With chronic diseases, patient adherence plays a crucial role in delaying disease progression and in determining the success of therapy. Problems arise not only from low medication adherence, but also non-adherence to recommended follow-up examinations. Obtaining an accurate estimate of adherence is difficult, especially in glaucoma patients, due to the fact that most antihypertensive drugs are administered in the form of eye drops. There is great variability in the published adherence values for glaucoma patients. Most studies report an average medication adherence of approximately 70%, with around 50% of patients having good adherence (at least 80% of medication administered as planned). Furthermore, 6.8 - 31.4% of the eye drops do not end up in the patient's eye, which means there is even less active ingredient to achieve a therapeutic effect. Glaucoma patients also show low persistence and adherence to follow-up appointments. Since diabetes increases the risk for POAG and secondary glaucoma and given that diabetics have particularly low adherence, the question arose whether a diabetes diagnosis is associated with reduced adherence in glaucoma patients. Previous studies found no significant association between diabetes and reduced adherence in glaucoma patients, although a significant impact of elevated HbA on adherence in glaucoma patients was found in one study. However, this connection still needs to be examined more closely in studies with larger samples.
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http://dx.doi.org/10.1055/a-1975-2396 | DOI Listing |
Transl Vis Sci Technol
January 2025
Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA.
Purpose: The integration of artificial intelligence (AI), particularly deep learning (DL), with optical coherence tomography (OCT) offers significant opportunities in the diagnosis and management of glaucoma. This article explores the application of various DL models in enhancing OCT capabilities and addresses the challenges associated with their clinical implementation.
Methods: A review of articles utilizing DL models was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, and large language models (LLMs).
Acta Ophthalmol
January 2025
Department of Ophthalmology, Aarhus University Hospital, Aarhus, Denmark.
Purpose: To evaluate the intraocular pressure (IOP) lowering effect and success rate of Paul glaucoma implant (PGI) in refractory glaucoma after changing practice pattern from Ahmed and Baerveldt tubes to PGI.
Methods: A prospective observational study of the first 50 consecutive PGI surgeries at a single Danish tertiary centre from January 2022 to October 2023. Primary endpoints were IOP and success rates after 12 months.
Jpn J Ophthalmol
January 2025
Department of Ophthalmology and Visual Science, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Purpose: To investigate outcomes after trabeculotomy in Japanese patients with primary congenital glaucoma (PCG), and to identify risk factors for multiple glaucoma surgery procedures.
Study Design: Retrospective observational study.
Methods: Surgical outcomes were investigated in Japanese patients with PCG who underwent their first glaucoma surgery at Hiroshima University Hospital between January, 2006, and December, 2021.
Int Ophthalmol
January 2025
The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.
Purpose: To characterize the anterior segment (AS) morphology of patients with long-term silicone oil (SiO) in situ (> 12 months) following pars plana vitrectomy (PPV).
Methods: This prospective, comparative characterization study was conducted between January 2022 and July 2023. Patients were included and sorted based on if they had undergone PPV without long-term SiO or had SiO in situ for at least 12 months at the time of review and image collection.
Introduction: Glaucoma is a leading cause of blindness, often progressing asymptomatically until significant vision loss occurs. Early detection is crucial for preventing irreversible damage. The pupillary light reflex (PLR) has proven useful in glaucoma diagnosis, and mobile technologies like the AI-based smartphone pupillometer (AI Pupillometer) offer a promising solution for accessible screening.
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