Assessment of the Impact of an Endpoint Committee in the Ocular Hypertension Treatment Study

Gordon, Mae O.; Higginbotham, Eve J.; Heuer, Dale K.; Parrish, Richard K.; Robin, Alan L.; Morris, Patricia A.; Dunn, Deborah A.; Wilson, Brad; Kass, Michael A.

doi:10.1016/j.ajo.2018.11.006

Cited by 21 publications

(29 citation statements)

References 21 publications

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“…It should be noted that the OHTS was designed to maximize specificity in its end point committee determinations of POAG. 33 For this reason, it is not surprising that there were more false-positive than false-negative errors in the external datasets that had ground-truth determinations that were more likely to classify an eye as glaucoma (more sensitive) than the OHTS ( Fig S2 ). We evaluated the alternative approaches on the ability to identify the most important regions of an image.…”

Section: Discussionmentioning

confidence: 99%

Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions

Fan

Alipour²,

Bowd³

et al. 2023

Ophthalmology Science

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Section: Discussionmentioning

confidence: 99%

Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions

Fan

Alipour²,

Bowd³

et al. 2023

Ophthalmology Science

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“…A benefit of using strict study protocols and employing strategies such as reading centers or endpoint committees in large-scale clinical trials 32 , 33 is enhancing the consistency of change detection, 34 , 35 but this may not necessarily reflect real-world clinical practices titrating risk at an individual level. A major contributing factor that has been recognized by clinical trial protocols is the potential variability in clinical test results or judgments.…”

Section: Discussionmentioning

confidence: 99%

Glaucoma Suspects: The Impact of Risk Factor-Driven Review Periods on Clinical Load, Diagnoses, and Healthcare Costs

Phu

Masselos

Sullivan-Mee³

et al. 2022

Trans. Vis. Sci. Tech.

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Purpose To model the healthcare impact (clinical attendance time and financial cost) and clinical outcomes (glaucoma diagnoses) of different risk factor–driven review frequencies for glaucoma suspect patients up until the point of discharge or diagnosis. Methods Medical records of 494 glaucoma suspects were examined to extract the clinical diagnosis. Two criteria for review periods were defined, based on contrasting stringency from established clinical guidelines: American Academy of Ophthalmology (AAO), more stringent/less frequent; and the Australian National Health and Medical Research Council (NHMRC), less stringent/more frequent. We used these data to model patient outcomes and healthcare costs using a Markov model. Results The less stringent/more frequent criterion resulted in more high-risk glaucoma suspects requiring more frequent review compared with the more stringent/less frequent criterion. Across the 15 Markov cycles (7.5 years), the less stringent/more frequent review criterion resulted in 6.6% more diagnoses and fewer overall clinical visits (14.7%) and reduced cost per diagnosis by 12% to 32% ( P < 0.0001). The number of glaucoma diagnoses made using each criterion converged at 2.5 to 3 years. Conclusions The stringency of risk assessments for glaucoma suspects impacts review periods and therefore clinical load, healthcare costs, and diagnosis rates. Using current testing methods, more frequent review periods appear advantageous for diagnostic efficiency, with both lower clinic load and lower cost up until the point of discharge or glaucoma diagnosis. Translational Relevance A less stringent criterion for assessing the risk of developing glaucoma potentially offers a more cost-effective method for reviewing glaucoma suspects, especially within the first 2.5 years.

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“…These false positives likely were true positives detecting disease related change earlier in OHT eyes; this was in part a result of the OHTS study design that emphasized high specificity for glaucomatous determination. 22 The CNNs used herein provide a probability of glaucoma as output allowing changes in sensitivity and specificity to desired levels by adjusting the cut-offs used to define POAG. This makes deep-learning models adaptable to different study goals.…”

Section: Discussionmentioning

confidence: 99%

“…The advantages of utilizing an Endpoint Committee in the OHTS have recently been reported. 4 In brief, and most importantly, using an Endpoint Committee had a significant effect on the accuracy of POAG incidence rate with 16.3% of study participants reaching an unadjudicated allcause endpoint but only 9.5% of participants developing a POAG Endpoint Committee adjudicated endpoint. As treatment is unlikely to affect a non-POAG study participant, removal of these unadjudicated, all-cause endpoints led to a more accurate estimate of the efficacy of treatment; treatment reduced the estimates of a POAG adjudicated endpoint by 56% (relative risk of 0.44), while it reduced all-cause endpoints by 33% (relative risk of 0.67).…”

Section: Data Collectionmentioning

confidence: 99%

“…The three committee members reached unanimity in 61% of the endpoints in the first round of masked independent reviews, 32.2% in the second round (reviews were completed independently to resolve disagreement) and 6.8% in a final round (a consensus conference telephone call). 4 The use of independent reading centers and a three-person, three-round consensus process endpoint determination of POAG was employed to ensure high specificity; appropriate for the OHTS, but not necessarily needed for all clinical trials.…”

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confidence: 99%

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Detecting Glaucoma in the Ocular Hypertension Study Using Deep Learning

Fan¹,

Bowd²,

Mark³

et al. 2022

Preprint

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<p></p><p>To investigate the diagnostic accuracy of deep learning (DL) algorithms to detect primary open-angle glaucoma (POAG) trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS). 66,715 photographs from 3,272 eyes were used to train and test a ResNet-50 model to detect the OHTS Endpoint Committee POAG determination based on optic disc (n=287 eyes, 3,502 photographs) and/or visual field (n=198 eyes, 2,300 visual fields) changes. OHTS training, validation and testing sets were randomly determined using an 85-5-10 percentage split by subject. Three independent test sets were used to estimate the generalizability of the model: UCSD Diagnostic Innovations in Glaucoma Study (DIGS, USA), ACRIMA (Spain) and Large-scale Attention-based Glaucoma (LAG, China). The DL model achieved an AUROC (95% CI) of 0.88 (0.82, 0.92) for the overall OHTS POAG endpoint. For the OHTS endpoints based on optic disc changes or visual field changes, AUROCs were 0.91 (0.88, 0.94) and 0.86 (0.76, 0.93), respectively. False-positive rates (at 90% specificity) were higher in photographs of eyes that later developed POAG by disc or visual field (19.1%) compared to eyes that did not develop POAG (7.3%) during their OHTS follow-up. The diagnostic accuracy of the DL model developed on the OHTS optic disc endpoint applied to 3 independent datasets was lower with AUROC ranging from 0.74 to 0.79. High diagnostic accuracy of the current model suggests that DL can be used to automate the determination of POAG for clinical trials and management. In addition, the higher false-positive rate in early photographs of eyes that later developed POAG suggests that DL models detected POAG in some eyes earlier than the OHTS Endpoint Committee.</p>

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Assessment of the Impact of an Endpoint Committee in the Ocular Hypertension Treatment Study

Cited by 21 publications

References 21 publications

Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions

Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions

Glaucoma Suspects: The Impact of Risk Factor-Driven Review Periods on Clinical Load, Diagnoses, and Healthcare Costs

Detecting Glaucoma in the Ocular Hypertension Study Using Deep Learning

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