ReviewCureus2026
Clinical Predictors of Successful Pregnancy After In Vitro Fertilization (IVF): A Comprehensive Systematic Review of Evidence.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In vitro fertilization (IVF) success is influenced by a complex interplay of patient- and treatment-related factors. Identifying reliable clinical predictors is crucial for patient counseling and individualized treatment planning. This systematic review aimed to synthesize the most recent evidence on clinical predictors of successful pregnancy after IVF. A comprehensive search of five electronic databases (PubMed, Scopus, Excerpta Medica database (Embase), Web of Science, and ClinicalTrials.gov) was conducted for studies published between 2020 and 2025. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, studies evaluating clinical predictors of clinical pregnancy or live birth in women undergoing IVF were included. Study selection, data extraction, and risk of bias assessment (using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool) were performed independently by two reviewers. A narrative synthesis was conducted due to significant methodological heterogeneity. Eight retrospective observational studies, comprising data from 40,490 IVF/intracytoplasmic sperm injection (ICSI) cycles, were included. Female age was the most consistent and powerful predictor, with a non-linear negative impact, particularly beyond 40 years. Ovarian reserve markers, anti-Müllerian hormone (AMH) and antral follicle count (AFC), were significant, with evidence suggesting AMH better predicts oocyte yield while AFC may better forecast embryo availability. Embryo quality parameters (number of high-quality embryos) were strongly associated with success. Male factors, including total progressive motile sperm count (TPMC) and sperm DNA fragmentation index (DFI), added incremental predictive value. Several studies developed predictive models using both traditional logistic regression and machine learning (ML) algorithms (e.g., eXtreme Gradient Boosting (XGBoost) and Random Forest), which demonstrated high accuracy but raised concerns regarding interpretability and temporal validity. Successful IVF pregnancy is multifactorial, with female age, ovarian reserve, embryo quality, and sperm DNA integrity being key prognostic determinants. While ML-based models show promise, their clinical integration requires rigorous external validation and transparency. Future research should prioritize prospective, multi-center designs and the integration of novel dynamic parameters to advance toward truly personalized prognostic tools in reproductive medicine.
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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.