Dinesh Visva Gunasekeran1,2,3,4, Rupesh Agrawal2,3, Ilaria Testi3, Aniruddha Agarwal5, Sarakshi Mahajan4, Quan Dong Nguyen4, Carlos Pavesio2, Vishali Gupta5. 1. National University of Singapore (NUS), Singapore, Singapore. 2. National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, Singapore. 3. Moorfields Eye Hospital, NHS Foundation Trust, London, UK. 4. Byers Eye Institute, Stanford Medicine, Palo Alto, CA, USA. 5. Department of Ophthalmology, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India.
Abstract
Purpose: There is controversy regarding the diagnosis and management of ocular tuberculosis (TB) due to lack of robust evidence. The Collaborative Ocular Tuberculosis Study (COTS) was conducted in stages to enable swift, accurate data collection across 25 participating centers.Method: Data collection was facilitated by a cloud-based data aggregation platform with programmed logic based on anecdotal evidence from uveitis experts corroborated with literature review. Results: The platform enabled standardization of interpretation and collection of data from patient medical records. The pre-programmed logic also ensured the platform only prompted the entry of relevant data based on initial data entered for each unit of analysis. This enabled collection of the vast amounts of data without compromising either of the breadth nor the depth of data collection. Conclusion: The final output from this effort was an in-depth retrospective analysis to facilitate the design of future prospective investigations on ocular TB and develop best practice guidelines.
Purpose: There is controversy regarding the diagnosis and management of ocular tuberculosis (TB) due to lack of robust evidence. The Collaborative Ocular Tuberculosis Study (COTS) was conducted in stages to enable swift, accurate data collection across 25 participating centers.Method: Data collection was facilitated by a cloud-based data aggregation platform with programmed logic based on anecdotal evidence from uveitis experts corroborated with literature review. Results: The platform enabled standardization of interpretation and collection of data from patient medical records. The pre-programmed logic also ensured the platform only prompted the entry of relevant data based on initial data entered for each unit of analysis. This enabled collection of the vast amounts of data without compromising either of the breadth nor the depth of data collection. Conclusion: The final output from this effort was an in-depth retrospective analysis to facilitate the design of future prospective investigations on ocular TB and develop best practice guidelines.
Authors: Dinesh Visva Gunasekeran; Zhenghong Liu; Win Jim Tan; Joshua Koh; Chiu Peng Cheong; Lay Hong Tan; Chee Siang Lau; Gaik Kheng Phuah; Newsie Donnah A Manuel; Che Chong Chia; Gek Siang Seng; Nancy Tong; May Hang Huin; Suzette Villaluna Dulce; Susan Yap; Kishanti Ponampalam; Hao Ying; Marcus Eng Hock Ong; R Ponampalam Journal: J Med Internet Res Date: 2020-06-15 Impact factor: 5.428
Authors: Onn Min Kon; Nicholas Beare; David Connell; Erika Damato; Thomas Gorsuch; Guy Hagan; Felicity Perrin; Harry Petrushkin; Jessica Potter; Charanjit Sethi; Miles Stanford Journal: BMJ Open Respir Res Date: 2022-03