SynthesisBMC pregnancy and childbirth2025
Risk prediction models for ovarian hyperstimulation syndrome: a systematic review and meta-analysis.
Synthesis in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Clinical Management of Ovarian Hyperstimulation Syndrome with Furosemide at a Tertiary Referral Center-A Retrospective Data Analysis.Journal of clinical medicine · 2026Article
- Cumulative live birth after unstimulated in vitro maturation versus conventional in vitro fertilization (IVF) in women with polycystic ovary syndrome: a 36-month retrospective matched cohort study.Journal of ovarian research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundOvarian hyperstimulation syndrome (OHSS) is a serious complication of controlled ovarian stimulation (COS). The main clinical manifestation of OHSS is increased ovarian volume. OHSS can cause local and systemic tissue oedema, electrolyte disturbances, cardiorespiratory dysfunction, coagulation dysfunction, and other symptoms. These symptoms greatly affect patients' quality of life. As infertility rates rise and assisted reproductive technology (ART) becomes more common, the risk of OHSS increases. Therefore, early identification of high-risk patients and timely intervention are crucial.
methodsThe PubMed, Embase, Cochrane Library, Web of Science, CINAHL, China National Knowledge Internet (CNKI), Wanfang, China Science and Technology Journal Database (VIP), and China Biology Medicine (CBM) databases were systematically searched from inception to March 30, 2025. Two researchers independently screened the literature, extracted data, and evaluated the quality of included studies using the updated prediction model risk of bias assessment tool (PROBAST + AI). We conducted a meta-analysis of predictors from the developed models using Stata 15.0 software.
resultsA total of 16 studies were included, comprising 29 OHSS risk prediction models. The area under the curve (AUC) ranged from 0.628 to 0.998, with 23 models demonstrating AUC > 0.700. Model calibration was performed in 10 studies, internal validation in 14 studies, and 2 studies conducted both internal and external validation. The PROBAST + AI assessment identified a high risk of bias across the included studies, primarily in the research design and statistical analysis domains. The most common predictors identified across the models included: antral follicle count (AFC), estrogen (E
conclusionsOur findings indicate substantial variation in OHSS incidence. Interpretation of the results should be with caution due to the limitations of the current evidence. Current OHSS risk prediction models remain under development and require further refinement. Future efforts to build and improve these models should focus on key areas, including research design, sample size, handling of missing data, model calibration and validation, and detailed reporting.
trial registrationPROSPERO CRD420251025876.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.