Daphna Landau Prat, Noa Kapelushnik, Mattan Arazi, Ofira Zloto, Ari Leshno, Eyal Klang, Sigal Sina, Shlomo Segev, Shahar Soudry, Guy J Ben Simon
{"title":"基于眼部和全身检查结果的青光眼预测模型","authors":"Daphna Landau Prat, Noa Kapelushnik, Mattan Arazi, Ofira Zloto, Ari Leshno, Eyal Klang, Sigal Sina, Shlomo Segev, Shahar Soudry, Guy J Ben Simon","doi":"10.1159/000535879","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Our aim was to explore the impact of various systemic and ocular findings on predicting the development of glaucoma.</p><p><strong>Methods: </strong>Medical records of 37,692 consecutive patients examined at a single medical center between 2001 and 2020 were analyzed using machine learning algorithms. Systemic and ocular features were included. Univariate and multivariate analyses followed by CatBoost and Light gradient-boosting machine prediction models were performed. Main outcome measures were systemic and ocular features associated with progression to glaucoma.</p><p><strong>Results: </strong>A total of 7,880 patients (mean age 54.7 ± 12.6 years, 5,520 males [70.1%]) were included in a 3-year prediction model, and 314 patients (3.98%) had a final diagnosis of glaucoma. The combined model included 185 systemic and 42 ocular findings, and reached an ROC AUC of 0.84. The associated features were intraocular pressure (48.6%), cup-to-disk ratio (22.7%), age (8.6%), mean corpuscular volume (MCV) of red blood cell trend (5.2%), urinary system disease (3.3%), MCV (2.6%), creatinine level trend (2.1%), monocyte count trend (1.7%), ergometry metabolic equivalent task score (1.7%), dyslipidemia duration (1.6%), prostate-specific antigen level (1.2%), and musculoskeletal disease duration (0.5%). The ocular prediction model reached an ROC AUC of 0.86. Additional features included were age-related macular degeneration (10.0%), anterior capsular cataract (3.3%), visual acuity (2.0%), and peripapillary atrophy (1.3%).</p><p><strong>Conclusions: </strong>Ocular and combined systemic-ocular models can strongly predict the development of glaucoma in the forthcoming 3 years. Novel progression indicators may include anterior subcapsular cataracts, urinary disorders, and complete blood test results (mainly increased MCV and monocyte count).</p>","PeriodicalId":19662,"journal":{"name":"Ophthalmic Research","volume":" ","pages":"29-38"},"PeriodicalIF":2.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Glaucoma Prediction Models Based on Ocular and Systemic Findings.\",\"authors\":\"Daphna Landau Prat, Noa Kapelushnik, Mattan Arazi, Ofira Zloto, Ari Leshno, Eyal Klang, Sigal Sina, Shlomo Segev, Shahar Soudry, Guy J Ben Simon\",\"doi\":\"10.1159/000535879\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Our aim was to explore the impact of various systemic and ocular findings on predicting the development of glaucoma.</p><p><strong>Methods: </strong>Medical records of 37,692 consecutive patients examined at a single medical center between 2001 and 2020 were analyzed using machine learning algorithms. Systemic and ocular features were included. Univariate and multivariate analyses followed by CatBoost and Light gradient-boosting machine prediction models were performed. Main outcome measures were systemic and ocular features associated with progression to glaucoma.</p><p><strong>Results: </strong>A total of 7,880 patients (mean age 54.7 ± 12.6 years, 5,520 males [70.1%]) were included in a 3-year prediction model, and 314 patients (3.98%) had a final diagnosis of glaucoma. The combined model included 185 systemic and 42 ocular findings, and reached an ROC AUC of 0.84. The associated features were intraocular pressure (48.6%), cup-to-disk ratio (22.7%), age (8.6%), mean corpuscular volume (MCV) of red blood cell trend (5.2%), urinary system disease (3.3%), MCV (2.6%), creatinine level trend (2.1%), monocyte count trend (1.7%), ergometry metabolic equivalent task score (1.7%), dyslipidemia duration (1.6%), prostate-specific antigen level (1.2%), and musculoskeletal disease duration (0.5%). The ocular prediction model reached an ROC AUC of 0.86. Additional features included were age-related macular degeneration (10.0%), anterior capsular cataract (3.3%), visual acuity (2.0%), and peripapillary atrophy (1.3%).</p><p><strong>Conclusions: </strong>Ocular and combined systemic-ocular models can strongly predict the development of glaucoma in the forthcoming 3 years. Novel progression indicators may include anterior subcapsular cataracts, urinary disorders, and complete blood test results (mainly increased MCV and monocyte count).</p>\",\"PeriodicalId\":19662,\"journal\":{\"name\":\"Ophthalmic Research\",\"volume\":\" \",\"pages\":\"29-38\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ophthalmic Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1159/000535879\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/12/18 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ophthalmic Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000535879","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/12/18 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
Glaucoma Prediction Models Based on Ocular and Systemic Findings.
Introduction: Our aim was to explore the impact of various systemic and ocular findings on predicting the development of glaucoma.
Methods: Medical records of 37,692 consecutive patients examined at a single medical center between 2001 and 2020 were analyzed using machine learning algorithms. Systemic and ocular features were included. Univariate and multivariate analyses followed by CatBoost and Light gradient-boosting machine prediction models were performed. Main outcome measures were systemic and ocular features associated with progression to glaucoma.
Results: A total of 7,880 patients (mean age 54.7 ± 12.6 years, 5,520 males [70.1%]) were included in a 3-year prediction model, and 314 patients (3.98%) had a final diagnosis of glaucoma. The combined model included 185 systemic and 42 ocular findings, and reached an ROC AUC of 0.84. The associated features were intraocular pressure (48.6%), cup-to-disk ratio (22.7%), age (8.6%), mean corpuscular volume (MCV) of red blood cell trend (5.2%), urinary system disease (3.3%), MCV (2.6%), creatinine level trend (2.1%), monocyte count trend (1.7%), ergometry metabolic equivalent task score (1.7%), dyslipidemia duration (1.6%), prostate-specific antigen level (1.2%), and musculoskeletal disease duration (0.5%). The ocular prediction model reached an ROC AUC of 0.86. Additional features included were age-related macular degeneration (10.0%), anterior capsular cataract (3.3%), visual acuity (2.0%), and peripapillary atrophy (1.3%).
Conclusions: Ocular and combined systemic-ocular models can strongly predict the development of glaucoma in the forthcoming 3 years. Novel progression indicators may include anterior subcapsular cataracts, urinary disorders, and complete blood test results (mainly increased MCV and monocyte count).
期刊介绍:
''Ophthalmic Research'' features original papers and reviews reporting on translational and clinical studies. Authors from throughout the world cover research topics on every field in connection with physical, physiologic, pharmacological, biochemical and molecular biological aspects of ophthalmology. This journal also aims to provide a record of international clinical research for both researchers and clinicians in ophthalmology. Finally, the transfer of information from fundamental research to clinical research and clinical practice is particularly welcome.