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

量化微生物角膜炎以预测结果：影像学和流行病学方法

• 批准号: 10357921
• 负责人: Maria Anneke Woodward
• 金额: $ 39.73万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10357921
• 关键词: Algorithms; Antimicrobial Resistance; Blindness; Clinic; Clinical; Clinical Data; Clinical Management; Clinical Trials; Complex; Control Groups; Cornea; Corneal Diseases; Data; Data Collection; Databases; Diabetic Retinopathy; Disease; Disease Progression; Electronic Health Record; Enrollment; Environment; Epidemiology; Equipment; Evaluation; Eye; Future; Goals; Health; Image; Image Analysis; Inflammatory; Keratitis; Link; Measurable; Measurement; Measures; Methods; Michigan; Modeling; Morphology; National Eye Institute; Organism; Outcome; Participant; Patient Outcomes Assessments; Patients; Pharmaceutical Preparations; Physicians; Provider; Research; Resource-limited setting; Risk; Risk Factors; Self-Examination; Severities; Standardization; Strategic Planning; Surveys; Symptoms; System; Technology; Testing; Treatment Efficacy; Universities; Virulence; Vision; Visual Acuity; Work; antimicrobial; automated algorithm; base; care systems; case control; clinical risk; cost; deep learning; epidemiology study; healing; improved outcome; individualized medicine; innovation; microbial; novel; open source; outcome prediction; patient stratification; performance tests; personalized care; personalized medicine; primary outcome; prospective; risk stratification; slit lamp imaging; tool; treatment planning; treatment response

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.

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，有急需 为微生物角膜炎创建风险分层系统。微生物角膜炎（MK），令人衰弱， 传染性角膜疾病估计是全球失明的第四个领先原因。 MK 严重程度取决于患者，组织和环境的复杂相互作用，导致 临床表现的范围和对治疗的反应。临床演示表现为 独特的形态特征和临床症状。形态学特征在角膜中可见，并且 症状是可以测量的。但是大多数患者接受了非特异性广谱治疗 抗菌剂，一种增加抗菌耐药性的方法。这种非特异性治疗 方法与独特的MK演示文稿缺乏一致。对新的迫切需要 个性化MK和测量治疗效率治疗的策略。具有量化的MK 形态学和临床特征，临床医生将拥有风险分层患者的工具。长期 目标是为MK患者制定快速，客观，个性化的治疗计划。该提议的 目的是使用图像和电子健康量化动态形态和临床MK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。 拟议的研究将检验以下假设：形态和临床特征准确风险 - 分层患者的角膜和视力结果。我们的前提得到了初步数据的支持 证明：（1）不同的生物产生独特的形态和临床特征； （2） 临床医生对形态的量化比图像 - 分析方法更精确，（3）专家能够使用 MK特征量身定制治疗； （4）量化功能的使用有改善的结果 通过帮助提供者量身定制治疗方法，例如糖尿病性视网膜病变； （5）EHR数据可以是 用于准确量化和分类临床疾病特征； （6）可以使用EHR数据 AIM 1将开发客观的图像分析工具来衡量功能 现有临床设备的MK。 AIM 2将使用形态学评估MK治疗效率 前瞻性调查的图像分析和临床特征。 AIM 3将风险分层MK患者 通过组合图像分析和EHR提取的数据。预期的结果是：（1）表征 MK图像和链接的临床数据的数据库，（2）跨频谱的量化MK特征 临床演示，（3）进行性能测试的开源成像算法和调查以测量 MK标记动态，（4）新型风险分层模型和评分系统。结果 工作将对临床医生具有重要的价值。临床医生可以使用实用的低成本技术，并且 易于获取的EHR数据量化MK特征和风险分离患者，以量身定制治疗。