a debilitating, infectious corneal disease, is estimated to be the fourth-leading cause of blindness worldwide. MK severity depends on a complex interaction of patient, organism, and environment, resulting in a spectrum of clinical presentations and responses to treatment. Clinical presentations manifest with unique morphology features and clinical symptoms. Morphology features are visible in the cornea, and symptoms are measurable. But most patients are treated with non-specific broad-spectrum antimicrobials, an approach that increases antimicrobial resistance. This non-specific treatment approach lacks congruence with the unique MK presentations. There is a critical need for a new strategy to personalize treatments for MK and measure treatment efficacy. With quantified MK morphologic and clinical features, clinicians will have the tools to risk-stratify patients. The long-term goal is to develop rapid, objective, personalized treatment plans for patients with MK. This proposal’s objective is to quantify dynamic morphologic and clinical MK features using image and electronic health record (EHR) analyses and then build a risk-stratification scoring system associated with MK outcomes. The proposed research will test the hypothesis that morphologic and clinical features accurately risk- stratify patients for corneal and vision outcomes. Our premise is supported by preliminary data demonstrating that: (1) different organisms generate distinct morphologic and clinical features; (2) clinicians quantify morphology less precisely than image-analysis methods, (3) an expert is able to use MK features to tailor treatments; (4) the use of quantified features has improved outcomes in other diseases, such as diabetic retinopathy, by helping providers to tailor treatments; (5) EHR data can be used to quantify and classify clinical disease features accurately; and (6) EHR data can be used effectively to risk-stratify patients. Aim 1 will develop objective image analysis tools to measure features of MK with existing clinical equipment. Aim 2 will evaluate MK treatment efficacy using morphologic image analysis and clinical features from prospective surveys. Aim 3 will risk-stratify patients with MK by combining image analysis and EHR extracted data. The expected outcomes are: (1) characterized databases of MK images and linked clinical data, (2) quantified MK features across a spectrum of clinical presentations, (3) performance-tested, open-source imaging algorithms and surveys to measure MK markers dynamically, and (4) a novel risk stratification model and scoring system. The resultant work will have significant value to clinicians. Clinicians can use practical, low-cost technologies and readily-available EHR data to quantify MK features and risk-stratify patients in order to tailor treatments.