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. 2026 Mar;30(25):1–35. doi: 10.3310/GJAA0514

Validating and updating the OHTS-EGPS model predicting 5-year glaucoma risk among patients with ocular hypertension using electronic medical records: a cohort study.

David Wright, Hangjian Wu, Anthony King, Giovanni Montesano, Bethany Higgins, Gus Gazzard, James Morgan, Andy McNaught, Rani Sebastian, Faisal Ahmed, Chrysostomos Dimitriou, Madhu Nagar, Andrew Scott, Omar Rafiq, Robert Harper, David Crabb, Verity Watson, Rodolfo Hernández, Chris Cardwell, Yemisi Takwoingi, Augusto Azuara-Blanco
PMCID: PMC13034089  PMID: 41873908

Abstract

BACKGROUND

Ocular hypertension, that is intraocular pressure > 21 mmHg, is a risk factor for glaucoma. A glaucoma risk predictor, the Ocular Hypertension Study-European Glaucoma Prevention Study model, is available.

OBJECTIVES

(1) To validate and update the Ocular Hypertension Study-European Glaucoma Prevention Study risk prediction model in a United Kingdom population; (2) to assess the relative efficiency of alternative monitoring pathways according to glaucoma risk; (3) to determine the clinical and cost-effectiveness of treating people with ocular hypertension with intraocular pressure of 22 or 23 mmHg and (4) to elicit patient preferences for monitoring.

DESIGN

(1) Retrospective data analysis of electronic medical records of ocular hypertension patients attending hospital eye services. The influence of the Ocular Hypertension Study-European Glaucoma Prevention Study predictors and additional ocular and systematic factors was explored. Validation: the Ocular Hypertension Study-European Glaucoma Prevention Study prediction model was applied. Update: the model was refitted by re-estimating baseline hazard and regression coefficients. (2, 3) Predictor versus standard care, with deterministic and probabilistic sensitivity analyses. Subgroup analysis for people with 22-23 mmHg intraocular pressure. (4) Discrete choice experiment.

SETTING AND PARTICIPANTS

People with intraocular pressure 22-32 mmHg in either eye, at least four visual field tests, 5 years of follow-up, no significant ocular comorbidities. Data sourced from secondary clinical settings.

MAIN OUTCOME MEASURES

Discriminative ability (c-index) and calibration (calibration slope) to predict conversion to glaucoma in 5 years. Quality-adjusted life-years, incremental cost-effectiveness ratio, preferences.

DATA SOURCES

Electronic medical records of 10 hospitals in England.

RESULTS

(1) Of 9030 patients with ocular hypertension who fitted the inclusion criteria 1530 (16.9%) converted to glaucoma. The Ocular Hypertension Study-European Glaucoma Prevention Study model provided a pooled c-index of 0.61 (95% confidence interval: 0.60 to 0.63). The updated model had a pooled c-index of 0.67 (0.51 to 0.84). (2) In the economic model almost all (99%) patients were treated in the risk predictor strategy, and less than half (47%) in the standard care strategy. The risk predictor strategy produced higher costs, but also higher quality-adjusted life-years and is likely to be cost-effective compared with standard care. (3) Patients with ocular hypertension and intraocular pressure 22-23 mmHg had similar risk of conversion to the rest of the cohort. A treat-all strategy may not be cost-effective. (4) Three hundred and sixty patients were recruited from four NHS hospitals. Almost all respondents (92%) had experienced face-to-face monitoring at a hospital; fewer respondents had experienced virtual clinics (47%) or community optometrist monitoring (43%). Most patients preferred hospital-based monitoring services by health professionals rather than community-based by optometrists but patients with prior experience of community optometrist monitoring preferred it. Patients preferred options associated with lower risk of conversion and lower costs.

LIMITATIONS

Insufficient data to evaluate influence of ethnicity or ocular factors such as optic disc and retinal anatomy.

CONCLUSIONS

We validated the Ocular Hypertension Study-European Glaucoma Prevention Study predictor model in a large population with ocular hypertension achieving modest improvements. The use of a risk prediction tool is likely to be cost-effective. Reducing the risk of conversion was the most important preference for patients with ocular hypertension.

FUTURE WORK

Future work should address the influence of genetic or other ocular factors in disease progression, evaluation of effectiveness and cost-effectiveness of different models of eye care, and on how to avoid late glaucoma presentation.

FUNDING

This synopsis presents independent research funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme as award number NIHR131808.

Plain language summary

Glaucoma is a common eye condition that can lead to loss of vision if not identified and treated early. Ocular hypertension (high eye pressure) increases the chance of developing glaucoma and is usually detected during an eye health test at the optometrist. Eye pressure is considered to be high if it is above 21 mmHg. In the United Kingdom, more than 1.5 million adults have ocular hypertension. Although most will not develop glaucoma, treatment may be required to stop this happening. People with ocular hypertension are typically monitored in hospital eye service. Having a tool that can help identify those at the greatest risk of converting from ocular hypertension to glaucoma will be useful to clinicians and patients: patients with high risk will need frequent checks, while those at low risk will need less frequent visits to the clinic. A ‘glaucoma risk calculator’ has previously been developed from data from two international trials, but we do not know how well it works in United Kingdom populations. Our research was designed to confirm that the existing risk calculator works well in people from the United Kingdom and, if possible, to improve how well we can foresee people who will develop glaucoma. We also wanted to know if using the risk calculator to plan for example how frequent patients need to be checked and where, is good value for money. We also asked patients for their preferences. To answer our queries we reviewed the electronic medical records of over 130,000 people who attended 10 NHS hospitals. We identified over 9000 patients with ocular hypertension meeting our inclusion criteria. By reviewing peripheral vision tests (called visual field tests) we were able to determine whether glaucoma developed or not. Patients’ preferences were formally assessed using a survey. A total of 1530 (16.9%) of patients converted to glaucoma during this follow-up period. Our risk prediction model performed better than the previous risk predictor but the improvement was modest and we cannot make a strong recommendation for its use. The use of the risk predictor can be an efficient way of organising the monitoring of people with high risk of converting to glaucoma. Further research will be needed to find a better risk calculator. Among respondents to the survey (n = 360) reducing the risk of glaucoma was important; most patients preferred hospital-based monitoring services but those who had experience with community optometrist monitoring preferred it.

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References

  1. Wright DM, Azuara-Blanco A, Cardwell C, Montesano G, Crabb DP, Gazzard G, et al.; GRIP Study Group. Validating and updating the OHTS-EGPS model predicting 5-year glaucoma risk among ocular hypertension patients using electronic records. Ophthalmol Glaucoma 2024;8:143–51. doi: 10.1016/j.ogla.2024.10.009. [DOI] [PubMed]
  2. Wu H, Hernández R, Crabb DP, Gazzard G, Harper RA, King A, et al. Patient preferences for ocular hypertension monitoring: a discrete choice experiment. BMJ Open Ophthalmol 2024;9:e001639. doi: 10.1136/bmjophth-2024-001639. [DOI] [PMC free article] [PubMed]
  3. Wu H, Gazzard G, King A, Morgan J, Wright D, Crabb DP, et al. Cost-effectiveness of monitoring ocular hypertension based on a risk prediction tool. BMJ Open Ophthalmol 2024;9:e001741. doi: 10.1136/bmjophth-2024-001741. [DOI] [PMC free article] [PubMed]
  4. Bourne RRA, Jonas JB, Bron AM, Cicinelli MV, Das A, Flaxman SR, et al.; Vision Loss Expert Group of the Global Burden of Disease Study. Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe in 2015: magnitude, temporal trends and projections. Br J Ophthalmol 2018;102:575–85.
  5. The Royal College of Ophthalmologists. The Way Forward. 2017. URL: www.rcophth.ac.uk/wp-content/uploads/2021/12/RCOphth-The-Way-Forward-Glaucoma-300117.pdf (accessed 19 November 2023).
  6. Kass MA, Heuer DK, Higginbotham EJ, Johnson CA, Keltner JL, Miller JP, et al. The ocular hypertension treatment study: a randomized trial determines that topical ocular hypotensive medication delays or prevents the onset of primary open-angle glaucoma. Arch Ophthalmol 2002;120:701–13; discussion 829. doi: 10.1001/archopht.120.6.701. [DOI] [PubMed]
  7. Miglior S, Zeyen T, Pfeiffer N, Cunha-Vaz J, Torri V, Adamsons I; European Glaucoma Prevention Study (EGPS) Group. Results of the European Glaucoma Prevention Study. Ophthalmology 2005;112:366–75. doi: 10.1016/j.ophtha.2004.11.030. [DOI] [PubMed]
  8. Collins GS, Altman DG. Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2. BMJ 2021;344:e4181. https://doi.org/10.1136/BMJ.E4181 doi: 10.1136/bmj.e4181. [DOI] [PMC free article] [PubMed]
  9. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361–87. doi: 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4. [DOI] [PubMed]
  10. Kelly SR, Khawaja AP, Bryan SR, Azuara-Blanco A, Sparrow JM, Crabb DP. Progression from ocular hypertension to visual field loss in the English hospital eye service. Br J Ophthalmol 2020;104:1406–11. doi: 10.1136/bjophthalmol-2019-315052. [DOI] [PubMed]
  11. Åsman P, Heijl A. Glaucoma Hemifield Test: automated visual field evaluation. Arch Ophthalmol 1992;110:812–9. doi: 10.1001/archopht.1992.01080180084033. [DOI] [PubMed]
  12. Katz J, Harry-Quigley A, Sommer A. Repeatability of the Glaucoma Hemifield Test in automated perimetry. Invest Ophthalmol Vis Sci 1995;36:1658–64. [PubMed]
  13. Brennan A, Chick SE, Davies R. A taxonomy of model structures for economic evaluation of health technologies. Health Econ 2006;15:1295–310. doi: 10.1002/hec.1148. [DOI] [PubMed]
  14. Caro JJ, Möller J, Getsios D. Discrete event simulation: the preferred technique for health economic evaluations? Value Health 2010;13:1056–60. doi: 10.1111/j.1524-4733.2010.00775.x. [DOI] [PubMed]
  15. Karnon J, Stahl J, Brennan A, Caro JJ, Mar J, Möller J; ISPOR-SMDM Modeling Good Research Practices Task Force. Modeling using discrete event simulation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-4. Value Health 2012;15:821–7. doi: 10.1016/j.jval.2012.04.013. [DOI] [PubMed]
  16. National Institute for Health and Care Excellence. NICE: Guidance on Glaucoma: Diagnosis and Management of Chronic Open Angle Glaucoma and Ocular Hypertension. URL: www.nice.org.uk/guidance/NG81 (accessed 20 April 2022). [PubMed]
  17. Burr JM, Botello-Pinzon P, Takwoingi Y, Hernández R, Vazquez-Montes M, Elders A, et al. Surveillance for ocular hypertension: an evidence synthesis and economic evaluation. Health Technol Assess 2012;16:1–266. doi: 10.3310/hta16290. [DOI] [PMC free article] [PubMed]
  18. Gordon MO, Torri V, Miglior S, Beiser JA, Floriani I, Miller JP, et al.; Ocular Hypertension Treatment Study Group. Validated prediction model for the development of primary open-angle glaucoma in individuals with ocular hypertension. Ophthalmology 2007;114:10–9. doi: 10.1016/j.ophtha.2006.08.031. [DOI] [PMC free article] [PubMed]
  19. Office for National Statistics. National Cost Collection for the NHS. URL: www.england.nhs.uk/costing-in-the-nhs/national-cost-collection/ (accessed 16 December 2023).
  20. Van Gestel A, Severens JL, Webers CAB, Beckers HJM, Jansonius NM, Schouten JSAG. Modeling complex treatment strategies: construction and validation of a discrete event simulation model for glaucoma. Value Health 2010;13:358–67. doi: 10.1111/j.1524-4733.2009.00678.x. [DOI] [PubMed]
  21. Mills RP, Budenz DL, Lee PP, Noecker RJ, Walt JG, Siegartel LR, et al. Categorizing the stage of glaucoma from pre-diagnosis to end-stage disease. Am J Ophthalmol 2006;141:24–30. doi: 10.1016/j.ajo.2005.07.044. [DOI] [PubMed]
  22. Azuara-Blanco A, Burr J, Thomas R, Maclennan G, McPherson S. The accuracy of accredited glaucoma optometrists in the diagnosis and treatment recommendation for glaucoma. Br J Ophthalmol 2007;91:1639–43. doi: 10.1136/bjo.2007.119628. [DOI] [PMC free article] [PubMed]
  23. National Institute for Health Research. British National Formulary (BNF). n.d. URL: https://bnf.nice.org.uk/ (accessed 2 April 2025).
  24. Office for National Statistics. National Cost Collection for the NHS. n.d. URL: www.england.nhs.uk/costing-in-the-nhs/national-cost-collection (accessed 23 October 2025).
  25. Gazzard G, Konstantakopoulou E, Garway-Heath D, Garg A, Vickerstaff V, Hunter R, et al. Selective laser trabeculoplasty versus drops for newly diagnosed ocular hypertension and glaucoma: the LiGHT RCT. Health Technol Assess 2019;23:1–101. doi: 10.3310/hta23310. [DOI] [PMC free article] [PubMed]
  26. Burr JM, Kilonzo M, Vale L, Ryan M. Developing a preference-based Glaucoma Utility Index using a discrete choice experiment. Optom Vis Sci 2007;84:797–808. doi: 10.1097/OPX.0b013e3181339f30. [DOI] [PubMed]
  27. Van Der Valk R, Webers CAB, Schouten JSAG, Zeegers MP, Hendrikse F, Prins MH. Intraocular pressure-lowering effects of all commonly used glaucoma drugs: a meta-analysis of randomized clinical trials. Ophthalmology 2005;112:1177–85. doi: 10.1016/j.ophtha.2005.01.042. [DOI] [PubMed]
  28. van Gestel A. Glaucoma Management: Economic Evaluations Based on a Patient Level Simulation Model. Doctoral thesis. Maastricht: Maastricht University; 2012. https://doi.org/10.26481/DIS.20121005AG
  29. Webers CAB, Beckers HJM, Nuijts RMMA, Schouten JSAG. Pharmacological management of primary open-angle glaucoma: second-line options and beyond. Drugs Aging 2008;25:729–59. doi: 10.2165/00002512-200825090-00002. [DOI] [PubMed]
  30. Chi SC, Kang YN, Hwang DK, Liu CJ. Selective laser trabeculoplasty versus medication for open-angle glaucoma: systematic review and meta-analysis of randomised clinical trials. Br J Ophthalmol 2020;104:1500–7. doi: 10.1136/bjophthalmol-2019-315613. [DOI] [PubMed]
  31. Crabb DP, Russell RA, Malik R, Anand N, Baker H, Boodhna T, et al. Frequency of visual field testing when monitoring patients newly diagnosed with glaucoma: mixed methods and modelling. Health Serv Deliv Res 2014;2:1–102. [PubMed]
  32. Kirwan JF, Lockwood AJ, Shah P, Macleod A, Broadway DC, King AJ, et al.; Trabeculectomy Outcomes Group Audit Study Group. Trabeculectomy in the 21st century: a multicenter analysis. Ophthalmology 2013;120:2532–9. doi: 10.1016/j.ophtha.2013.07.049. [DOI] [PubMed]
  33. Department of Health. The NHS Constitution for England. URL: www.gov.uk/government/publications/the-nhs-constitution-for-england (accessed 16 December 2023).
  34. Burr JM, Mowatt G, Hernández R, Siddiqui MAR, Cook J, Lourenco T, et al. The clinical effectiveness and cost-effectiveness of screening for open angle glaucoma: a systematic review and economic evaluation. Health Technol Assess 2007;11:iii–v, ix. doi: 10.3310/hta11410. [DOI] [PubMed]
  35. Shemilt I, Thomas J, Morciano M. A web-based tool for adjusting costs to a specific target currency and price year. Evid Pol 2010;6:51–9.
  36. Bridges JFP, Hauber AB, Marshall D, Lloyd A, Prosser LA, Regier DA, et al. Conjoint analysis applications in health – a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health 2011;14:403–13. doi: 10.1016/j.jval.2010.11.013. [DOI] [PubMed]
  37. Watson V, Becker F, de Bekker-Grob E. Discrete choice experiment response rates: a meta-analysis. Health Econ 2017;26:810–7. doi: 10.1002/hec.3354. [DOI] [PubMed]
  38. Coast J, Al-Janabi H, Sutton EJ, Horrocks SA, Vosper AJ, Swancutt DR, Flynn TN. Using qualitative methods for attribute development for discrete choice experiments: issues and recommendations. Health Econ 2012;21:730–41. doi: 10.1002/hec.1739. [DOI] [PubMed]
  39. ChoiceMetrics. Ngene 1.3.0 User Manual & Reference Guide.
  40. McFadden D. Conditional Logit Analysis of Qualitative Choice Behaviour. In McFadden D, editor. Frontiers in Econometrics. London: Academic Press; 1973. pp. 105–42.
  41. Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user’s guide. PharmacoEconomics 2008;26:661–77. doi: 10.2165/00019053-200826080-00004. [DOI] [PubMed]
  42. Scarpa R, Ferrini S, Willis K. Performance of Error Component Models for Status-Quo Effects in Choice Experiments. In Scarpa R, Alberini A, editors. Applications of Simulation Methods in Environmental and Resource Economics. Cham: Springer; 2005. pp. 247–73.
  43. Hauber AB, González JM, Groothuis-Oudshoorn CGM, Prior T, Marshall DA, Cunningham C, et al. Statistical methods for the analysis of discrete choice experiments: a report of the ISPOR Conjoint Analysis Good Research Practices Task Force. Value Health 2016;19:300–15. doi: 10.1016/j.jval.2016.04.004. [DOI] [PubMed]
  44. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010;21:128–38. doi: 10.1097/EDE.0b013e3181c30fb2. [DOI] [PMC free article] [PubMed]
  45. Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW; Topic Group ‘Evaluating diagnostic tests and prediction models’ of the STRATOS initiative. Calibration: the Achilles heel of predictive analytics. BMC Med 2019;17:230. https://doi.org/10.1186/S12916-019-1466-7 doi: 10.1186/s12916-019-1466-7. [DOI] [PMC free article] [PubMed]
  46. Hippisley-Cox J, Coupland C, Brindle P. Derivation and validation of QStroke score for predicting risk of ischaemic stroke in primary care and comparison with other risk scores: a prospective open cohort study. BMJ 2013;346:f2573. https://doi.org/10.1136/BMJ.F2573 doi: 10.1136/bmj.f2573. [DOI] [PMC free article] [PubMed]
  47. Innes H, Jepsen P, McDonald S, Dillon J, Hamill V, Yeung A, et al. Performance of models to predict hepatocellular carcinoma risk among UK patients with cirrhosis and cured HCV infection. JHEP Reports 2021;3:100384. https://doi.org/10.1016/J.JHEPR.2021.100384 doi: 10.1016/j.jhepr.2021.100384. [DOI] [PMC free article] [PubMed]
  48. Grzybowski A, Och M, Kanclerz P, Leffler C. Primary open angle glaucoma and vascular risk factors: a review of population based studies from 1990 to 2019. J Clin Med 2020;97:761. https://doi.org/10.3390/JCM9030761 doi: 10.3390/jcm9030761. [DOI] [PMC free article] [PubMed]
  49. Langman MJS, Lancashire RJ, Cheng KK, Stewart PM. Systemic hypertension and glaucoma: mechanisms in common and co-occurrence. Br J Ophthalmol 2005;89:960960–3. doi: 10.1136/bjo.2004.053397. [DOI] [PMC free article] [PubMed]
  50. Van Gestel A, Webers CA, Severens JL, Beckers HJ, Jansonius NM, Hendrikse F, Schouten JS. The long-term outcomes of four alternative treatment strategies for primary open-angle glaucoma. Acta Ophthalmol 2012;90:20–31. doi: 10.1111/j.1755-3768.2011.02318.x. [DOI] [PubMed]
  51. Institute of Medicine Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academies Press; 2001. https://doi.org/10.17226/10027
  52. Rosengren K, Brannefors P, Carlstrom E. Adoption of the concept of person-centred care into discourse in Europe: a systematic literature review. J Health Organ Manag 2021;35:265–80. doi: 10.1108/JHOM-01-2021-0008. [DOI] [PMC free article] [PubMed]
  53. Inoue K. Managing adverse effects of glaucoma medications. Clin Ophthalmol 2014;8:903–13. doi: 10.2147/OPTH.S44708. [DOI] [PMC free article] [PubMed]
  54. Denis P. Adverse effects, adherence and cost–benefits in glaucoma treatment. Eur Ophthal Rev 2011;5:116.
  55. Kass MA, Gordon MO, Gao F, Heuer DK, Higginbotham EJ, Johnson CA, et al.; Ocular Hypertension Treatment Study Group. Delaying treatment of ocular hypertension: the ocular hypertension treatment study. Arch Ophthalmol 2010;128:276–87. doi: 10.1001/archophthalmol.2010.20. [DOI] [PMC free article] [PubMed]
  56. Stewart WC, Stewart JA, Nassar QJ, Mychaskiw MA. Cost-effectiveness of treating ocular hypertension. Ophthalmology 2008;115:94–8. doi: 10.1016/j.ophtha.2007.01.040. [DOI] [PubMed]
  57. Gomes M, Murray E, Raftery J. Economic evaluation of digital health interventions: methodological issues and recommendations for practice. PharmacoEconomics 2022;40:367–78. doi: 10.1007/s40273-022-01130-0. [DOI] [PMC free article] [PubMed]
  58. Bhargava JS, Bhan-Bhargava A, Foss AJE, King AJ. Views of glaucoma patients on provision of follow-up care; An assessment of patient preferences by conjoint analysis. Br J Ophthalmol 2008;92:1601–5. doi: 10.1136/bjo.2008.140483. [DOI] [PubMed]
  59. Lu TC, Angell B, Dunn H, Ford B, White A, Keay L. Determining patient preferences in a glaucoma service: a discrete choice experiment. Clin Exp Ophthalmol 2019;47:1146–55. doi: 10.1111/ceo.13606. [DOI] [PubMed]
  60. Muth DR, Neubauer AS, Klingenstein A, Schaller U, Priglinger SG, Hirneiß CW. What would an ‘ideal’ glaucoma examination be like? A conjoint analysis of patients’ and physicians’ preferences. Int Ophthalmol 2021;41:3911–20. doi: 10.1007/s10792-021-01960-5. [DOI] [PMC free article] [PubMed]
  61. Glenk K, Meyerhoff J, Akaichi F, Martin-Ortega J. Revisiting cost vector effects in discrete choice experiments. Resour Energy Econ 2019;57:135–55.

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