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Record W1690447342 · doi:10.1371/journal.pone.0128146

iStent as a Solo Procedure for Glaucoma Patients: A Systematic Review and Meta-Analysis

2015· review· en· W1690447342 on OpenAlexaff
Monali S. Malvankar‐Mehta, Yufeng Nancy Chen, Yiannis Iordanous, Wan Wendy Wang, John Costella, Cindy Hutnik

Bibliographic record

VenuePLoS ONE · 2015
Typereview
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsGlaucomaMedicineMeta-analysisIntraocular pressureGlaucoma medicationOphthalmologyPharmacotherapySubgroup analysisStudy heterogeneityMEDLINESurgeryInternal medicine

Abstract

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BACKGROUND: Glaucoma is a leading cause of irreversible blindness. It is firmly entrenched in the traditional treatment paradigm to start with pharmacotherapy. However, pharmacotherapy is not benign and has been well documented to have a number of significant challenges. Minimally invasive glaucoma surgery (MIGS) that targets the outflow pathway with minimal to no scleral dissection has resulted in the need to reconsider the glaucoma treatment paradigm. PURPOSE: To perform a systematic review and meta-analysis to evaluate and quantify the effect on post-operative intraocular pressure (IOP) and number of topical glaucoma medications, in patients receiving the iStent MIGS device as the solo procedure without concurrent cataract surgery. METHODS: A systematic review was conducted by searching various databases between January 1, 2000, and June 30, 2014. Studies reporting up to a maximum follow-up period of 24 months were retrieved and screened using the EPPI-Reviewer 4 gateway. Percentage reduction in IOP (IOPR%), and mean reduction in topical glaucoma medications after surgery were computed. Meta-analysis was performed using STATA v. 13.0. The standardized mean difference (SMD) was calculated as the effect size for continuous scale outcomes. Heterogeneity was determined using the I2 statistics, Z-value, and χ2 statistics. Fixed-effect and random-effect models were developed based on heterogeneity. Sub-group analysis was performed based on the number of iStents implanted and the follow-up period. The outcome measures were changes in the IOP and number of glaucoma medications. RESULTS: The search strategy identified 105 records from published literature and 9 records from the grey literature. Five studies with 248 subjects were included for quantitative synthesis. A 22% IOP reduction (IOPR%) from baseline occurred at 18-months after one iStent implant, 30% at 6-months after two iStents implantations, and 40% at 6-months after implantation of three iStents. A mean reduction of 1.2 bottles per patient of topical glaucoma medications occurred at 18-months after one iStent implant, 1.45 bottles per patient at 6-months after two iStents, and one bottle of medication per patient was reduced at 6-months following placement of three iStents implants. Meta-analysis results showed a significant reduction in the IOP after one iStent (SMD = -1.68, 95% CI: [-2.7, -0.61]), two iStents (SMD = -1.88, 95% CI: [-2.2, -1.56]), and three iStents (SMD = -2, 95% CI: [-2.62, -1.38]) implantation. Results showed a significant drop in the topical glaucoma medications after one iStent (SMD = -2.11, CI: [-3.95, -0.27]), two iStent (SMD = -1.88, CI: [-2.20, -1.56]), and three iStents (SMD = -2.00, CI: [-2.62, -1.38]) implantation. The maximum reduction in IOP occurred at 12-months (SMD = -2.21, CI: [-2.53, -1.88]) and a significant reduction in post-operative topical glaucoma medications occurred even after 18-months of iStent implantation (SMD = -0.71, CI: [-1.15, -0.26]). CONCLUSION: iStent implantation as a solo procedure without concurrent cataract extraction does lower IOP, and reduces the dependency on glaucoma medications. This effect seems to last at least 18 months.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.569
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0150.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.157
GPT teacher head0.334
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations92
Published2015
Admission routes1
Has abstractyes

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