Effectiveness of hormone add-on strategies in ovarian stimulation for women with poor ovarian response: a systematic review and network meta-analysis of randomized controlled trials.

This network meta-analysis was carried out using the procedures described in the Cochrane Handbook for Systematic Reviews of Interventions [ 24 ] and the Mbuagbaw et al. methodological specifications [ 25 ]. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension statement for network meta-analyses (PRISMA-NMA) was adhered to.The study protocol (CRD42024618797) was submitted to the PROSPERO (International Prospective Register of Systematic Reviews) database on 25 November 2024. The following Medical Subject Heading (MeSH) terms and keywords were used to search the electronic databases MEDLINE (available through PubMed), SCOPUS, LILACS, EMBASE, Scielo.br, and PROSPERO: “ovarian stimulation”/“ovulation induction” (MeSH Unique ID: D010062), “assisted reproductive technologies” (MeSH Unique ID: D027724), “in vitro fertilization” (MeSH Unique ID: D005307), “reproductive techniques”, “POSEIDON criteria”, “Bologna criteria”, “low responders”, “poor responders”, “poor ovarian response”, “poor response”, “poor ovarian responsiveness”, “ovarian reserve” (MeSH Unique ID: D065851), “diminished ovarian reserve”, “poor ovarian response”and “poor ovarian reserve”. without any date restrictions. The search query was changed to fit the format of each database (Appendix  1 ). A filter for randomized controlled trials (RCTs) was used in each database.Additional searches were conducted to find more relevant papers on CINAHL, PsycINFO, and AMED in order to reduce publication bias. The Cochrane Central Register of Controlled Studies, Clinicaltrials.gov, and the World Health Organization’s International Clinical Trials Registry Platform (ICTRP) were also searched to locate additional randomized controlled trials. Additionally, the gray literature (NTIS, PsycEXTRA) was examined to find national and international conference abstracts. The included study’s references and pertinent reviews were also reviewed to locate more articles that eluded our first search.The analysis did not include any editorials, letters to the editor, comments, or second thoughts publications. A.E. and V.A. carried out the approach for the literature search. The titles and abstracts of all the papers were separately screened by two reviewers (G.R. and J.C.C.F.) to identify which research should be evaluated further and to omit citations that were judged unnecessary by both observers.The authors, institutions, publication titles, and study findings were all concealed during this initial screening. Any ambiguity or disagreement was cleared up by talking with a third reviewer (A.F.R). Two reviewers (A.B.G. and B.M.R.) retrieved and evaluated full texts of possibly relevant articles based on the pre-established inclusion criteria. In addition, methodological validity was evaluated before being included in the review. Every doubt or disagreement was cleared up by discussion among reviewers in order to reach an agreement.Add-ons have been defined as any additional treatment used, in addition to gonadotropin releasing hormone (GnRH) analogs and gonadotropins, during the vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) cycle, with the goal of increasing pregnancy success in women with POR. As such, hormonal add-ons directly affect the endocrine system and influence ovarian response, oocyte quality, or endometrial receptivity. In this review, we include letrozole (LTZ) and clomiphene citrate (CC) as hormonal add-ons, although they are more commonly defined as ovulation-inducing agents. We included only RCTs that allocated women with POR, according to Bologna criteria [ 23 ], undergoing ART to at least two different OS protocols with the treatment arm including at least one hormonal add-on. We chose to use the Bologna criteria to strictly limit the inclusion criteria of patients with POR in the network meta-analysis. Considering the recent introduction but the paucity of studies adopting exclusively or predominantly the POISEDON criteria, we opted to exclusively use the Bologna criteria in the present analysis to ensure greater homogeneity of the population, significantly reducing clinical and methodological variability and thus increasing the reliability and generalizability of the results thanks to their validation and consolidation in a wider amount of RCTs. Control arms consisted of women undergoing the same OS protocol without the hormonal add-ons. The exclusion criteria were the following: studies including different OS protocols between treatment and control groups, studies with women not matching the Bologna criteria [ 23 ] and studies evaluating non-hormonal add-ons defined as additional treatments that improve metabolic, immunologic, and supportive factors without directly modifying hormonal balance. The extraction forms were made especially for this network meta-analysis. The nation, study design, study type, inclusion and exclusion criteria, study period, location, were extracted from each study. For each group (OS + add-ons and OS without add-ons) the following study characteristics were gathered in order to characterize the included studies: factors such as the number of women, the women’s age, body mass index (BMI), the type and duration of her infertility, the AFC, the AMH, day-3 FSH, the number of previous failed fertility treatments, the cause of infertility besides poor ovarian response, the method of diagnosis, the characteristics of IVF/ICSI, the protocol for OS, the duration of the stimulation, the total dosage of gonadotropins used during the OS, the cancelation rate, the number of retrieved and metaphase II (MII) oocytes, the rates of fertilization, the clinical pregnancy rate (CPR) and the LBR. Each abstract was examined and categorized independently by two authors (A.E. and V.A.). In order to agree on possible application, the same two writers carefully read the texts of the selected studies and independently gathered pertinent information about the research characteristics and the noteworthy findings. After discussing each contradiction with the other three authors (J.C.C.F., A.F.R. and B.M.R.), the reviewers came to a consensus. Unpublished data were acquired by contacting the original study authors directly when the study methods specified that additional outcome data were collected. The risk of bias for the included RCTs was assessed using the Cochrane Risk of Bias Tool version 2 (RoB 2.0) [ 26 ]. This tool categorizes the risk of bias in research into five areas: the randomization process, deviations from intended interventions, missing outcome data, outcome evaluation, and choice of reported result. Three risk levels are evaluated for every domain: “low risk”, “some concerns” or “high risk”. Three authors (A.E., V.A. and G.R.) independently evaluated the risk of bias assessment. The dispute was settled after concealment with J.C.C.F., A.F.R. and A.B.G. We examined the feasibility and comparability of baseline patient characteristics, the reported results, and the registration of trials before the recruitment of the first patient (for RCTs that began less than 15 years ago) to further assess the reliability of the studies. Additionally, we examined each study’s compliance with the CONSORT declaration and any believable retractions or expressions of concern using the PubMed and RetractionWatch.org databases. Furthermore, the Confidence in Network Meta-Analysis framework (CINeMA) criteria were used to evaluate the certainty of the evidence [ 27 ]. Publication bias was assessed using funnel plots and Egger’s test for each outcome reported in at least 10 trials. The primary outcome of the present network meta-analysis was the LBR, where a live birth was defined according to the International Glossary on Infertility and Fertility Care [ 28 ] as the complete expulsion or removal of a fetus from a woman after at least 22 weeks’ gestation, which, when separated, manifests vital signs. Secondary outcomes were the CPR, defined as pregnancy diagnosed by ultrasonographic visualization of one or more gestational sacs or definitive clinical signs of pregnancy [ 28 ]; total dosage of gonadotropins used during the OS; duration in days of the OS; the serum estrogen levels at the day of trigger with the human chorionic gonadotropin (hCG) or the agonist of the GnRH; the number of oocytes retrieved; the number of MII oocytes retrieved; the fertilization rate, defined as the number of fertilized oocytes on the total number of oocytes; the cancellation rate defined as the number of cycles cancelled due to ultrasonographic non-response to gonadotropin administration or absence of oocytes obtained out of the number of oocyte retrievals. All data analysis and graphical representations were done using STATA version 14.1 (StataCorp, College Station, TX). The network assumption of overall consistency was statistically confirmed for every meaningful result using the  command. The local test on loop inconsistencies was then carried out using the Separating Indirect from Direct Evidence (SIDE) procedure with the command . When no discrepancy between the global and local testing findings was found, the consistency assumption was accepted. The study’s direct and indirect comparisons would undoubtedly yield pertinent results in this case, as the consistency model demonstrated that any differences in the results were only attributable to chance mistakes and the effect of the intervention. The summary measurements were presented as odds ratios (OR) for categorical variables and standardized mean differences (SMD) for continuous variables with 95% confidence intervals (CI) using the Der Simonian and Laird random effects model. A Higgins I 2 score higher than 25% suggested the possibility of heterogeneity. To evaluate the efficacy of the different hormonal add-ons and rank the therapies to determine which was statistically better, a forest plot and an interval plot together with a Surface Under the Cumulative Ranking curve Area [SUCRA] ranking plot were made for each outcome under study.

A total of 7327 studies were initially identified using database searches. Of those, 1267 were removed as duplicates. After title and abstract screening, 5962 papers were subsequently removed (Fig.  1 ). Ninety-eight papers underwent Full-text assessment, of which 36 studies [ 10 , 18 – 20 , 29 – 59 ] were excluded for not having an appropriate hormonal add-on, 13 studies [ 14 , 15 , 60 – 70 ] for not respecting the POR definition according to Bologna criteria [ 23 ], 11 studies [ 9 , 71 – 80 ] were excluded for inappropriate control group, 8 studies for not being registered in appropriate RCT repositories, 3 studies [ 81 – 83 ] for being registered after enrollment of the first patient, 2 studies [ 84 , 85 ] for being non-randomized, 1 study [ 86 ] for having an expression of concern, 1 study [ 87 ] for being retracted, and 1 study [ 88 ] for untrustworthiness for the criteria mentioned above (Supplementary Table  1 ) (Fig.  1 ). Fig. 1 PRISMA flowchart of included studies in systematic review and network meta-analysis PRISMA flowchart of included studies in systematic review and network meta-analysis Therefore, 22 studies [ 12 , 13 , 16 , 17 , 89 – 106 ] were included in the systematic review and network meta-analysis (Fig.  1 ). The potential benefits of the following protocols with hormonal add-ons were analyzed: addition of LTZ [ 16 , 95 , 97 ], addition of CC [ 17 , 98 , 100 ], addition of recombinant luteinizing hormone (rLH) [ 94 , 102 , 104 ], pretreatment with estrogen [ 13 ], pretreatment with dehydroepiandrosterone (DHEA) [ 92 , 103 , 105 , 106 ], pretreatment with testosterone [ 89 , 91 , 93 , 99 , 101 ], and addition of human growth hormone (GH) [ 12 , 90 , 96 ]. The main characteristics of the included studies are summarized in Table  1 , and the baseline patients’ characteristics are presented in Table  2 . Table 1 Main characteristics of studies included in network meta-analysis Study Primary outcome(s) Add-ons Inclusion criteria Exclusion criteria Nationality Trial ID Aflatoonain, 2022 Clinical and biochemical pregnancy rates Addition of testosterone Poor ovarian response based on Bologna criteria • Endocrine disorders • Intrauterine disorders • Azoospermia • Severe endometriosis Iran IRCT20180818040828N1 Bastu, 2016 Number of oocytes retrieved Addition of letrozole Poor ovarian response based on Bologna criteria • History of chemotherapy/radiotherapy • ovarian surgery • DHEA/testosterone use Turkey NCT02293668 Bayoumi, 2016 Mean numbers of mature oocytes retrieved and fertilized Addition of Growth Hormone Poor ovarian response based on Bologna criteria • FSH > 20 IU/L, previous ovarian surgery • Non-POR infertility causes • PCOS • Endocrine disorders • Male factor infertility Egypt NCT02185326 Bosdou, 2016 Number of COCs retrieved Addition of testosterone Poor ovarian response based on Bologna criteria • BMI ≥ 32 kg/m2 • Endometriosis stage III-IV • Ovarian surgery • Endocrine/metabolic disorders • Use of testicular sperm Greece NCT01961336 Fu, 2017 High-quality embryo yield Addition of DHEA Poor ovarian response based on Bologna criteria • Endometriosis • History of chemotherapy • Ovarian surgery • Prior DHEA use China NCT02866253 Hoang, 2021 Number of mature oocytes retrieved Addition of testosterone Poor ovarian response based on Bologna criteria • POI • Male factor infertility • Severe endometriosis • Thyroid/liver/kidney disease • Abnormal genitalia Vietnamese NCT04602143 Humaidan, 2017 Number of oocytes retrieved Addition of rLH Poor ovarian response based on Bologna criteria • Women aged ≥ 41 years • POI • Preimplantation genetic screening Denmark NCT02047227 Liu, 2020 Cumulative Live Birth Rate Addition of Letrozole Poor ovarian response based on Bologna criteria • Repeated IVF failure • Severe endometriosis • PCOS China ChiCTR-TRC-13003454 Moffat, 2021 Number of oocytes retrieved Addition of Clomiphene Citrate Poor ovarian response based on Bologna criteria N/D Switzerland NCT01577472 Mohammad, 2020 Clinical pregnancy rate Addition of Growth Hormone Poor ovarian response based on Bologna criteria • Known medical disease (e.g. severe hypertension or hepatic disease) • History of altered karyotype in one or both partners • History of chronic, autoimmune or metabolic diseases • Presence of endocrinopathies • Male factor infertility Egypt NCT03759301 Moini, 2019 number of oocytes retrieved and the number of oocytes MII Addition of Letrozole Poor ovarian response based on Bologna criteria • POI • Donor/recipient treatments • Metabolic or endocrine disorders including hyperprolactinemia and hypo/hyperthyroidism • Endometriosis • Body mass index (BMI) > 30 kg/m2 • Azoospermic male partner Iran IRCT201701291952N8 Norman, 2019 Live birth rate Addition of Growth Hormone Poor ovarian response based on Bologna criteria • Any clinically significant systemic disease, • History of radiotherapy or chemotherapy • Any current history of malignant disease • Pituitary or hypothalamic disease • Current ovarian cyst > 3 cm • Any chronic infectious diseases • PCOS • Unexplained menstrual bleeding • Preimplantation genetic testing • Smokers • Using steroids, DHEA or prednisolone in the last 3 months Australia ACTRN12609001060235 Ragni, 2012 delivery rate per started cycle Addition of Clomiphene Citrate Poor ovarian response based on Bologna criteria • Number of previous IVF cycles ≥ 3 • Cycles requiring the use of spermatozoa from MESA-TESE procedures Italy NCT01389713 Saharkhiz, 2018 mean number of oocytes and embryo Addition of Testosterone Poor ovarian response based on Bologna criteria • Presence of endocrine disorders (thyroid, prolactin, etc.) • Presence of endometrioma and any history of surgery on the ovaries • Reluctance to participate in the project • New clinical conditions or a change in a treatment procedure • Sensitivity to testosterone gel Iran IR.SBMU.RETECH.REC.1395.1007 Siristatidis, 2017 Number of Cumulus- oocyte complexes retrieved Addition of Clomiphene Citrate Poor ovarian response based on Bologna criteria • Basal level of FSH at day 3 of menstrual cycle > 20 IU/l) • Increased BMI > 35 kg/m2 • History of endocrine or metabolic disorders • History of ovarian cystectomy or oophorectomy • Severe endometriosis • Severe azoospermia Greece NCT01319708 Subirà, 2021 Number of MII oocytes Addition of Testosterone Poor ovarian response based on Bologna criteria • Non-corrected uterine malformations, endometrial pathology • Severe male factor (motile sperm count  35 kg/m2 • Androgen treatment within the last 3 months • Known allergy to the experimental drug Spain NCT03378713 Tosun, 2021 GCs apoptosis rate in terms of viability, early apoptosis, late apoptosis and necrosis Addition of rLH Poor ovarian response based on Bologna criteria • Male factor • POI • Need of preimplantation genetic testing Turkey NCT03527823 Wang, 2022 Live birth rate Addition of DHEA Poor ovarian response based on Bologna criteria • Women that failed to obtain clinical pregnancy after three or more IVF/ICSI cycles • History of two or more recurrent pregnancy losses • Diagnosis of uterine abnormalities using hysteroscopy • Diagnosed with hydrosalpinx using hystero salpingography • History of chemotherapy with cytotoxic agents • History of pelvic radiotherapy • Epilepsy; • Treatment with DHEA before study enrolment • Allergy to DHEA China ChiCTR-IPR-15006909 Yeung, 2014 AFC after 12 weeks of treatment Addition of DHEA Poor ovarian response based on Bologna criteria • History of ovarian cystectomy or oophorectomy • History of cytotoxic chemotherapy • History of pelvic irradiation • History of taking testosterone or DHEA supplementation Hong Kong NCT01915186 Younis, 2016 maximal serum E2 level on the day of hCG administration Addition of rLH Poor ovarian response based on Bologna criteria • Severe endometriosis • Uncontrolled thyroid disease • Diabetes mellitus • Significant hyperprolactinaemia • Hypogonadotrophic hypogonadism • More than four previous unsuccessful ART attempts Israel NCT01016210 Zhang, 2014 follicular fluid BMP- 15, GDF-9 and serum AMH, FSH, E 2 Addition of DHEA Poor ovarian response based on Bologna criteria • History of ovarian cystectomy or oophorectomy • Diagnosis of endometriosis • History of DHEA supplementation or hormonal replacement therapy China ChiCTR-TRC-14005002 Zhang, 2022 total number of retrieved oocytes Addition of Estrogen Poor ovarian response based on Bologna criteria • Preimplantation genetic testing cycle • Donor cycle • Family history of thrombosis or a high risk of thrombosis • Previous history of hypertension or hypertension • Hyperlipidemia • Estrogen-dependent breast disease • Cervical biopsy showing cervical intraepithelial neoplasia grade III or above China NCT03300518 PCOS polycistic ovary syndrome, POI premature ovarian insufficiency, BMI body mass index, FSH follicle-stimulating hormone, rLH recombinant lutenizing hormone, DHEA Dehydroepiandrosterone, MII metaphase II, MESA microsurgical epididymal sperm aspiration, TESE testicular epididymal sperm extraction, AMH anti-Mullerian hormone, BMP −15 bone morphogenetic protein-15, GDF-9 growth differentiation factor-9, E2 estradiol Table 2 Baseline patient characteristics Study Group Age (mean ± SD), years AMH (mean ± SD), ng/ml AFC (mean ± SD), n Previously cycles with POR Aflatoonain, 2022 Control 36.21 ± 3.01 0.82 ± 0.08 5.54 ± 2.13 N/D Addition of Testosterone 35.38 ± 5.23 0.88 ± 0.08 5.34 ± 1.69 N/D Bastu, 2016 Control (450 IU) 36.94 ± 3.33 0.55 ± 0.35 4 N/D Control (300 IU) 35.00 ± 3.10 0.71 ± 0.28 3 N/D Addition of Letrozole 37.52 ± 4.06 0.64 ± 0.41 4 N/D Bayoumi, 2016 Control 34.8 ± 5.6 0.5 ± 0.2 5.9 ± 1.7 2.7 ± 1.5 Addition of Growth Hormone 34.9 ± 4.8 0.4 ± 0.2 5.9 ± 1.6 2.4 ± 1.5 Bosdou, 2016 Control 42.5 ± 4.0 0.65 ± 0.78 5.0 ± 3.0 N/D Addition of Growth Hormone 41.5 ± 3.0 0.97 ± 0.65 6.0 ± 4.0 N/D Fu, 2017 Control 36.8 ± 4.3 1.00 ± 0.77 3.57 ± 1.16 N/D Addition of DHEA 37.4 ± 3.6 1.01 ± 0.61 3.74 ± 1.43 N/D Hoang, 2021 Control 36.4 ± 5.2 1.0 ± 0.3 5.9 ± 3.6 N/D Addition of Testosterone (4 weeks) 35.9 ± 5.4 1.0 ± 0.4 5.4 ± 2.6 N/D Addition of Testosterone (6 weeks) 36.2 ± 4.7 0.9 ± 0.4 4.9 ± 1.9 N/D Humaidan, 2017 Control 38.3 ± 3.0 0.60 ± 0.48 4.8 ± 2.2 84.3% Addition of rLH 38.3 ± 2.9 0.58 ± 0.50 4.9 ± 2.3 82.0% Liu, 2020 Control 39 ± 1.5 0.8 ± 0.1 5 ± 0.33 N/D Addition of Letrozole 37 ± 1.17 0.6 ± 0.1 4 ± 0.33 N/D Moffat, 2021 Control (450 IU) 38.8 ± 1.3 0.78 ± 0.15 5.5 ± 1.2 N/D Control (150 IU) 39.9 ± 0.6 0.66 ± 0.09 6.0 ± 1.0 N/D Addition of Clomiphene Citrate (450 IU) 38.4 ± 1.0 0.64 ± 0.12 6.0 ± 0.5 N/D Addition of Clomiphene Citrate (150 IU) 39.9 ± 1.2 0.84 ± 0.14 6.5 ± 0.8 N/D Mohammad, 2020 Control 34.7 ± 2.0 0.69 ± 0.16 5.8 ± 1.8 2.6 ± 0.3 Addition of Growth Hormone 34.3 ± 2.4 0.72 ± 0.09 5.7 ± 1.8 2.5 ± 0.2 Moini, 2019 Control 36.5 ± 3.7 0.73 ± 0.31 4.8 ± 1.5 1.1 ± 0.6 Addition of Letrozole 37.2 ± 3.3 0.75 ± 0.35 5.1 ± 2.0 1.0 ± 0.9 Norman, 2019 Control N/D N/D N/D N/D Addition of Growth Hormone N/D N/D N/D N/D Ragni, 2012 Control 38.5 ± 3.1 0.76 ± 1.0 3.9 ± 2.6 N/D Addition of Clomiphene Citrate 38.6 ± 2.9 0.71 ± 1.0 3.4 ± 2.5 N/D Saharkhiz, 2018 Control 39.7 ± 3.3 0.6 ± 0.5 3.1 ± 2.6 N/D Addition of Testosterone 41.0 ± 3.8 0.5 ± 0.6 2.2 ± 0.6 N/D Siristatidis, 2017 Control 40.0 ± 3.0 N/D 4.0 ± 2.2 N/D Addition of Clomiphene Citrate 39.0 ± 2.7 N/D 3.0 ± 1.3 N/D Subirà, 2021 Control 35.2 ± 3.0 0.5 ± 0.3 N/D 0.8 ± 0.4 Addition of Testosterone (short) 37.1 ± 3.3 0.74 ± 0.41 N/D 0.9 ± 0.7 Addition of Testosterone (long) 36.9 ± 2.5 0.60 ± 0.32 N/D 0.9 ± 0.6 Tosun, 2021 Control 34.4 ± 2.6 0.3 ± 0.2 5.5 ± 1.4 N/D Addition of rLH 35.6 ± 4.6 0.4 ± 0.3 4.6 ± 1.5 N/D Wang, 2022 Control 39.5 ± 4.4 0.49 ± 0.11 4.9 ± 2.8 N/D Addition of DHEA 39.0 ± 4.6 0.55 ± 0.10 5.1 ± 2.2 N/D Yeung, 2015 Control 37.0 ± 0.8 0.64 ± 0.17 3.0 ± 0.3 N/D Addition of DHEA 36.0 ± 0.8 0.75 ± 0.19 4.0 ± 0.3 N/D Younis, 2016 Control 38.6 ± 3.7 N/D 6.6 ± 2.5 N/D Addition of rLH 38.9 ± 2.8 N/D 6.0 ± 3.2 N/D Zhang, 2014 Control 37.4 ± 4.3 1.12 ± 0.84 3.0 ± 1.4 N/D Addition of DHEA 37.2 ± 5.2 1.01 ± 0.77 2.9 ± 1.4 N/D Zhang, 2022 Control 40.2 ± 3.7 1.00 ± 0.80 5.7 ± 3.0 N/D Addition of Estrogen 40.4 ± 4.1 1.00 ± 0.80 5.4 ± 2.9 N/D POR poor ovarian response, BMI body mass index, POR poor ovarian response, rLH recombinant luteinizing hormone, DHEA Dehydroepiandrosterone, AMH anti-Müllerian hormone, IU international unit Main characteristics of studies included in network meta-analysis • Endocrine disorders • Intrauterine disorders • Azoospermia • Severe endometriosis • History of chemotherapy/radiotherapy • ovarian surgery • DHEA/testosterone use • FSH > 20 IU/L, previous ovarian surgery • Non-POR infertility causes • PCOS • Endocrine disorders • Male factor infertility • BMI ≥ 32 kg/m2 • Endometriosis stage III-IV • Ovarian surgery • Endocrine/metabolic disorders • Use of testicular sperm • Endometriosis • History of chemotherapy • Ovarian surgery • Prior DHEA use • POI • Male factor infertility • Severe endometriosis • Thyroid/liver/kidney disease • Abnormal genitalia • Women aged ≥ 41 years • POI • Preimplantation genetic screening • Repeated IVF failure • Severe endometriosis • PCOS • Known medical disease (e.g. severe hypertension or hepatic disease) • History of altered karyotype in one or both partners • History of chronic, autoimmune or metabolic diseases • Presence of endocrinopathies • Male factor infertility • POI • Donor/recipient treatments • Metabolic or endocrine disorders including hyperprolactinemia and hypo/hyperthyroidism • Endometriosis • Body mass index (BMI) > 30 kg/m2 • Azoospermic male partner • Any clinically significant systemic disease, • History of radiotherapy or chemotherapy • Any current history of malignant disease • Pituitary or hypothalamic disease • Current ovarian cyst > 3 cm • Any chronic infectious diseases • PCOS • Unexplained menstrual bleeding • Preimplantation genetic testing • Smokers • Using steroids, DHEA or prednisolone in the last 3 months • Number of previous IVF cycles ≥ 3 • Cycles requiring the use of spermatozoa from MESA-TESE procedures • Presence of endocrine disorders (thyroid, prolactin, etc.) • Presence of endometrioma and any history of surgery on the ovaries • Reluctance to participate in the project • New clinical conditions or a change in a treatment procedure • Sensitivity to testosterone gel • Basal level of FSH at day 3 of menstrual cycle > 20 IU/l) • Increased BMI > 35 kg/m2 • History of endocrine or metabolic disorders • History of ovarian cystectomy or oophorectomy • Severe endometriosis • Severe azoospermia • Non-corrected uterine malformations, endometrial pathology • Severe male factor (motile sperm count  35 kg/m2 • Androgen treatment within the last 3 months • Known allergy to the experimental drug • Male factor • POI • Need of preimplantation genetic testing • Women that failed to obtain clinical pregnancy after three or more IVF/ICSI cycles • History of two or more recurrent pregnancy losses • Diagnosis of uterine abnormalities using hysteroscopy • Diagnosed with hydrosalpinx using hystero salpingography • History of chemotherapy with cytotoxic agents • History of pelvic radiotherapy • Epilepsy; • Treatment with DHEA before study enrolment • Allergy to DHEA • History of ovarian cystectomy or oophorectomy • History of cytotoxic chemotherapy • History of pelvic irradiation • History of taking testosterone or DHEA supplementation • Severe endometriosis • Uncontrolled thyroid disease • Diabetes mellitus • Significant hyperprolactinaemia • Hypogonadotrophic hypogonadism • More than four previous unsuccessful ART attempts • History of ovarian cystectomy or oophorectomy • Diagnosis of endometriosis • History of DHEA supplementation or hormonal replacement therapy • Preimplantation genetic testing cycle • Donor cycle • Family history of thrombosis or a high risk of thrombosis • Previous history of hypertension or hypertension • Hyperlipidemia • Estrogen-dependent breast disease • Cervical biopsy showing cervical intraepithelial neoplasia grade III or above PCOS polycistic ovary syndrome, POI premature ovarian insufficiency, BMI body mass index, FSH follicle-stimulating hormone, rLH recombinant lutenizing hormone, DHEA Dehydroepiandrosterone, MII metaphase II, MESA microsurgical epididymal sperm aspiration, TESE testicular epididymal sperm extraction, AMH anti-Mullerian hormone, BMP −15 bone morphogenetic protein-15, GDF-9 growth differentiation factor-9, E2 estradiol Baseline patient characteristics Addition of Testosterone (4 weeks) Addition of Testosterone (6 weeks) Addition of Testosterone (short) Addition of Testosterone (long) POR poor ovarian response, BMI body mass index, POR poor ovarian response, rLH recombinant luteinizing hormone, DHEA Dehydroepiandrosterone, AMH anti-Müllerian hormone, IU international unit Among the 22 included studies, all were RCTs [ 12 , 13 , 16 , 17 , 89 – 106 ]. Of these, 5 studies [ 13 , 92 , 95 , 103 , 105 ] where from China, 3 studies [ 89 , 97 , 99 ] were from Iran, 2 studies [ 16 , 102 ] were from Turkey, 2 studies were from Greece [ 91 , 100 ], 2 studies were from Egypt [ 90 , 96 ], 1 study was from Denmark [ 94 ], 1 study was from Italy [ 98 ], 1 study was from Spain [ 101 ], 1 study was from Switzerland [ 17 ], 1 study was from Australia [ 12 ], 1 study was from Hong Kong [ 106 ], 1 study was from Israel [ 104 ], and 1 study was from Vietnam [ 93 ]. A total of 4131 infertile patients with a diagnosis of POR according to Bologna criteria [ 23 ] were included in this analysis; of these, 2069 patients underwent OS without hormonal add-ons and 2062 patients received hormonal additions in the context of OS. Two hundred ten patients were given LTZ as a hormonal add-on during the OS [ 16 , 95 , 97 ], 214 patients were given CC during the OS [ 17 , 98 , 100 ], 509 patients were given rLH during the OS [ 94 , 102 , 104 ], 276 patients were pre-treated with estrogen before the OS [ 13 ], 493 patients were pre-treated with DHEA before the OS [ 92 , 103 , 105 , 106 ], 167 patients were pre-treated with testosterone before the OS [ 89 , 91 , 93 , 99 , 101 ], and 200 patients were given GH during the OS [ 12 , 90 , 96 ]. Figure S1 a shows the quality of the methodology used in the 22 included RCTs, and Figure S1 b shows a percentage-based assessment of the methodological quality of these trials. Overall, one RCT [ 90 ] was deemed at high risk of bias, six RCTs [ 89 , 93 , 98 – 101 ] were judged as having “some concerns” and 15 RCTs [ 12 , 13 , 16 , 17 , 92 , 94 – 97 , 102 – 106 ] were considered at low risk, respectively. In detail, nine trials [ 90 – 92 , 95 , 98 – 100 , 105 , 106 ] had some or severe concerns for biases arising from the randomization process (Figure S1 a-b). No study was deemed at high risk for bias due to deviations from intended interventions or missing data. On the contrary, there was a high risk of bias in the measurement of the outcomes in three studies [ 89 , 93 , 101 ]. Conversely, the vast majority of RCTs were at low risk for selection biases (Figure S1 a-b). All the included RCTs [ 12 , 13 , 16 , 17 , 70 , 89 – 105 ] were registered in valid prospective repositories prior to the enrollment of the first woman. Certainty of evidence according to CINeMA criteria ranged from very low to low, principally due to major concerns in the imprecision and incoherence domains, which were related to the low number of direct comparisons available (Supplementary Table  2 ). Potential publication bias was visually displayed in Figure S2 a-g. Eight studies [ 12 , 17 , 91 , 94 , 95 , 98 , 103 , 106 ] reported on the LBR. The following add-ons were directly compared with controls and indirectly compared among them: LTZ, CC, rLH, DHEA, testosterone, and GH. The network map for the outcome is shown in Fig.  2 a . The general analysis for inconsistency revealed no degrees of inconsistency. As outlined in the forest and interval plots (Fig.  2 b, c), there were no significant differences among all the evaluated add-ons (very low evidence) (Supplementary Table  2 ). Fig. 2 Live birth rate. a Network of comparisons of interventions analyzed in included studies. b Forest plot for the outcome. c Interval plot. d Ranking plot according to SUCRA analysis Live birth rate. a Network of comparisons of interventions analyzed in included studies. b Forest plot for the outcome. c Interval plot. d Ranking plot according to SUCRA analysis According to SUCRA ranking of available choices, testosterone (SUCRA = 34.0%) and GH (SUCRA = 23.9%) had the highest probability of being ranked treatment of choice (Fig. 2 d). Publication bias for the LBR was not assessed since less than 10 studies reported the outcome. Nineteen RCTs [ 13 , 16 , 17 , 89 – 99 , 101 – 105 ] estimated the CPR. All the included interventions (LTZ, CC, rLH, estrogen, DHEA, testosterone and GH) were directly and indirectly compared. The network of direct comparisons is shown in Fig.  3 a. No source of inconsistency was notable. The forest and interval plots displayed in Fig.  3 b and c show the results of direct and indirect comparisons for CPR. GH was more efficacious than adding estrogens (OR 3.46 [95% CI 1.59 to 7.53]; low evidence) while adding estrogens was less efficacious than controls (OR 0.49 [95% CI 0.29 to 0.84]; low evidence) and DHEA (OR 0.46 [95% CI 0.24 to 0.89]; very low evidence). Similarly, adding rLH was significantly less efficacious than GH (OR 0.50 [95% CI 0.26 to 0.96]; very low evidence). No significant differences were retrieved among the protocol comparisons (low to very low evidence) (Supplementary Table  2 ) (Fig.  3 c). According to the SUCRA ranking, GH (SUCRA = 46.3%) and testosterone (SUCRA = 44.6%) had increased chances of being ranked first among the hormonal add-ons (Fig. 3 d).Publication bias was not apparent in both Egger’s test ( p  = 0.329) and funnel plot analysis (Figure S2 a). Fig. 3 Clinical pregnancy rate. a Network of comparisons of interventions analyzed in included studies. b Forest plot for the outcome. c Interval plot. d Ranking plot according to SUCRA analysis Clinical pregnancy rate. a Network of comparisons of interventions analyzed in included studies. b Forest plot for the outcome. c Interval plot. d Ranking plot according to SUCRA analysis Regarding the overall mean number of retrieved oocytes, reported in all the 22 studies [ 12 , 13 , 16 , 17 , 89 – 106 ], all the seven hormonal add-ons (LTZ, CC, rLH, estrogen, DHEA, testosterone, and GH) were directly and indirectly compared (Fig.  4 a) without the presence of inconsistency in the general analysis. Forest and interval plots showed that GH was significantly more efficacious than CC (SMD 1.33 [95% CI 0.04 to 2.62]; very low evidence) and controls (SMD 0.93 [95% CI 0.02 to 1.84]; very low evidence) (Fig.  4 b, c) with no other significant differences noted (very low evidence) (Supplementary Table  2 ). SUCRA analysis revealed that GH was likely the treatment of choice for gaining a higher number of retrieved oocytes (SUCRA = 53.2%) (Fig. 4  d).Egger’s test ( p  = 0.010) and funnel plot analysis (Figure S2 e) reported the plausible presence of publication bias, warranting caution in the interpretation of the outcome.The mean number of MII oocytes was available in 17 trials [ 13 , 16 , 89 – 94 , 96 – 98 , 100 – 105 ] in which all the available hormonal add-ons (LTZ, CC, rLH, estrogen, DHEA, testosterone and GH) were compared (Fig.  5 a). There were no degrees of inconsistency to report. Among all the add-on strategies, adding GH was more efficacious than CC (SMD 2.28 [95% CI 0.58 to 3.98]; very low evidence) and controls (SMD 1.46 [95% CI 0.26 to 2.66]; very low evidence). No other relevant differences were notable (very low evidence) (Supplementary Table  2 ) (Fig.  5 b, c). According to the SUCRA ranking plot, GH was the add-on with the highest degree of best treatment (SUCRA = 67.9%) (Fig. 5  d).Both Egger’s test ( p  = 0.110) and funnel plot analysis (Figure S2 f) reported no apparent publication bias. Fig. 4 Mean number of retrieved oocytes. a Network of comparisons of interventions analyzed in included studies. b Forest plot for the outcome. c Interval plot. d Ranking plot according to SUCRA analysis Fig. 5 Mean number of M2 oocytes. a Network of comparisons of interventions analyzed in included studies. b Forest plot for the outcome. c Interval plot. d Ranking plot according to SUCRA analysis Mean number of retrieved oocytes. a Network of comparisons of interventions analyzed in included studies. b Forest plot for the outcome. c Interval plot. d Ranking plot according to SUCRA analysis Mean number of M2 oocytes. a Network of comparisons of interventions analyzed in included studies. b Forest plot for the outcome. c Interval plot. d Ranking plot according to SUCRA analysis The fertilization rate was reported in 14 studies [ 13 , 16 , 70 , 89 – 94 , 96 – 98 , 100 – 105 ] comparing all the hormonal add-ons (LTZ, CC, rLH, estrogen, DHEA, testosterone, and GH) eligible for this network meta-analysis (Figure S3 a). No degrees of inconsistency were retrieved in the general analysis. As shown in forest and interval plots (Figure S3 b-c), adding estrogens was significantly less efficacious than the control group (OR 0.49 [95% CI 0.29 to 0.84]; low evidence) while GH was significantly more efficacious than estrogen too (OR 3.14 [95% CI 1.50 to 6.57]; low evidence). There were no other significant differences between treatments and controls (very low evidence) (Supplementary Table  2 ) (Fig. S3 c). SUCRA analysis revealed that rLH addition (SUCRA = 37.3%), DHEA (SUCRA = 18.8%) and testosterone (SUCRA = 14.8%) had the highest likelihood of being treatments of choice for raising the fertilization rate (Figure S3 d).According to Egger’s test ( p  = 0.376) and funnel plot analysis (Figure S2 g), there were no apparent issues of publication bias. Fifteen studies [ 12 , 13 , 16 , 17 , 90 , 91 , 93 , 95 – 97 , 100 – 103 , 106 ], directly and indirectly comparing all the add-ons, reported the outcome (Figure S4 a). According to forest and interval plots (Figure S4 b-c), there were no significant differences among the approaches (very low evidence) (Supplementary Table  2 ).SUCRA analysis reported that LTZ had increased chances of being the add-on with the shortest OS duration (SUCRA = 52.0%) (Figure  S4 d). No publication bias was reported in Egger’s test ( p  = 0.423) and funnel plot analysis (Figure S2 b). Out of 15 studies [ 12 , 13 , 16 , 17 , 70 , 90 , 91 , 93 – 95 , 97 , 100 , 102 , 103 , 106 ] reporting the mean total dose of gonadotropins used during COS for all the hormonal add-ons (LTZ, CC, rLH, estrogen, DHEA, testosterone and GH) (Figure S5 a), no significant differences were notable (low to very low evidence) (Supplementary Table  2 ) among treatments except for a significantly smaller number of used gonadotropins with LTZ relative to controls (SMD –7.02 [95% CI − 12.82 to − 1.22]; low evidence) (Figures  S5 b-c).According to the SUCRA ranking plot, LTZ (SUCRA = 67.0%) had the greatest degree of best treatment for the outcome (Figure  S5 d). Publication bias was not apparent in both Egger’s test ( p  = 0.339) and funnel plot analysis (Figure S2 c). The outcome was reported in 14 studies [ 13 , 17 , 89 – 93 , 95 – 97 , 102 – 104 , 106 ] in which all the hormone add-ons (LTZ, CC, rLH, estrogen, DHEA, testosterone, and GH) were compared (Figure  S6 a). The consistency analysis showed no source of inconsistency. Figures S6 b and S6 c show the forest and interval plots, in which estrogen levels were significantly less with the use of LTZ relative to CC (SMD − 3.47 [95% CI − 6.85 to − 0.01]; very low evidence), controls (SMD − 2.42 [95% CI − 4.38 to − 0.47]; very low evidence) and GH (SMD − 4.04 [95% CI − 6.80 to − 1.28]; very low evidence). Similarly, testosterone was more efficacious than LTZ (SMD 2.83 [95% CI 0.31 to 5.36]; very low evidence). No other differences were notable (very low evidence) (Supplementary Table  2 ) (Figure  S6 c). According to SUCRA analysis, GH (SUCRA = 46.3%) was the add-on with the highest likelihood of best treatment (Figure  S6 d). According to Egger’s test ( p  = 0.913) and funnel plot analysis (Figure S2 d), no publication bias was apparent. With all the evaluated hormonal add-ons (LTZ, CC, rLH, estrogen, DHEA, testosterone, and GH), eight RCTs [ 13 , 16 , 91 , 94 – 96 , 98 , 101 ] reported the cancellation rate (Figure S7 a). There were zero degrees of inconsistency to report. According to forest and interval plots (Figure  S7 b-c), no significant differences among all the add-ons were notable (very low evidence) (Supplementary Table  2 ). SUCRA analysis revealed that GH (SUCRA = 45.1%) was the most likely best-ranked treatment (Figure  S7 d). Publication bias was not evaluated since less than 10 studies reported the outcome.

The aim of this systematic review and network meta-analysis of RCTs was to evaluate the reproductive outcomes of hormonal add-ons in women with POR undergoing ART. For the LBR, which was our primary outcome, no significant differences were observed between the evaluated interventions; however, testosterone showed the highest probability of improving LBR according to SUCRA analysis. Meanwhile, GH showed the highest SUCRA score for CPR. Moreover, GH also demonstrated better results in terms of total number of retrieved oocytes and mean number of MII oocytes. Finally, LTZ demonstrated benefits in reducing gonadotropin usage and stimulation duration. Several add-ons to OS have been explored in POR women. However, available studies did not ensure their effectiveness. A recent meta-analysis of RCTs concluded that pretreatment with testosterone appears to be related to a better LBR in women with POR [ 21 ]. The authors of the study, however, compared not only hormonal add-ons with controls, but also variations in OS protocols including GnRH agonists or antagonists, different types of gonadotropins, and dual-stimulation regimen. This heterogeneity, combined with the limitations derived from a classic meta-analysis approach, makes it difficult to interpret how their conclusions were derived. In fact, a conventional pairwise meta-analysis, which only compares treatments directly from primary studies, does not allow treatments to be ranked according to their efficacy or safety, offering a more limited basis for clinical decision-making. Because the ideal goal should be to compare available hormonal add-on therapies within each other to achieve a broader and more quantitative view of their comparative effectiveness of, a network meta-analytic approach should be preferred, as performed by Zhang et al. in 2020 [ 22 ]. However, this study is not without strong limitations. First, the reliability of the included studies has not been evaluated and is not adequate to the actual trustworthiness standards [ 107 ]. In addition, although the authors claimed to include only patients defined as POR according to the March 2010 Bologna criteria of the European Society of Human Reproduction and Embryology (ESHRE) [ 23 ], multiple studies published before this date were included, some of which enrolled patients who could not be defined as POR according to them [ 23 ]. This lower homogeneity of the included population consequently significantly reduces the clinical and methodological variability and thus also the reliability and generalizability of the results. Moreover, a crucial strength of our analysis compared to that of Zhang et al. [ 22 ] concerns the primary outcome analyzed. The choice of LBR as the primary outcome, compared to the CPR used by the authors of the previous network meta-analysis, represents a substantial advantage. In fact, the ESHRE explicitly recommends the use of LBR as the primary outcome in clinical trials and systematic reviews conducted with the aim to evaluate the clinical effectiveness of using add-ons in ART treatments, as LBR is considered the most clinically relevant parameter, being directly related to the final success of treatment and reflecting the ultimate goal of couples undertaking reproductive medicine treatment [ 108 ]. Therefore, the use of LBR as the primary endpoint in this network meta-analysis significantly increases its clinical relevance and relevance to the information needs of physicians and patients, clearly distinguishing it from the previous meta-analysis by Zhang et al. [ 22 ]. It is crucial to underline that SUCRA rankings reflect the probability that an intervention is among the best options based on the entire network of evidence. However, a higher SUCRA score does not imply that the intervention is statistically superior to others in direct or indirect comparisons. In our analysis, no statistically significant differences were observed among most pairwise comparisons, which suggests limited certainty in the relative superiority of the treatments. Nevertheless, the consistency of higher SUCRA values for GH and testosterone across multiple outcomes suggests a favorable trend that warrants further investigation. Therefore, SUCRA rankings should be interpreted as hypothesis-generating tools rather than definitive clinical guidance, particularly in this scenario where statistical significance is lacking. Our findings highlight GH and testosterone as the agents with the highest probability of improving reproductive outcomes in women with POR undergoing ART. The exact mechanism by which GH might improve the outcomes of OS remains unclear. However, it is known that GH enhances insulin-like growth factor 1 (IGF-1) activity in the follicle, which plays a critical role in granulosa cell function and follicular maturation [ 109 ]. The studies included in our meta-analysis [ 12 , 18 , 90 ] reported a higher number of oocytes retrieved after the administration of GH in POR women. Nevertheless, the benefit for the LBR could not be clearly demonstrated. A review by Kolibianakis et al. in 2009 [ 110 ] had already suggested a significant increase in the number of retrieved oocytes and an improvement in LBR when GH was added to OS in poor responders. However, with the implementation of Bologna criteria, recent reviews concluded that the effect of GH administration on ovarian response or pregnancy rate is still contradictory and remains unclear [ 21 , 111 ]. A recent Cochrane on this topic also reported similar results [ 112 ]. Conversely, the use of adjuvant GH in OS protocols has an uncertain effect on LBR and the average number of oocytes retrieved in patients expected to be good responders. Therefore, although promising, the use of GH as an add-on in women with POR requires further high-quality studies to confirm its efficacy, given the paucity of trials and their inherent biases and imprecisions. Androgens play a crucial role in folliculogenesis. Specifically, they influence the proliferation of granulosa cells, the number of growing follicles as well as the expression of FSH receptors in the ovary [ 113 ]. Different agents have been explored to increase intraovarian androgen concentration: transdermal testosterone, DHEA, and androgen modulators such as LTZ. Our findings suggest that transdermal testosterone could have a positive impact on the LBR and CPR. However, the available evidence regarding the clinical utility of transdermal testosterone in poor responders shows contradictory results. Some authors reported an increase in pregnancy rates [ 93 , 99 ] and ovarian response [ 89 , 99 ] after testosterone administration. In contrast, well-designed clinical trials did not show differences in their primary outcomes (number of oocytes and pregnancy rate) using testosterone [ 91 , 101 ]. Recently, a conference abstract of a multicenter multinational RCT concluded that testosterone pre-treatment prior to IVF/ICSI does not increase clinical pregnancy rates in poor ovarian responders [ 114 ]. Nevertheless, a recent meta-analysis [ 115 ] concluded that the probability of obtaining more oocytes and clinical pregnancies was significantly increased in women pretreated with transdermal testosterone compared with those who were not, with a lower cancellation rate in the testosterone group. However, available studies lack homogeneity regarding type, dose, and duration of testosterone supplementation. Thus, although it is suggested that the use of testosterone in POR women may improve reproductive outcomes, a consensus on the supplementation protocol needs to be reached. In the current meta-analysis, the use of DHEA has shown no benefit in improving reproductive outcomes, except for a decrease in the cancellation rate. This contrasts with other reviews and meta-analyses suggesting that DHEA could improve oocyte response and pregnancy rates in patients with poor response [ 21 , 22 ]. Another add-on that showed some results in our network meta-analysis was LTZ. LTZ is an aromatase inhibitor that increases intraovarian androgens by inhibiting the conversion of androgens into estrogens. In concordance with other authors [ 21 ], we did not observe a benefit with LTZ in the number of oocytes retrieved or pregnancy rate although we observed a reduction of gonadotropin consumption and duration of stimulation with the use of LTZ. Only a few studies have evaluated the use of this add-on in patients with POR [ 8 , 16 , 97 ]. Authors who have evaluated the use of LTZ in mild stimulation compared to conventional stimulation without LTZ have observed no differences in reproductive outcomes [ 8 , 16 ]. However, Moini et al. [ 97 ] showed that patients treated with conventional doses of gonadotrophins plus LTZ had an increase in the number of oocytes retrieved along with a decrease in gonadotropin consumption and duration of stimulation compared to the women without LTZ addition. LTZ is therefore a promising treatment that requires further exploration. According to our results, neither the use of CC, estrogen priming nor the addition of rLH would gain an additional benefit in patients with POR. While this network meta-analysis was limited to hormonal add-ons, other interventions such as coenzyme Q10, low-dose hCG, and corifollitropin alfa showed promising potential in improving ovarian response. Coenzyme Q10, through its antioxidant and mitochondrial-supportive properties, may enhance oocyte quality in women with POR [ 19 ]. Low-dose hCG, when added during OS, may mimic LH activity and support follicular maturation, although high-quality RCTs in Bologna-defined POR are lacking [ 116 ]. Corifollitropin alfa offers the advantage of simplified stimulation protocols with a single injection replacing daily FSH, and may be useful in selected POR [ 117 ]. However, the current body of evidence is either methodologically limited or inconsistent with our inclusion criteria. Future high-quality RCTs are needed to assess the role of such agents and determine their integration into individualized OS strategies. Several limitations should be considered while interpreting our findings. The certainty of evidence was primarily low due to imprecision and incoherence, mainly stemming from the limited number of direct comparisons available and variations in study methodologies. The dosages, the beginning time, the length of ovulation stimulation, and the GnRH analog protocols employed in the suggested interventions and OS protocols also showed notable differences. Therefore, some small heterogeneity among included trials may have influenced the findings. Due to the small sample sizes of the eligible trials, it was typically impossible to properly investigate this clinical heterogeneity. Consequently, the current systematic review and network meta-analysis permitted only broad comparisons between various hormonal adjuvant treatments and no treatment, lowering the certainty of evidence for imprecision due to the lack of significant differences in several comparisons. Future research will allow for the assessment of a possible moderating influence of these parameters on the effectiveness of the interventions under investigation. Conversely, to ensure the robustness and validity of such findings, we included only studies in which the POR followed the Bologna criteria [ 23 ], excluding many trials that might increase inherent heterogeneity and thus be a strength of this network meta-analysis. Moreover, all the most critical ART outcomes, including also the LBR, were analyzed, emphasizing the importance of our findings in clinical practice. Additionally, the consistency of the reported findings, showing agreement in the top-ranked add-ons (GH and testosterone) for most study outcomes, provides useful indications for routine practice in practitioners dealing with women with POR undergoing ART. In conclusion, the results of the present systematic review and network meta-analysis of RCTs suggest that the addition of testosterone and GH may offer benefits to POR patients undergoing OS. The addition of LTZ also showed some sort of benefit, showing the improvement of some OS parameters. In contrast, the addition of DHEA, CC, estrogen, or rLH did not show significant benefit. However, the current certainty of evidence is still limited to low and very low; therefore, it is crucial to further investigate the real clinical value of these additional hormone treatments with further rigorous studies.

Ovarian reserve refers to the number of oocytes a woman has at a given stage of her life. Poor ovarian response (POR) is due to a decline in the number of oocytes resulting in inadequate ovarian function and reduced fertility [ 1 ]. This condition is typically characterized by decreased levels of anti-Müllerian hormone (AMH), decreased antral follicle number (AFC), and sometimes also elevated follicle-stimulating hormone (FSH) at baseline [ 2 ]. Some pieces of evidence report that the decline in number is also often accompanied by a concomitant decline in oocyte quality [ 3 ]. In fact, it is debated whether the natural decline in oocyte quantity is related to a decrease in oocyte quality and an increased risk of aneuploidies [ 4 ]. In support of this hypothesis, data suggest that women with POR are at higher risk of aneuploid pregnancies and miscarriages [ 5 , 6 ].Therefore, compromised reproductive potential characterizes women with POR undergoing ovarian stimulation (OS) and assisted reproductive technologies (ART) compared with comparably aged counterparts with normal ovarian reserve and OS responsiveness [ 7 ]. However, the ideal stimulation regimen for patients with POR is currently unknown. In fact, several therapeutic strategies have been proposed to manage women with POR, seeking to increase the number of good-quality oocytes collected following OS and to optimize ART outcomes [ 8 – 10 ]. Among these, several stimulation protocols, with or without pre-treatment, and involving the use of various hormonal [ 11 – 15 ], pharmacological [ 16 , 17 ] and non-pharmacological [ 18 – 20 ] add-ons to gonadotropins have been described in numerous randomized controlled trials (RCTs). In fact, a recent meta-analysis of RCTs suggested that pretreatment with testosterone could improve the live birth rate (LBR) in women with (POR) [ 21 ]. However, this conclusion is derived from heterogeneous comparisons that include different treatment protocols, making the interpretation of the results difficult. To date, there is only one analysis that attempts such a multiple comparison to evaluate adjuvant treatments in OS for poor responder women in IVF cycles, but it has important methodological limitations and doubts about the quality and appropriateness of the included studies [ 22 ].Therefore, given these assumptions, to date the superiority of one protocol with one add-on over another has not yet been demonstrated.This systematic review and network meta-analysis aims to explore the superiority of an OS protocol with hormonal add-ons over the same protocol without additions in terms of reproductive and ART outcomes in women with POR according to the Bologna criteria [ 23 ], analyzing currently available randomized controlled trials conducted for this purpose.

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