Prediction of ovarian hyperstimulation syndrome in women undergoing in vitro fertilization: A systematic review

 Corresponding author: Ibtesam Fawaz Alshammari Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Prediction of ovarian hyperstimulation syndrome in women undergoing in vitro fertilization: A systematic review Ibtesam Fawaz Alshammari 1, *, Maya Abdullah Alyahya 2, Shumukh Okab K AlSalem 3, Suha Hashim Hassan Elsayed 4, Muneerah A Aljumah 5 and Ragdah faisal al hatim 6 1 Consultant OB/ GYN, Infertility, IVF and MIS, Head of IVF center in KSMC. 2 Obstetric and gynecology Resident, King Saud Medical center. 3 Obstetric and Gynecology Resident, King Saud Medical City (KSMC), Riyadh. 4 Consultant Obstetric and Gynecology, King Saud Medical City, KSMC. 5 Obstetrics and gynecology Resident, Riyadh second cluster. 6 Obstetric and gynecology Resident, Women health Hospital in king Saud medical city. World Journal of Advanced Research and Reviews, 2025, 27(02), 295-302 Publication history: Received on 25 June 2025; revised on 02 August 2025; accepted on 05 August 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.27.2.2869 Abstract Background: Ovarian hyperstimulation syndrome (OHSS) is a significant complication of controlled ovarian stimulation in assisted reproductive technologies (ART). Identifying predictors of OHSS is important to optimize patient outcomes and prevent severe complications. This systematic review aimed to discuss evidence on predictive markers of OHSS in women undergoing in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) cycles. Methods: Following the PRISMA guidelines, electronic searches conducted in PubMed, Scopus, Web of Science, and Google Scholar databases. Keywords related to OHSS prediction and IVF were used, restricted to English-language fulltext articles published between 2009 and 2024. Eligible studies included original prospective, retrospective, observational, or cohort studies investigating predictors of OHSS. Twelve studies were included in the qualitative synthesis. Results: Antral follicle count (AFC), anti-Müllerian hormone (AMH) levels, serum estradiol (E2) concentrations on the day of hCG administration, number of follicles, and number of retrieved oocytes emerged as the most evaluated predictors. High AFC, elevated AMH, and increased E2 levels were associated with development of OHSS. Several studies developed predictive models, including nomograms, with good discriminatory performance (AUC 0.70–0.85). Intrafollicular melatonin concentrations and coagulation factors were also studied. Conclusion: AFC, AMH, and E2 levels are reliable predictors of OHSS risk in IVF/ICSI cycles. Integrating traditional markers with emerging predictive tools allow earlier identification of high-risk patients, and enable stimulation protocols and improved clinical outcomes. Keywords: Ovarian hyperstimulation syndrome; OHSS prediction; IVF; Antral follicle count Anti-Müllerian hormone; Assisted reproductive technologies 1. Introduction Ovarian hyperstimulation syndrome (OHSS) is iatrogenic complication of controlled ovarian stimulation (COS) in assisted reproductive technologies (ART), mainly in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) World Journal of Advanced Research and Reviews, 2025, 27(02), 295-302 296 (1). It is characterized by increased vascular permeability, leading to fluid shifts into the third space and associated with morbidity, and in severe cases, mortality (2). The pathophysiology of OHSS is complex and driven by the upregulation of vascular endothelial growth factor (VEGF) in the ovaries, resulting in capillary leakage (3). The incidence of OHSS differ depending on patient populations and stimulation protocols. Mild to moderate OHSS occurs in 20–33% of IVF cycles, and severe OHSS is reported in 0.1–2% of cycles (2). Including the introduction of GnRH antagonist cycles and safer ovulation trigger alternatives, OHSS is a major concern in ART programs (4). Identifying patients at increased risk is important to prevent the occurrence of OHSS. Several predictors have been initiated, with antral follicle count (AFC), anti-Müllerian hormone (AMH) levels, serum estradiol concentrations, and the number of growing follicles is the most significant markers (3). The presence of ≥15 follicles ≥10 mm on the day of hCG administration associated with the development of moderate to severe OHSS (3). AMH is independent predictor of ovarian response, with high AMH levels correlating with an increased risk of OHSS (5). AMH measurement allows for the adjustment of stimulation protocols to reduce the risk of excessive response. GnRH agonist triggers in antagonist cycles and embryo cryopreservation, were efficient in reducing OHSS incidence without compromising pregnancy rates (6). This systematic review aims to find evidence about the predictive markers for OHSS in IVF/ICSI cycles. A better understanding of these predictors facilitate individualized ovarian stimulation protocols, minimize the incidence of OHSS, and improve patient safety and ART outcomes. 2. Methodology This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) guidelines. We aimed to identify, and evaluate original research studies focused on predicting the risk of OHSS in women undergoing in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI). 2.1. Search Strategy An electronic search was performed in PubMed, Web of science, Scopus and Google scholar.. Keywords include: ovarian hyperstimulation syndrome, OHSS prediction, in vitro fertilization, IVF, risk factors, and predictive models. The search was restricted to full-text original articles published in English in the period from 2009 to 2024. 2.2. Eligibility and exclusion Criteria We include, original research articles (prospective, retrospective, observational, or cohort studies); investigated predictors or developed models for OHSS risk in IVF/ICSI patients; reported specific predictive factors (estradiol levels, antral follicle count, oocyte retrieval numbers, hormonal markers, or clinical scores); and provided sufficient methodological details for data extraction. We exclude reviews, case reports, conference abstracts, editorials, expert opinions, studies focusing exclusively on OHSS treatment without predictive assessment, and non-English language publications. We include 12 articles in the qualitative analysis (Fig1). 2.3. Study Selection Two reviewers screened titles and abstracts. Full-text articles were retrieved for eligible studies and assessed against the inclusion and exclusion criteria. Discrepancies resolved through discussion with a third reviewer. 2.4. Data Extraction A data extraction form was developed to collect the important information from each study which include, citation details, study design and duration, population characteristics, predictive factors and methods used, main findings, and outcomes. Qualitative data synthesis was performed to find predictive factors, and outcome definitions. Results were grouped and compared based on predictors evaluated. World Journal of Advanced Research and Reviews, 2025, 27(02), 295-302 297 Figure 1 PRISMA consort chart of study selection 3. Results A total of twelve original studies were included in this systematic review, which analyze predictive methods for OHSS risk in women undergoing in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI). The studies include different populations and clinical settings, varied predictive markers and statistical approaches (Table 1). The included studies evaluated diverse patient groups, rang from general IVF populations to high-risk groups such as patients with polycystic ovary syndrome (PCOS). Age distribution differ but included women of reproductive age (20– World Journal of Advanced Research and Reviews, 2025, 27(02), 295-302 298 40 years). Two studies targeted PCOS populations (Li et al., 2021; Zheng et al., 2019), while others assessed broader IVF cohorts. Antral follicle count (AFC), serum anti-Müllerian hormone (AMH) levels, estradiol (E2) levels on the day of human chorionic gonadotropin (hCG) administration, number of follicles, and number of retrieved oocytes were the most common evaluated indicators. Five studies indictaed that AFC and AMH were strong predictors of OHSS (Tan et al., 2021; Li et al., 2021; Zheng et al., 2019; Madrazo et al., 2020; Griesinger et al., 2016). E2 levels on the day of hCG were investigated in six studies, with varying cutoffs proposed for risk stratification (Table 1). Several studies developed predictive models. Nomograms were used by Li et al. (2021) and Tan et al. (2021) to facilitate clinical application. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to evaluate predictive performance, with area under the curve (AUC) values rang between 0.70 and 0.85, which indicate good discriminatory capacity. Madrazo et al. (2020) reported an AUC of 0.85 for number of ova collected predicting severe OHSS. Some studies examined novel predictors: Zheng et al. (2019) show intrafollicular melatonin levels as a new biomarker, which had a strong correlation with traditional risk factors and IVF outcomes. Abbara et al. (2018) compared the incidence of OHSS between different ovulation triggers, and indicate kisspeptin as a safer alternative. Outcomes differ but show the importance of early risk prediction to guide preventive strategies, trigger modification, cycle cancellation, or embryo freezing (Table 2). Table 1 Characteristics of the studies included Citation Study Design Study Duration Inclusion Criteria Prediction Method Used Study Aim Methodology Aljawoan et al., 2012 (7) Retrospective study 2004–2007 Coasted patients in IVF/ICSI cycles Serum E2 and number of oocytes Identify predictors of OHSS in coasted patients Manual review of coasted patient records Kaur et al., 2015 (8) Retrospective observational study 2012–2014 Women undergoing IVF at risk of OHSS Hematocrit levels Evaluate hematocrit as a predictor of OHSS Review of hematocrit at OPU and ET days Kahnberg et al., 2009 (9) Prospective observational study 2003–2005 First IVF/ICSI cycle with oocyte retrieval Number of follicles, oocytes retrieved Identify independent predictors of severe OHSS Univariate and multivariate analyses Grynnerup et al., 2022 (10) Secondary analysis of RCT 2022 Women undergoing ART, examined at OPU and 5 days later Peritoneal fluid measurement on day 5 Evaluate peritoneal fluid as predictor for severe late OHSS Logistic regression and ROC analysis Madrazo et al., 2020 (11) Retrospective study 2008–2017 Women undergoing IVF between 20082017 Estradiol levels, AFC, number of ova collected Assess estradiol, follicle count, ova in predicting OHSS ROC analysis of E2, AFC, ova collected Tarlatzi et al., 2017 (12) Retrospective cohort study 2009–2014 Fresh IVF cycles using gonadotropins and GnRH analogues Follicle count ≥10 mm Identify best predictor of severe OHSS GEE modeling for multiple IVF cycles World Journal of Advanced Research and Reviews, 2025, 27(02), 295-302 299 Shields et al., 2016 (13) Case-control study 2008–2013 Women admitted with OHSS diagnosis after IVF Risk score based on multiple risk factors Identify risk factors and create a risk score Multivariate Poisson regression to create risk score Abbara et al., 2018 (14) Retrospective cohort study 2013–2016 Women at high risk of OHSS undergoing IVF Comparison of hormonal triggers (hCG, GnRHa, kisspeptin) Compare OHSS symptoms after different triggers Comparison across three hormonal trigger groups Tan et al., 2021 (15) Longitudinal study 2018–2019 Women undergoing IVF-ET with specific eligibility criteria AFC, AMH, and progesterone levels Develop prediction model for high ovarian response Logistic regression and internal validation Li et al., 2021 (16) Retrospective analysis Not explicitly stated (based on retrospective IVF cycles) PCOS patients undergoing IVF/ICSI Nomogram based on AMH, FSH, E2, Gn dose, follicles Develop a nomogram predicting OHSS risk in PCOS patients Logistic regression and nomogram creation Griesinger et al., 2016 (17) Retrospective analysis of RCT data Data from Engage, Ensure, Trust trials (RCTs completed previously) Women treated with corifollitropin alfa or rFSH in GnRH antagonist protocols Number of follicles ≥11 mm and E2 levels on hCG day Identify threshold for predicting OHSS using follicles and E2 levels ROC analysis, logistic regression on pooled RCT data Zheng et al., 2019 (18) Prospective observational study April 2016 to May 2016 Women undergoing IVF-ET, 20 OHSS and 23 non-OHSS women Intrafollicular melatonin concentration measurement Assess intrafollicular melatonin as a predictor for OHSS Measurement of follicular fluid melatonin and correlation analysis Table 2 Main findings of the studies included Citation Demographic Characteristics Main Findings Outcome Aljawoan et al., 2012 IVF coasted patients, age range not reported High estradiol and oocyte numbers linked to OHSS risk Serum E2 and oocyte retrieval numbers valuable for OHSS risk stratification Kaur et al., 2015 Women at high risk of OHSS, various ages Hematocrit is a simple predictor for OHSS Simple blood test (hematocrit) aids early OHSS detection Kahnberg et al., 2009 First IVF cycle patients, mainly young women Follicle count and oocytes best predicted severe OHSS Ultrasound and retrieval data useful for predicting severe OHSS Grynnerup et al., 2022 Women undergoing ART with variable risk Peritoneal fluid volume predicts late OHSS severity New predictor for late OHSS cases identified World Journal of Advanced Research and Reviews, 2025, 27(02), 295-302 300 Madrazo et al., 2020 (11) Women undergoing IVF, control and OHSS groups matched Ova number and E2 levels predict OHSS and culdocentesis need Early intervention strategies possible with E2, AFC, ova collected Tarlatzi et al., 2017 (12) Women in IVF cycles, follicle monitoring Follicle count ≥15 on trigger day best predicts severe OHSS Follicle monitoring key to prevent severe OHSS Shields et al., 2016 (13) Women hospitalized for OHSS post IVF Risk score including multiple factors predicts OHSS hospitalization Risk score enables proactive OHSS management Abbara et al., 2018 Women triggered with hCG, GnRHa, or kisspeptin Kisspeptin trigger associated with lowest OHSS risk Kisspeptin may be preferred for high-risk patients Tan et al., 2021 (15) Women aged 20–40 years undergoing IVF-ET AFC, AMH, and progesterone predict high ovarian response Model accurately identifies high responders Li et al., 2021 (16) PCOS patients undergoing IVF/ICSI Nomogram model predicts OHSS risk in PCOS patients Tool improves patient-specific risk management Griesinger et al., 2016 Women aged 18–36 years in GnRH antagonist protocol ≥19 follicles ≥11 mm predicts moderate/severe OHSS Follicle count better than E2 for OHSS risk prediction Zheng et al., 2019 Women undergoing IVF aged 20–35 years Intrafollicular melatonin higher in OHSS patients Melatonin may be used as a novel OHSS biomarker 4. Discussion This systematic review include twelve original studies to assess predictors of OHSS in patients undergoing in IVF or ICSI. Elevated antral follicle count (AFC), high anti-Müllerian hormone (AMH) levels, and elevated serum estradiol (E2) levels at the time of human chorionic gonadotropin (hCG) administration were reported. These markers are included in assessment of patient's risk profile before oocyte retrieval and have been widely used in clinical decision-making (19). Metabolomic profiling of follicular fluid show that elevated concentrations of deoxyinosine, L-isoleucine, and pyruvic acid, are correlated with the number of retrieved oocytes and serum E2 levels, which give potential as a predictors of OHSS (19). This biochemical approach supplement traditional markers and give earlier, and more accurate indication of OHSS risk. In vitro maturation (IVM) represents one such alternative, eliminate the risk of OHSS while permitting oocyte retrieval without full ovarian stimulation (20). IVM is associated with lower cumulative live birth rates compared to conventional IVF, but it give a compelling option for OHSS-prone patients and accent the importance of individualized therapy. Predictive modeling also gained prominence as method to optimize patient-specific risk assessment. Recent developments through a nomogram model incorporates patient characteristics (age, BMI, baseline hormone levels, and ovarian response parameters) to predict the moderate to severe OHSS (21). These models assist clinicians in risk stratification and enable preemptive adjustments to treatment plans, minimizing complications. The follicle count remains is a powerful and simple predictor for severe OHSS. Studies indicate that having 15 or more follicles ≥10 mm on the day of hCG administration is associated with increased risk (22). This obtainable ultrasonographic marker is valuable in guiding trigger strategies and considering a freeze-all approach to mitigate OHSS risk. Coagulation and fibrinolysis markers are emerging as predictive biomarkers in addition to hormonal and ultrasound parameters. Elevated levels of thrombin-antithrombin complex and plasmin-α2-plasmin inhibitor complex (PIC) correlate with OHSS severity, which suggest that vascular permeability changes integral to OHSS pathophysiology is detectable through simple blood tests (23). The integration of such laboratory biomarkers into predictive algorithms improve early preventive measures. List of Abbreviations OHSS, Ovarian Hyperstimulation Syndrome; IVF, In vitro Fertilization; ICSI, Intracytoplasmic Sperm Injection; COS, Controlled Ovarian Stimulation; ART, Assisted Reproductive Technologies; hCG, Human Chorionic Gonadotropin; GnRH, Gonadotropin-Releasing Hormone; AFC, Antral Follicle Count; AMH, Anti-Müllerian Hormone; VEGF, Vascular World Journal of Advanced Research and Reviews, 2025, 27(02), 295-302 301 Endothelial Growth Factor; E2, Estradiol; IVM, In vitro Maturation; TAT, Thrombin-Antithrombin Complex; PIC, Plasmin-α2-Plasmin Inhibitor Complex; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; AUC, Area Under the Curve. 5. Conclusion AFC, AMH, and E2 levels were a reliable and practical predictors to assess OHSS risk in IVF/ICSI patients. Early identification of high-risk patients through predictive models allow for safer treatment protocols, reduced complications, and improved reproductive outcomes. 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