William Rojas-Carabali, Carlos Cifuentes-González, Kerry Goetz, Maria Vittoria Cicinelli, Zheng Xian Thng, Sally L Baxter, Edmund Tsui, Padmamalini Mahendradas, Jyotirmay Biswas, Sofia Androudi, Andre Luiz Land Curi, Su Ling Ho, Alfredo Adán, Rina La Distia Nora, Claudio Silveira, Heloisa Nascimento, João M Furtado, Cristina Muccioli, Germán Mejía-Salgado, Cristhian A Urzua, Justus G Garweg, Ariel Schlaen, Xin Wei, Sivaraman Balamurugan, Ranju Kharel Sitaula, Ikhwanuliman Putera, Marcelo Rudzinski, Kalpana Babu, Mark Westcott, Rubens Belfort, Justine R Smith, Jorge Gomez-Marin, Quan Dong Nguyen, Vishali Gupta, Rupesh Agrawal, Alejandra de-la-Torre
{"title":"Common data elements for observational studies in ocular toxoplasmosis: a Delphi consensus.","authors":"William Rojas-Carabali, Carlos Cifuentes-González, Kerry Goetz, Maria Vittoria Cicinelli, Zheng Xian Thng, Sally L Baxter, Edmund Tsui, Padmamalini Mahendradas, Jyotirmay Biswas, Sofia Androudi, Andre Luiz Land Curi, Su Ling Ho, Alfredo Adán, Rina La Distia Nora, Claudio Silveira, Heloisa Nascimento, João M Furtado, Cristina Muccioli, Germán Mejía-Salgado, Cristhian A Urzua, Justus G Garweg, Ariel Schlaen, Xin Wei, Sivaraman Balamurugan, Ranju Kharel Sitaula, Ikhwanuliman Putera, Marcelo Rudzinski, Kalpana Babu, Mark Westcott, Rubens Belfort, Justine R Smith, Jorge Gomez-Marin, Quan Dong Nguyen, Vishali Gupta, Rupesh Agrawal, Alejandra de-la-Torre","doi":"10.1186/s12348-025-00525-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Ocular toxoplasmosis (OT) is the most common cause of posterior uveitis globally, with a significant risk of visual impairment. However, the lack of standardized data collection hinders meaningful comparisons across studies. This study aimed to develop a consensus-based set of Common Data Elements (CDEs) for observational studies in OT using a Delphi approach.</p><p><strong>Design: </strong>A set of CDEs was developed through a combination of a comprehensive literature review, a hybrid workshop, and a Delphi consensus process. This effort was led by an international panel of experts in OT to define a standardized CDE set for research and clinical purposes.</p><p><strong>Methods: </strong>A multidisciplinary steering committee identified an initial list of candidate CDEs through a targeted literature review. A panel of 30 international experts participated in a structured, one-round Delphi process to evaluate and refine these CDEs. Consensus was determined based on predefined thresholds for inclusion, exclusion, and modification.</p><p><strong>Results: </strong>A total of 139 CDEs were categorized across nine domains: Demographic and Background Information, Medical and Ocular History, Clinical Presentation, Clinical Findings, Lesion Characteristics, Diagnostics, Imaging Findings, Treatment and Interventions, and Outcomes. All 139 CDEs met the inclusion criteria, with 79.8% rated as \"very important\". The consensus underscores the importance of a comprehensive, standardized dataset for OT research.</p><p><strong>Conclusions: </strong>This study establishes the first expert-derived standardized dataset requested for reporting OT outcomes, providing a framework to standardize data collection for future observational studies. Adopting these CDEs will enhance data comparability, improve meta-analyses, and strengthen the evidence base for clinical decision-making in OT. Future work will focus on real-world validation and refinement of this dataset.</p>","PeriodicalId":16600,"journal":{"name":"Journal of Ophthalmic Inflammation and Infection","volume":"15 1","pages":"68"},"PeriodicalIF":2.3000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12460859/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ophthalmic Inflammation and Infection","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s12348-025-00525-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
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Abstract
Purpose: Ocular toxoplasmosis (OT) is the most common cause of posterior uveitis globally, with a significant risk of visual impairment. However, the lack of standardized data collection hinders meaningful comparisons across studies. This study aimed to develop a consensus-based set of Common Data Elements (CDEs) for observational studies in OT using a Delphi approach.
Design: A set of CDEs was developed through a combination of a comprehensive literature review, a hybrid workshop, and a Delphi consensus process. This effort was led by an international panel of experts in OT to define a standardized CDE set for research and clinical purposes.
Methods: A multidisciplinary steering committee identified an initial list of candidate CDEs through a targeted literature review. A panel of 30 international experts participated in a structured, one-round Delphi process to evaluate and refine these CDEs. Consensus was determined based on predefined thresholds for inclusion, exclusion, and modification.
Results: A total of 139 CDEs were categorized across nine domains: Demographic and Background Information, Medical and Ocular History, Clinical Presentation, Clinical Findings, Lesion Characteristics, Diagnostics, Imaging Findings, Treatment and Interventions, and Outcomes. All 139 CDEs met the inclusion criteria, with 79.8% rated as "very important". The consensus underscores the importance of a comprehensive, standardized dataset for OT research.
Conclusions: This study establishes the first expert-derived standardized dataset requested for reporting OT outcomes, providing a framework to standardize data collection for future observational studies. Adopting these CDEs will enhance data comparability, improve meta-analyses, and strengthen the evidence base for clinical decision-making in OT. Future work will focus on real-world validation and refinement of this dataset.