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: 10674082
• 负责人: Maria Anneke Woodward
• 金额: $ 5.97万
• 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/10674082
• 关键词: Algorithms; Antimicrobial Resistance; Blindness; Clinical; Clinical Data; Clinical Management; Clinical Trials; Complex; Cornea; Corneal Diseases; Data; Data Collection; Databases; Diabetic Retinopathy; Disease; Early Intervention; Electronic Health Record; Enrollment; Environment; Epidemiology; Equipment; Etiology; Eye; Future; Goals; Health; Image; Image Analysis; Inflammatory; Intervention; Keratitis; Linear Regressions; Link; Literature; Measurable; Measures; Methods; Michigan; Modeling; Morphology; Organism; Outcome; Participant; Patient Outcomes Assessments; Patient-Focused Outcomes; Patients; Physicians; Provider; Research; Research Personnel; Risk; Risk Factors; Self-Examination; Severities; Severity of illness; Standardization; Strategic Planning; Surveys; Symptoms; System; Techniques; Technology; Testing; Time; Treatment Efficacy; Universities; Vision; Visual Acuity; Work; antimicrobial; base; care systems; comparison group; cost; deep learning; epidemiology study; healing; improved outcome; individualized medicine; innovation; insight; longitudinal database; 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.

项目总结/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， 建立一个微生物角膜炎的风险分层系统。微生物性角膜炎（MK），一种使人衰弱， 传染性角膜疾病估计是全世界第四大致盲原因。MK 严重程度取决于患者、生物体和环境的复杂相互作用， 临床表现和对治疗的反应谱。临床表现为 独特的形态学特征和临床症状。角膜中的形态特征可见， 症状是可测量的。但大多数患者接受的是非特异性广谱 抗生素，一种增加抗生素耐药性的方法。这种非特异性治疗 方法缺乏与独特的MK演示文稿的一致性。迫切需要一个新的 策略，以个性化治疗MK和衡量治疗效果。量化MK 形态学和临床特征，临床医生将有工具来对患者进行风险分层。长期 我们的目标是为MK患者制定快速，客观，个性化的治疗计划。这个提议 目的是使用图像和电子健康来量化MK的动态形态学和临床特征 记录（EHR）分析，然后建立与MK结果相关的风险分层评分系统。 拟议的研究将测试形态和临床特征准确风险的假设- 根据角膜和视力结果对患者进行分层。我们的假设得到了初步数据的支持 证明：（1）不同的生物体产生不同的形态学和临床特征;（2） 临床医生量化形态不如图像分析方法精确，（3）专家能够使用 MK功能，以定制治疗;（4）使用量化功能改善了其他 疾病，如糖尿病视网膜病变，通过帮助提供者定制治疗;（5）EHR数据可以 用于准确地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。AIM 1将开发客观的图像分析工具来测量特征 使用现有的临床设备。目的2将使用形态学评价MK治疗的疗效 图像分析和前瞻性调查的临床特征。目标3将对MK患者进行风险分层 通过结合图像分析和EHR提取的数据。预期结果是：（1）特征化 MK图像和相关临床数据的数据库，（2）量化MK特征， 临床表现，（3）性能测试，开源成像算法和调查，以衡量 MK标志物动态变化;（4）一种新的危险分层模型和评分系统。所得 这项工作将对临床医生有重大价值。临床医生可以使用实用、低成本的技术， 易于获得的EHR数据来量化MK特征和风险分层患者，以便定制治疗。

项目成果

Factors Associated With Laboratory Test Negativity Following a Transition in Specimen Collection in Microbial Keratitis Cases.

微生物性角膜炎病例标本采集转变后与实验室检测阴性相关的因素。

• DOI: 10.1080/02713683.2023.2294700
• 发表时间: 2024
• 期刊: Current eye research
• 影响因子: 2
• 作者: Miller,KeithD;Toiv,Avi;Deng,Callie;Lu,Ming-Chen;Niziol,LeslieM;Hart,JennaN;Sherman,Eric;Mian,ShahzadI;Lephart,PaulR;Sugar,Alan;Kang,Linda;Woodward,MariaA
• 通讯作者: Woodward,MariaA

Measurement Reliability for Keratitis Morphology.

• DOI: 10.1097/ico.0000000000002470
• 发表时间: 2020-12
• 期刊: Cornea
• 影响因子: 2.8
• 作者: Kriegel MF;Loo J;Farsiu S;Prajna V;Tuohy M;Kim KH;Valicevic AN;Niziol LM;Tan H;Ashfaq HA;Ballouz D;Woodward MA
• 通讯作者: Woodward MA

Prediction of Visual Acuity in Patients With Microbial Keratitis.

微生物性角膜炎患者视力的预测。

• DOI: 10.1097/ico.0000000000003129
• 发表时间: 2023
• 期刊: Cornea
• 影响因子: 2.8
• 作者: Woodward,MariaA;Niziol,LeslieM;Ballouz,Dena;Lu,Ming-Chen;Kang,Linda;Thibodeau,Alexa;Singh,Karandeep
• 通讯作者: Singh,Karandeep

Impact of Scleral Contact Lens Use on the Rate of Corneal Transplantation for Keratoconus.

• DOI: 10.1097/ico.0000000000002388
• 发表时间: 2021-01
• 期刊: Cornea
• 影响因子: 2.8
• 作者: Ling JJ;Mian SI;Stein JD;Rahman M;Poliskey J;Woodward MA
• 通讯作者: Woodward MA

Algorithm Variability in Quantification of Epithelial Defect Size in Microbial Keratitis Images.

• DOI: 10.1097/ico.0000000000002258
• 发表时间: 2020-05
• 期刊: Cornea
• 影响因子: 2.8
• 作者: Kriegel MF;Huang J;Ashfaq HA;Niziol LM;Preethi M;Tan H;Tuohy MM;Patel TP;Prajna V;Woodward MA
• 通讯作者: Woodward MA