Dr Angelica Ly
PhD GradCertOcTher BOptom (Hons) FAAO
Integrated Care Co-ordinator and Lead Clinician (macula)
Centre for Eye Health
Age-related macular degeneration (AMD) is the leading cause of vision loss and blindness in Australia.
One in four cases of AMD are classified as normal by eye care professionals.1 Poor visual acuity at presentation translates to poor outcomes and up to 87 per cent of patients with neovascular AMD have a visual acuity worse than 6/12 at the time of diagnosis.2,3 Additionally, one in five patients that need treatment may be lost to follow-up.4
These statistics paint a sobering picture on the state of AMD care in Australia and worldwide. In this article, I describe a series of clinical strategies to supercharge the way you diagnose, understand and manage AMD.
CONFESSION 1. THE RULES KEEP CHANGING
Solution: Clinical practice guidelines
There is a plethora of resources now available to practising clinicians aimed at improving clinical practice patterns and ultimately, patient outcomes. Broadly, these resources include convenient and easily-accessible forms of information, such as case studies, peer-reviewed publications, chair-side references and clinical guidelines (Table 1).5
Optometry Australia – 2018 (New)
National Institute for Health and Care Excellence – 2018
British Medical Journal – 2018
The Royal Australian and New Zealand College of Ophthalmologists – 2018
New Zealand Association of Optometrists – 2018
Review of Optometry – 2017
American Academy of Ophthalmology – 2015
Canadian Journal of Optometry – 2015
Royal College of Ophthalmologists – 2013
Australian Prescriber – 2012
Optical Confederation – 2010
International Council of Ophthalmology – 2007
American Optometric Association – 2004
Table 1. Clinical practice guidelines on AMD, last updated January 2019
Optometry Australia has recently developed a clinical practice guide, providing evidence-based information about current best practice in the diagnosis, treatment and management of age-related macular degeneration. This is an open-access resource available on the Optometry Australia website.
Because the collective wisdom is constantly evolving, these tools can be helpful for distinguishing fact from fiction and often provide all of the relevant information succinctly, filtered through the lens of an expert committee. Although the evidence for efficacy of these materials is limited and the best approach for optimising their efficacy still requires clarification, they are indeed one of the few methods we have of translating research findings into clinical practice. They help to define and promote the use of evidence-based procedures of proven benefit and discourage ineffective alternatives.
In these materials, you can find ready support on a myriad of topics ranging from general management advice, diagnosis, procedures, referrals, test ordering, patient education, clinical prevention and professional-patient communication. They may be accessible through one or multiple means (either personally, online, through mass mailing and most commonly, via publication in a peer-reviewed journal) and result in a statistically significant improvement in professional practice.5
CONFESSION 2. I AM NOT CONFIDENT ABOUT WHAT I’M SEEING
Solution: Clinical decision support platforms
Today’s ‘routine’ eye examination is incredibly complex. In AMD alone, we may be accustomed to performing a targeted history, a routine battery of entrance tests, followed by fundoscopy and retinal photography. It helps to know which instrument to use and when. Optical coherence tomography (OCT) is quickly becoming the norm6 and OCT angiography, fundus autofluorescence and other imaging techniques, including near infrared imaging or ultra-widefield imaging, are also effective.7,8 The combination of multiple modalities improves the diagnosis of ocular disease but may not always be accessible and is often time consuming and subject to interpretation.
Take for instance, a routine 512 x 128 macular OCT volume scan acquired using the Cirrus HD-OCT (Carl Zeiss Meditec). This means that in addition to the rest of the examination, the optometrist has an added duty of care to review each of the 128 serial line scans taken per eye, meaning a total of 256 B-scans per patient. Add to this the myriad of prognostic biomarkers, which are relevant to stratifying risk of AMD progression9 and the complexity is mind-boggling.
Support for accurately interpreting imaging results is on its way. With the aid of computational approaches and machine learning, we can expect to see a growing suite of clinical decision-making support tools. Risk calculators represent an example many will be more familiar with, which is commonly applied to case history data.
Figures 1 and 2 showcase two computational methods of analysing AMD-related ocular imaging data in development at the Centre for Eye Health.10,11 Current commercially-available software on the Cirrus HD-OCT, described as ‘advanced retinal pigment epithelium (RPE) analysis,’ presents a similar tool with the capacity to automatically quantify drusen load.
Figure 1. A clinical decision support tool currently in development at Centre for Eye Health. This method uses unsupervised cluster analysis to semi-automatically classify drusen (red) and pigmentary abnormalities (blue). Each distinct colour in the profile map corresponds to a statistically separable, specific anatomic structure.
Figure 2. Case images taken 16 months apart from an eye with intermediate AMD. The change or difference map pictured on the right alerts the clinician to areas of drusen regression (red).
CONFESSION 3. MY PATIENTS REFUSE TO QUIT SMOKING
Solution: Motivational interviewing and printed patient educational materials
Having fulfilled the onerous task of keeping up-to-date with the latest evidence, acquiring and correctly interpreting the sum of results from the eye examination, it can be tempting to presume that our job is done; however, all that work may be in vain if not disseminated to the patient. Several risk factors carry a well-described association with the onset and progression of AMD, such as age, family history and smoking. Hypertension, cardiovascular disease, raised BMI, poor diet and lack of exercise are less often considered but represent additional and more importantly, modifiable risk factors for disease. Therefore, your AMD management strategy should regularly include advice on improving dietary habits as well as the benefits of nutritional supplements and quitting or reducing smoking.
As optometrists, our unique position in the health care system empowers us to educate and reinforce key management strategies that can make a difference and ultimately, save sight. Behaviour change in chronic disease can be difficult, particularly in asymptomatic cases where the fear of change, ambivalence, lack of skills or a history of prior failures abound; however, it can also be one of the most rewarding aspects of routine optometric practice and a real relationship-builder between you and your patients.
I encourage you to have those ‘difficult’ conversations. But be advised that authoritarian, confrontational or guilt-inducing communications are often counter-productive. If you’re finding it hard to know where to start, motivational interviewing describes an evidence-based, directive counselling approach to behaviour change. I urge all practitioners to learn more about its application in chronic diseases.12 Personalising the message to the individual is important and relevant material or contact from patient support groups, such as the Macular Disease Foundation Australia, or low vision services, including Guide Dogs Australia or Vision Australia, can also be invaluable.
Optometry has entered a period with an ever-increasing range of tools and information to supercharge the way we manage AMD and other diseases. With this comes both challenges and opportunities to apply strategies for the benefit of our patients. How will you improve the way you manage AMD tomorrow?
Conflicts of interest: The author is a named inventor on a provisional patent relating to the use of pattern recognition on ocular imaging data. Centre for Eye Health is an initiative of Guide Dogs NSW/ACT and UNSW Sydney and has an affiliation with the Macular Disease Foundation Australia.
Acknowledgements: The author thanks Michael Yapp and Professor Michael Kalloniatis for reviewing the manuscript.
1 Neely DC, Bray KJ, Huisingh CE et al. Prevalence of Undiagnosed Age-Related Macular Degeneration in Primary Eye Care. JAMA Ophthalmol 2017; 135: 570-575.
2 Fong DS, Custis P, Howes J et al. Intravitreal bevacizumab and ranibizumab for age-related macular degeneration a multicenter, retrospective study. Ophthalmology 2010; 117: 298-302.
3 Ho AC, Albini TA, Brown DM et al. The Potential Importance of Detection of Neovascular Age-Related Macular Degeneration When Visual Acuity Is Relatively Good. JAMA Ophthalmol 2017; 135: 268-273.
4 Obeid A, Gao X, Ali FS et al. Loss to Follow-up Among Patients With Neovascular Age-Related Macular Degeneration Who Received Intravitreal Anti-Vascular Endothelial Growth Factor Injections. JAMA Ophthalmol 2018; 136: 1251-1259.
5 Giguere A, Legare F, Grimshaw J et al. Printed educational materials: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev 2012; 10: CD004398.
6 Ly A, Nivison-Smith L, Zangerl B et al. Self-reported optometric practise patterns in age-related macular degeneration. Clin Exp Optom 2017; 100: 718-728.
7 Ly A, Nivison-Smith L, Assaad N et al. Infrared reflectance imaging in age-related macular degeneration. Ophthalmic Physiol Opt 2016; 36: 303-316.
8 Ly A, Nivison-Smith L, Assaad N et al. Fundus Autofluorescence in Age-related Macular Degeneration. Optom Vis Sci 2017; 94: 246-259.
9 Ly A, Yapp M, Nivison-Smith L et al. Developing prognostic biomarkers in intermediate age-related macular degeneration: their clinical use in predicting progression. Clin Exp Optom 2018; 101: 172-181.
10 Ly A, Nivison-Smith L, Assaad N et al. Multispectral Pattern Recognition Reveals a Diversity of Clinical Signs in Intermediate Age-Related Macular Degeneration. Invest Ophthalmol Vis Sci 2018; 59: 1790-1799.
11 Ly A, Yapp M, Kalloniatis M, Zangerl B. Automated identification of drusen regression. American Academy of Optometry Annual meeting, San Antonio, Texas, 2018.
12 Linden A, Butterworth SW, Prochaska JO. Motivational interviewing-based health coaching as a chronic care intervention. J Eval Clin Pract 2010; 16: 166-174.
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