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Ophthalmic & Physiological Optics : the... Jan 2024Nonadherence to medication reduces treatment effectiveness, and in chronic conditions it can significantly reduce health outcomes. In glaucoma, suboptimal adherence can... (Review)
Review
PURPOSE
Nonadherence to medication reduces treatment effectiveness, and in chronic conditions it can significantly reduce health outcomes. In glaucoma, suboptimal adherence can lead to sight loss, which places a greater financial burden on society and reduces patients' quality of life. Interventions to improve adherence have so far had limited success and lack robust theoretical underpinnings. A better understanding of the determinants of medication adherence behaviour is needed in order to develop interventions that can target these factors more effectively. This systematic review aims to identify modifiable barriers and enablers to glaucoma medication adherence and identify factors most likely to influence adherence behaviour.
RECENT FINDINGS
We searched CINAHL, MEDLINE, PsycINFO, EMBASE, the Cochrane Library and sources of grey literature up to August 2022 for studies reporting determinants of glaucoma medication adherence. Data describing modifiable barriers/enablers to adherence were extracted and analysed using the Theoretical Domains Framework (TDF), a behavioural framework consisting of 14 domains representing theoretical factors that most likely influence behaviour. Data were deductively coded into one of the TDF domains and inductively analysed to generate themes. Key behavioural domains influencing medication adherence were identified by frequency of study coding, level of elaboration and expressed importance. Eighty-three studies were included in the final synthesis. Four key domains influencing glaucoma medication adherence were identified: 'Environmental Context and Resources', 'Knowledge', 'Skills' and 'Memory, Attention and decision processes'. Frequently reported barriers included complex eyedrop regimens, lack of patient understanding of their condition, forgetfulness and difficulties administering eyedrops. Whereas simplified treatments, knowledgeable educated patients and good patient-practitioner relationships were enablers to adherence.
SUMMARY
We identified multiple barriers and enablers affecting glaucoma medication adherence. Four theoretical domains were found to be key in influencing adherence behaviour. These findings can be used to underpin the development of behaviour change interventions that aim to improve medication adherence.
Topics: Humans; Quality of Life; Glaucoma; Medication Adherence
PubMed: 37985237
DOI: 10.1111/opo.13245 -
The Cochrane Database of Systematic... Nov 2023Keratoconus remains difficult to diagnose, especially in the early stages. It is a progressive disorder of the cornea that starts at a young age. Diagnosis is based on... (Review)
Review
BACKGROUND
Keratoconus remains difficult to diagnose, especially in the early stages. It is a progressive disorder of the cornea that starts at a young age. Diagnosis is based on clinical examination and corneal imaging; though in the early stages, when there are no clinical signs, diagnosis depends on the interpretation of corneal imaging (e.g. topography and tomography) by trained cornea specialists. Using artificial intelligence (AI) to analyse the corneal images and detect cases of keratoconus could help prevent visual acuity loss and even corneal transplantation. However, a missed diagnosis in people seeking refractive surgery could lead to weakening of the cornea and keratoconus-like ectasia. There is a need for a reliable overview of the accuracy of AI for detecting keratoconus and the applicability of this automated method to the clinical setting.
OBJECTIVES
To assess the diagnostic accuracy of artificial intelligence (AI) algorithms for detecting keratoconus in people presenting with refractive errors, especially those whose vision can no longer be fully corrected with glasses, those seeking corneal refractive surgery, and those suspected of having keratoconus. AI could help ophthalmologists, optometrists, and other eye care professionals to make decisions on referral to cornea specialists. Secondary objectives To assess the following potential causes of heterogeneity in diagnostic performance across studies. • Different AI algorithms (e.g. neural networks, decision trees, support vector machines) • Index test methodology (preprocessing techniques, core AI method, and postprocessing techniques) • Sources of input to train algorithms (topography and tomography images from Placido disc system, Scheimpflug system, slit-scanning system, or optical coherence tomography (OCT); number of training and testing cases/images; label/endpoint variable used for training) • Study setting • Study design • Ethnicity, or geographic area as its proxy • Different index test positivity criteria provided by the topography or tomography device • Reference standard, topography or tomography, one or two cornea specialists • Definition of keratoconus • Mean age of participants • Recruitment of participants • Severity of keratoconus (clinically manifest or subclinical) SEARCH METHODS: We searched CENTRAL (which contains the Cochrane Eyes and Vision Trials Register), Ovid MEDLINE, Ovid Embase, OpenGrey, the ISRCTN registry, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP). There were no date or language restrictions in the electronic searches for trials. We last searched the electronic databases on 29 November 2022.
SELECTION CRITERIA
We included cross-sectional and diagnostic case-control studies that investigated AI for the diagnosis of keratoconus using topography, tomography, or both. We included studies that diagnosed manifest keratoconus, subclinical keratoconus, or both. The reference standard was the interpretation of topography or tomography images by at least two cornea specialists.
DATA COLLECTION AND ANALYSIS
Two review authors independently extracted the study data and assessed the quality of studies using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. When an article contained multiple AI algorithms, we selected the algorithm with the highest Youden's index. We assessed the certainty of evidence using the GRADE approach.
MAIN RESULTS
We included 63 studies, published between 1994 and 2022, that developed and investigated the accuracy of AI for the diagnosis of keratoconus. There were three different units of analysis in the studies: eyes, participants, and images. Forty-four studies analysed 23,771 eyes, four studies analysed 3843 participants, and 15 studies analysed 38,832 images. Fifty-four articles evaluated the detection of manifest keratoconus, defined as a cornea that showed any clinical sign of keratoconus. The accuracy of AI seems almost perfect, with a summary sensitivity of 98.6% (95% confidence interval (CI) 97.6% to 99.1%) and a summary specificity of 98.3% (95% CI 97.4% to 98.9%). However, accuracy varied across studies and the certainty of the evidence was low. Twenty-eight articles evaluated the detection of subclinical keratoconus, although the definition of subclinical varied. We grouped subclinical keratoconus, forme fruste, and very asymmetrical eyes together. The tests showed good accuracy, with a summary sensitivity of 90.0% (95% CI 84.5% to 93.8%) and a summary specificity of 95.5% (95% CI 91.9% to 97.5%). However, the certainty of the evidence was very low for sensitivity and low for specificity. In both groups, we graded most studies at high risk of bias, with high applicability concerns, in the domain of patient selection, since most were case-control studies. Moreover, we graded the certainty of evidence as low to very low due to selection bias, inconsistency, and imprecision. We could not explain the heterogeneity between the studies. The sensitivity analyses based on study design, AI algorithm, imaging technique (topography versus tomography), and data source (parameters versus images) showed no differences in the results.
AUTHORS' CONCLUSIONS
AI appears to be a promising triage tool in ophthalmologic practice for diagnosing keratoconus. Test accuracy was very high for manifest keratoconus and slightly lower for subclinical keratoconus, indicating a higher chance of missing a diagnosis in people without clinical signs. This could lead to progression of keratoconus or an erroneous indication for refractive surgery, which would worsen the disease. We are unable to draw clear and reliable conclusions due to the high risk of bias, the unexplained heterogeneity of the results, and high applicability concerns, all of which reduced our confidence in the evidence. Greater standardization in future research would increase the quality of studies and improve comparability between studies.
Topics: Humans; Artificial Intelligence; Keratoconus; Cross-Sectional Studies; Physical Examination; Case-Control Studies
PubMed: 37965960
DOI: 10.1002/14651858.CD014911.pub2 -
The Cochrane Database of Systematic... Aug 2023'Blue-light filtering', or 'blue-light blocking', spectacle lenses filter ultraviolet radiation and varying portions of short-wavelength visible light from reaching the... (Review)
Review
BACKGROUND
'Blue-light filtering', or 'blue-light blocking', spectacle lenses filter ultraviolet radiation and varying portions of short-wavelength visible light from reaching the eye. Various blue-light filtering lenses are commercially available. Some claims exist that they can improve visual performance with digital device use, provide retinal protection, and promote sleep quality. We investigated clinical trial evidence for these suggested effects, and considered any potential adverse effects.
OBJECTIVES
To assess the effects of blue-light filtering lenses compared with non-blue-light filtering lenses, for improving visual performance, providing macular protection, and improving sleep quality in adults.
SEARCH METHODS
We searched the Cochrane Central Register of Controlled Trials (CENTRAL; containing the Cochrane Eyes and Vision Trials Register; 2022, Issue 3); Ovid MEDLINE; Ovid Embase; LILACS; the ISRCTN registry; ClinicalTrials.gov and WHO ICTRP, with no date or language restrictions. We last searched the electronic databases on 22 March 2022.
SELECTION CRITERIA
We included randomised controlled trials (RCTs), involving adult participants, where blue-light filtering spectacle lenses were compared with non-blue-light filtering spectacle lenses.
DATA COLLECTION AND ANALYSIS
Primary outcomes were the change in visual fatigue score and critical flicker-fusion frequency (CFF), as continuous outcomes, between baseline and one month of follow-up. Secondary outcomes included best-corrected visual acuity (BCVA), contrast sensitivity, discomfort glare, proportion of eyes with a pathological macular finding, colour discrimination, proportion of participants with reduced daytime alertness, serum melatonin levels, subjective sleep quality, and patient satisfaction with their visual performance. We evaluated findings related to ocular and systemic adverse effects. We followed standard Cochrane methods for data extraction and assessed risk of bias using the Cochrane Risk of Bias 1 (RoB 1) tool. We used GRADE to assess the certainty of the evidence for each outcome.
MAIN RESULTS
We included 17 RCTs, with sample sizes ranging from five to 156 participants, and intervention follow-up periods from less than one day to five weeks. About half of included trials used a parallel-arm design; the rest adopted a cross-over design. A variety of participant characteristics was represented across the studies, ranging from healthy adults to individuals with mental health and sleep disorders. None of the studies had a low risk of bias in all seven Cochrane RoB 1 domains. We judged 65% of studies to have a high risk of bias due to outcome assessors not being masked (detection bias) and 59% to be at high risk of bias of performance bias as participants and personnel were not masked. Thirty-five per cent of studies were pre-registered on a trial registry. We did not perform meta-analyses for any of the outcome measures, due to lack of available quantitative data, heterogenous study populations, and differences in intervention follow-up periods. There may be no difference in subjective visual fatigue scores with blue-light filtering lenses compared to non-blue-light filtering lenses, at less than one week of follow-up (low-certainty evidence). One RCT reported no difference between intervention arms (mean difference (MD) 9.76 units (indicating worse symptoms), 95% confidence interval (CI) -33.95 to 53.47; 120 participants). Further, two studies (46 participants, combined) that measured visual fatigue scores reported no significant difference between intervention arms. There may be little to no difference in CFF with blue-light filtering lenses compared to non-blue-light filtering lenses, measured at less than one day of follow-up (low-certainty evidence). One study reported no significant difference between intervention arms (MD - 1.13 Hz lower (indicating poorer performance), 95% CI - 3.00 to 0.74; 120 participants). Another study reported a less negative change in CFF (indicating less visual fatigue) with high- compared to low-blue-light filtering and no blue-light filtering lenses. Compared to non-blue-light filtering lenses, there is probably little or no effect with blue-light filtering lenses on visual performance (BCVA) (MD 0.00 logMAR units, 95% CI -0.02 to 0.02; 1 study, 156 participants; moderate-certainty evidence), and unknown effects on daytime alertness (2 RCTs, 42 participants; very low-certainty evidence); uncertainty in these effects was due to lack of available data and the small number of studies reporting these outcomes. We do not know if blue-light filtering spectacle lenses are equivalent or superior to non-blue-light filtering spectacle lenses with respect to sleep quality (very low-certainty evidence). Inconsistent findings were evident across six RCTs (148 participants); three studies reported a significant improvement in sleep scores with blue-light filtering lenses compared to non-blue-light filtering lenses, and the other three studies reported no significant difference between intervention arms. We noted differences in the populations across studies and a lack of quantitative data. Device-related adverse effects were not consistently reported (9 RCTs, 333 participants; low-certainty evidence). Nine studies reported on adverse events related to study interventions; three studies described the occurrence of such events. Reported adverse events related to blue-light filtering lenses were infrequent, but included increased depressive symptoms, headache, discomfort wearing the glasses, and lower mood. Adverse events associated with non-blue-light filtering lenses were occasional hyperthymia, and discomfort wearing the spectacles. We were unable to determine whether blue-light filtering lenses affect contrast sensitivity, colour discrimination, discomfort glare, macular health, serum melatonin levels or overall patient visual satisfaction, compared to non-blue-light filtering lenses, as none of the studies evaluated these outcomes.
AUTHORS' CONCLUSIONS
This systematic review found that blue-light filtering spectacle lenses may not attenuate symptoms of eye strain with computer use, over a short-term follow-up period, compared to non-blue-light filtering lenses. Further, this review found no clinically meaningful difference in changes to CFF with blue-light filtering lenses compared to non-blue-light filtering lenses. Based on the current best available evidence, there is probably little or no effect of blue-light filtering lenses on BCVA compared with non-blue-light filtering lenses. Potential effects on sleep quality were also indeterminate, with included trials reporting mixed outcomes among heterogeneous study populations. There was no evidence from RCT publications relating to the outcomes of contrast sensitivity, colour discrimination, discomfort glare, macular health, serum melatonin levels, or overall patient visual satisfaction. Future high-quality randomised trials are required to define more clearly the effects of blue-light filtering lenses on visual performance, macular health and sleep, in adult populations.
Topics: Adult; Humans; Eyeglasses; Asthenopia; Melatonin; Sleep; Light; Drug-Related Side Effects and Adverse Reactions
PubMed: 37593770
DOI: 10.1002/14651858.CD013244.pub2