Validation of the Online Collaborative Ocular Tuberculosis Study Calculator for Tubercular Uveitis
Authors: Zhang Ludi , Rojas-Carabali William , Choo Shannon Sheriel , Thng Zheng Xian , Lim Yuan Heng , Lee Bernett , Jun Song Wen , Patnaik Gazal , Biswas Jyotirmay , Agarwal Aniruddha , Testi Ilaria , Mahajan Sarakshi , Kempen John H. , Smith Justine R. , McCluskey Peter , Kon Onn Min , Nguyen Quan Dong , Pavesio Carlos , Gupta Vishali , Agrawal Rupesh ,   Collaborative Ocular Tuberculosis Study (COTS) Group, Agrawal 1 2 3 Rupesh , Testi 2  Ilaria , Mahajan 4  Sarakshi , Yuen 5  Yew Sen , Agarwal 6  Aniruddha , Rousselot 7  Andres , Raje 8  Dhananjay , Gunasekeran 1 2  Dinesh Visva , Kon 9  Onn Min , Barisani-Asenbauer 10  Talin , Kempen 11  John H. , Gupta 6  Amod , Jabs 12  Douglas A. , Smith 13  Justine R. , Bodaghi 14  Bahram , Zierhut 15  Manfred , DeSmet 16  Marc , McCluskey 17  Peter , Agarwal 18  Mamta , Agarwal 19  Manisha , Aggarwal 6  Kanika , Agrawal 20  Mukesh , Al-Dhibi 21  Hassan , Androudi 22  Sofia , Asyari 23  Fatma , Balasundaram 24  Manohar Babu , Murthy 25  Kalpana Babu , Baglivo 26  Edoardo , Banker 27  Alay , Bansal 6  Reema , Basu 28  Soumyava , Behera 6  Digamber , Biswas 18  Jyotirmay , Carreño 29  Ester , Caspers 30  Laure , Chee 3 31  Soon-Phaik , Chhabra 32  Romi , Cimino 33  Luca , Concha Del Rio 34  Luz Elena , Cunningham 35  Emmett T , Curi 36  Andrè Luiz Land , Das 37  Dipankar , Denisova 38  Ekaterina , Denniston 2 39  Alastair K , Errera 40  Marie-Hélène , Fonollosa 41  Alejandro , George 18  Amala , Goldstein 42  Debra A , Crosier 43  Yan Guex , Gurbaxani 2  Avinash , Invernizzi 44  Alessandro , Isa 45  Hazlita M , Islam 46  Shah Md , Jones 32  Nicholas , Katoch 6  Deeksha , Khairallah 47  Moncef , Khosla 48  Amit , Kramer 49  Michal , Kumar 50  Amitabh , Kumar 51  Atul , La Distia Nora 23  Rina , Lee 2  Richard , Lowder 52  Careen , Luthra 53  Saurabh , Mahendradas 54  Padmamalini , Makhoul 30  Dorine , Mazumdar 55  Shahana , Mehta 56  Salil , Miserocchi 57  Elisabetta , Mochizuki 58  Manabu , Mohamed 59  Oli S , Muccioli 60  Cristina , Munk 61  Marion R , Murthy 62  Somasheila , Narain 63  Shishir , Nascimento 64  Heloisa , Neri 65  Piergiorgio , Nguyen 66  Myhanh , Okada 67  Annabelle A , Ozdal 68  Pinar , Palestine 69  Alan , Pichi 65  Francesco , Rathinam 70  S R , Schlaen 71  Ariel , Sehgal 6  Shobha , Sen 72  H Nida , Sharma 6  Aman , Sharma 6  Kusum , Shoughy 73  Samir S , Singh 6  Nirbhai , Singh 6  Ramandeep , Soheilian 74  Masoud , Sridharan 18  Sudharshan , Thorne 75  Jennifer E , Tappeiner 61  Christoph , Teoh 76  Stephen , Tognon 77  Maria Sofia , Tugal-Tutkun 78  Ilknur , Tyagi 79  Mudit , Uy 80  Harvey , Vasconcelos-Santos 81  Daniel Vitor , Vidovic Valentincic 82  Natasa , Westcott 2  Mark , Yanai 83  Ryoji , Yanez Alvarez 84  Bety , Zahedur 85  Rahman , Nguyen 4  Quan Dong , Pavesio 2  Carlos , Gupta 6  Vishali 
Published: October 2024

ImportanceThis was the first study, to the authors’ knowledge, to statistically evaluate the predictive accuracy of Collaborative Ocular Tuberculosis Study (COTS) calculator in guiding initiation of antitubercular therapy (ATT) in patients with clinically suspicious tubercular uveitis (TBU) in an international cohort.ObjectiveTo evaluate the accuracy of a score of 4 or greater on the online COTS calculator in recommending ATT initiation.Design, Setting, and ParticipantsThis study was an evaluation of a diagnostic test or technology. Data input required for the COTS calculator were extracted from the COTS-1 study dataset, which comprised retrospective, observational records of patients with TBU who were monitored for 12 months after treatment. Patients were recruited from international ophthalmic centers. In the absence of a traditional criterion standard, the 12-month treatment response to ATT was used to classify patients as disease positive or negative. The accuracy of clinicians at the ATT decision-making stage in the COTS-1 study was set against COTS calculator scores of 4 or greater. Diagnostic accuracy metrics, including sensitivity, specificity, positive predictive value (PPV), precision, recall, and F1 score, were computed. Data collected from January 2004 to December 2014 were analyzed.ExposuresCOTS calculator to guide initiation of ATT in patients with TBU.Main Outcomes and MeasuresComparison of accuracy between clinician judgment and the COTS calculator, analyzed at varying scores and further stratified by tuberculosis endemicity.ResultsOf the 492 participants (mean [SD] age, 42.3 [19.0] years; 233 male [47.3%]), application of the COTS calculator identified 225 (45.7%) with high or very high probability to start ATT (score = 4 or 5) and 111 (22.5%) with very high probability alone (score = 5). COTS-5 exhibited the highest specificity (88.7%; 95% CI, 81.4%-93.8%) compared with clinician judgment (29.6%; 95% CI, 21.4%-38.8%), and clinician judgment led in sensitivity (95.5%; 95% CI, 92.9%-97.4%) compared with COTS-5 (26%; 95% CI, 21.6%-30.7%). COTS-4 and COTS-5 balanced specificity (64.3%; 95% CI, 54.9%-73.1%) and sensitivity (48.8%; 95% CI, 43.7%-54%). PPV and sensitivity were consistently higher in the endemic group for all 3 tests.Conclusions and RelevanceResults of this diagnostic study suggest that the COTS calculator (score ≥4) was more specific than clinician judgment for ATT initiation. Although clinician judgment is a good first step to identify all potential true positives (with high sensitivity), a second consultation with COTS-5 (with high PPV) may lead to less false positives. This tool, apt for high-prevalence, low-resource settings, recommends ATT more selectively for genuine TBU cases. Large prospective studies are essential to explore potential improvements in the calculator’s sensitivity.