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Clinical science
Semi-automated screening reveals patients with glaucoma-induced blindness missing out on social support: a cross-sectional study of certificate of visual impairment allocation
  1. Arun James Thirunavukarasu1,2,3,
  2. Nikhil Jain2,4,
  3. Helmut C Y Yu2,5,
  4. George Nishimura2,3,
  5. Ansh Tandon2,3,
  6. Hamid Butt2,3,
  7. Rohan Sanghera1,3,
  8. Rupert R A Bourne2,6
  9. Cambridge Students Glaucoma Initiative
    1. 1 Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK
    2. 2 Cambridge Eye Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
    3. 3 School of Clinical Medicine, University of Cambridge, Cambridge, UK
    4. 4 Bedfordshire Hospitals NHS Foundation Trust, Luton, UK
    5. 5 University of New South Wales, Sydney, New South Wales, Australia
    6. 6 Vision and Eye Research Unit (VERI), Anglia Ruskin University, Cambridge, UK
    1. Correspondence to Dr Arun James Thirunavukarasu; arun.thirunavukarasu{at}medsci.ox.ac.uk

    Abstract

    Background/aims Many countries provide social support to patients with severe sight impairment (blindness). In the UK, these benefits require a certificate of visual impairment (CVI) which requires referral by a consultant ophthalmologist. Many eligible patients do not receive a CVI due to personal choice or lack of consideration or communication by their doctor. This study investigated the frequency and reasons for missed certification in glaucoma.

    Methods A retrospective cross-sectional survey was undertaken of all patients with glaucoma attending a tertiary referral clinic over a 12-month period. Semi-automated screening using objective visual acuity and perimetry parameters was facilitated by a web application, GFDC (Glaucoma Field Defect Classifier). CVI-eligible patients’ records were analysed to determine the reasons for missed registration, including incorrect screening classification.

    Results Of 5620 individual patients consulted, 919 were classified as sight impaired, and 64 were classified as severely sight impaired (blind). Of the patients classified as blind, 7 (11%) were misclassified, and 36 (56%) were on the CVI register already. 21 of 57 eligible patients with glaucoma-induced blindness (37%) were unregistered. Reasons for missed registration included administrative failure (23.8%), lack of consent (9.5%), reversible visual impairment (19.0%), frailty and comorbidity (71.4%), and mental health diagnoses (38.1%).

    Conclusion A semi-automated algorithm can be used to screen large numbers of patients for CVI eligibility due to blindness. Many eligible patients are not registered, with risk factors including frailty, comorbidity and reversible causes of visual impairment. This algorithm could be used to prompt ophthalmologists to consider registration or used as an alternative referral mechanism. Screening for CVI-eligible patients with an objective algorithm may ameliorate the inequity associated with subjective and variable decision-making.

    • Glaucoma
    • Low vision aid
    • Public health
    • Vision
    • Epidemiology

    Data availability statement

    Data are available upon reasonable request. Available upon request to the corresponding author.

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    Data availability statement

    Data are available upon reasonable request. Available upon request to the corresponding author.

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    Footnotes

    • X @thirumasu

    • AJT and NJ contributed equally.

    • Collaborators Cambridge Students Glaucoma Initiative: Florence Bradshaw, Aina Chang, Clara Yuqing Chen, Luke Coakham, Georgina Dunkinson, Anna Economou, Sarah Farrell, Tomasz Galus, Santosh Guru, Ian Holdroyd, Ishan Jain, Haowen Kwan, Federico Lattuada, Aaron Limonard, Shathar Mahmood, Firnaaz Mohideen, Daniel Myers, Jamie Parker, Andrew Quarrell, Tanzil Mohammad Rujeedawa, Chandan Sekhon, Amy Stuart, Juliet Thornton, Edward Wakefield, Haifeng Xu, Woojin Yang, Melissa Yuan.

    • Contributors AJT, NJ and RRAB conceptualised and designed the study. AJT and RRAB obtained ethical approval. AJT coordinated the multidisciplinary team. AJT and RS were responsible for technical development. AJT, NJ, HCYY, GN, AT, HB and CSGI contributed to data collection. AJT completed statistical analysis and data visualisation. AJT and NJ wrote the manuscript, and this was appraised by HCYY, GN, AT, HB, CSGI and RRAB. RRAB provided material support and supervised the project. AJT is the guarantor for the paper.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests RRAB has received consultancy fees/honoraria from Thea, Santen and AbbVie; and is eligible for payment in receipt for provision of CVIs as a consultant ophthalmologist. AJT and RS have received funding from HealthSense for machine learning research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.