The Usual Suspects
How can you predict which patients will develop keratectasia, and if they do, how are you going to define it?
Alain Saad |
At a Glance
- Predicting which patients are at a high risk of developing post-LASIK keratectasia can be a challenge, particularly in cases of suspect and forme fruste keratoconus
- A range of parameters can be used to try to distinguish between normal and at-risk patients, but none are perfect; the challenge is finding the right combination
- Clear terminology, effective statistical analysis and a useful clinical model all help when developing an artificial intelligence tool to assess a patient’s risk
- A comprehensive statistical analysis combining a number of parameters can differentiate normal and abnormal corneas with a high rate of success
Corneal ectasia can be a disaster for patients. Blurred vision, light sensitivity, frequent changes in spectacle prescription, halo, ghosting, headaches, eye irritation… and later corneal scarring and hydrops – are all potential consequences of the progressive steepening and thinning of the cornea, and all can conspire to dramatically reduce the quality of life of these patients.
Keratectasia can come in a primitive form, as we see in patients with keratoconus or pellucid marginal degeneration, or an iatrogenic form, like we see after refractive surgery on patients with undiagnosed, subclinical keratoconus. Clearly, anything that’s iatrogenic is something we need to minimize the occurrence of. So how can we detect the earliest manifestations of keratectasia so that we can avoid performing laser refractive surgery on these patients? And what terminology do we use to label these corneas? Understanding exactly how to assess and diagnose these cases is key to making the right treatment decisions. Often, this isn’t a simple process.
To give us the best chance of detecting corneas that are susceptible to keratectasia, we need to do three things: clarify our terminology, develop appropriate statistical methods, and use an applicable clinical model.
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