Tubercular Uveitis: Nuggets from Collaborative Ocular Tuberculosis Study (COTS)-1
Yazar
Cimino, Luca
Barisani-Asenbauer, Talin
Gonzalez-Lopez, Julio J.
Androudi, Sofia
Bansal, Reema
Moharana, Bruttendu
Degli Esposti, Simona
Tasiopoulou, Anastasia
Nadarajah, Sengal
Agarwal, Mamta
Abraham, Sharanya
Vala, Ruchi
Singh, Ramandeep
Sharma, Aman
Sharma, Kusum
Zierhut, Manfred
Rousselot, Andres
Grant, Robert
Kon, Onn Min
Cunningham, Emmett T.
Kempen, John
Quan Dong Nguyen, Quan Dong Nguyen
Pavesio, Carlos
Gupta, Vishali
Tugal-Tutkun, Ilknur
Testi, Ilaria
Agrawal, Rupesh
Mahajan, Sarakshi
Agarwal, Aniruddha
Gunasekeran, Dinesh Visva
Raje, Dhananjay
Aggarwal, Kanika
Murthy, Somasheila
Westcott, Mark
Chee, Soon Phaik
McCluskey, Peter
Ho, Su Ling
Teoh, Stephen
Biswas, Jyotirmay
Narain, Shishir
Agarwal, Manisha
Mahendradas, Padmamalini
Khairallah, Moncef
Jones, Nicholas
Babu, Kalpana
Basu, Soumayava
Carreno, Ester
Lee, Richard
Al-Dhibi, Hassan
Bodaghi, Bahram
Invernizzi, Alessandro
Goldstein, Debra A.
Herbort, Carl P.
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Background: Tuberculosis (TB) is a major infection that can affect the eye as first and sole presentation without features of systemic disease. Controversy exists regarding diagnosis and management of tubercular uveitis (TBU), further compounded by regional variations in disease expression. Purpose: Collaborative Ocular Tuberculosis Study (COTS)-1 aims to address knowledge deficits through collaboration amongst uveitis specialists across the globe by sharing the data of patients with TBU presented at participating centers from January 2004 to December 2014. Methods: Data collection was facilitated by a novel method of real-time encrypted web-based data entry allowing regular updates as new data and recommendations become available. Results: Information on clinical features, investigation findings, management, and treatment outcomes were reviewed to get an idea about real world scenario. Conclusion: The current review aims to focus on methodology and briefing of published reports from COTS group in COTS-1 study to highlight key messages from this large data.
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