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Record W4411200804 · doi:10.1016/j.ajo.2025.06.012

Eplerenone and Spironolactone for Chronic Central Serous Chorioretinopathy: A Systematic Review and Meta-Analysis

2025· review· en· W4411200804 on OpenAlexaff
Ryan S. Huang, Andrew Mihalache, Ali Benour, Michele Zaman, Marko M. Popovic, Peter J. Kertes, Rajeev H. Muni, David Sarraf, Srinivas R. Sadda, Radha P. Kohly

Bibliographic record

VenueAmerican Journal of Ophthalmology · 2025
Typereview
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsToronto East General HospitalSt. Michael's HospitalSunnybrook Health Science CentreQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsEplerenoneSpironolactoneMedicineSerous fluidInternal medicineCardiologyAldosterone

Abstract

fetched live from OpenAlex

TOPIC: To evaluate the efficacy and safety of mineralocorticoid receptor antagonists (MRAs), specifically eplerenone and spironolactone, in comparison to observation, photodynamic therapy (PDT), and subthreshold micropulse laser (SML) for chronic central serous chorioretinopathy (cCSCR). CLINICAL RELEVANCE: In the context of cCSCR, MRAs are thought to reduce choroidal vascular hyperpermeability and thickness by inhibiting the mineralocorticoid receptor pathways that contribute to fluid accumulation. METHODS: A systematic literature search was performed using Ovid MEDLINE, Embase, and the Cochrane Library from January 2000 to March 2024 for comparative studies evaluating the efficacy of MRAs against other treatment arms for cCSCR. The primary outcome was the best-corrected visual acuity (BCVA) at the last study visit, as well as at specific follow-up timepoints (ie, 1 month, 3 months, 6 months, 12 months). Secondary outcomes included retinal thickness (RT), subretinal fluid (SRF) height, and SRF resolution at the same timepoints. Meta-analyses were performed using a random-effects model, with subgroup analyses performed for eplerenone and spironolactone separately. A P-value of less than 0.05 was considered statistically significant. RESULTS: Thirteen articles (four RCTs reporting on 253 eyes and nine observational studies reporting on 393 eyes, mean follow-up duration = 7.02 ± 3.78 months) were included. The mean BCVA at the last study visit was similar between the MRA and observation groups (WMD=-0.01 logMAR, 95% CI = [-0.05, 0.02], P = .40, n = 5 studies). However, MRAs resulted in a significantly lower mean SRF height at 1 month (WMD=-69.56 µm, 95% CI [-127.26, -11.86], P = .02, n = 2 studies), while the observation group had a significantly lower SRF height at 12 months (WMD=48.23 µm, 95% CI [45.99, 50.46], P < .00001, n = 2 studies). Similarly, MRAs demonstrated a higher rate of SRF resolution at 1 month (RR=4.24, 95% CI = [1.54, 11.72], P = .005), whereas the observation group showed a higher resolution rate at 12 months (RR=0.45, 95% CI = [0.22, 0.95], P = .04). On subgroup analysis, spironolactone showed a significantly reduced mean RT at the last study visit compared to observation (WMD = -46.44 µm, 95% CI [-74.76, -18.13], P = .001, n = 2 studies). When compared to PDT, MRAs were associated with a significantly higher mean SRF height at the last study visit (WMD = 51.99 µm, 95% CI [2.70, 101.27], P = .04, n = 2 studies). In contrast, efficacy outcomes were largely similar between patients treated with MRAs and SML at the last study visit, with no significant differences in SRF resolution (P = .22, n = 2 studies). CONCLUSION: MRAs offer short-term benefits in reducing SRF but may have limited long-term durability based on current evidence, highlighting the need for further studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0150.026
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.051
GPT teacher head0.390
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations3
Published2025
Admission routes1
Has abstractno

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