Published: 20 December 2024
Human Reproduction Update, dmae032, https://doi.org/10.1093/humupd/dmae032
Nienke Schouten1, Rui Wang2, Helen Torrance1, Theodora Van Tilborg1, Ercan Bastu3, Christina Bergh4, Thomas D’Hooghe5, Jesper Friis Petersen6, Kannamannadiar Jayaprakasan7, Yacoub Khalaf8, Ellen Klinkert9, Antonio La Marca10, Lan Vuong11, Louise Lapensee12, Sarah Lensen13, Åsa Magnusson4, Adolfo Allegra14, Anders Nyboe Andersen15, Simone Oudshoorn1, Biljana Popovic-Todorovic16, Ben Willem Mol2, Marinus Eijkemans1, and Frank Broekmans1
Authors information
1Division Woman and Baby, Reproductive Medicine, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
2Department of Obstetrics and Gynaecology, Monash Medical Centre, Monash University, Clayton, VIC, Australia
3Acibadem University Faculty of Medicine, Department of Obstetrics and Gynecology, Istanbul University School of Medicine, Istanbul, Turkey
4Department of Obstetrics and Gynaecology, Institute of Clinical Sciences, Sahlgrenska Academy, Sahlgrenska University Hospital, Gothenburg University, Gothenburg, Sweden
5Global Medical Affairs, Research and Development, Merck Healthcare KGaA, Darmstadt, Germany
6Department of Obstetrics and Gynecology, North Zealand Hospital, Hilleroed, Denmark
7Royal Derby Hospital, Derby & University of Nottingham, Nottingham, UK
8Assisted Conception Unit, Guy’s & St Thomas’s Hospital, London, UK
9Department of Obstetrics & Gynaecology, University Medical Center Groningen, Groningen, The Netherlands
10Department of Obstetrics Gynaecology and Paediatric Sciences, University of Modena and Reggio Emilia, Modena, Italy
11Department of Obstetrics and Gynecology, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
12Reproductive Endocrinology and Infertility, Clinique ovo, Montr� eal, Canada
13Department of Obstetrics and Gynaecology, Royal Women’s Hospital, University of Melbourne, Melbourne, Victoria, Australia
14Reproductive Medicine Unit, ANDROS Day Surgery Clinic, Palermo, Italy
15The Fertility Department, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
16Center for Reproductive Medicine, Universitair Ziekenhuis Brussel, Brussels, Belgium
Abstract
BACKGROUND
The ovarian response to gonadotropin stimulation varies widely among women, and could impact the probability of live birth as well as treatment risks. Many studies have evaluated the impact of different gonadotropin starting doses, mainly based on predictive variables like ovarian reserve tests (ORT) including anti-Müllerian hormone (AMH), antral follicle count (AFC), and basal follicle-stimulating hormone (bFSH). A Cochrane systematic review revealed that individualizing the gonadotropin starting
dose does not affect efficacy in terms of ongoing pregnancy/live birth rates, but may reduce treatment risks such as the development of ovarian hyperstimulation syndrome (OHSS). An individual patient data meta-analysis (IPD-MA) offers a unique opportunity to develop and validate a universal prediction model to help choose the optimal gonadotropin starting dose to minimize treatment risks without affecting efficacy.
OBJECTIVE AND RATIONALE
The objective of this IPD-MA is to develop and validate a gonadotropin dose-selection model to guide the choice of a gonadotropin starting dose in IVF/ICSI, with the purpose of minimizing treatment risks without compromising live birth rates.
SEARCH METHODS
Electronic databases including MEDLINE, EMBASE, and CRSO were searched to identify eligible studies. The last search was performed on 13 July 2022. Randomized controlled trials (RCTs) were included if they compared different doses of gonadotropins in women undergoing IVF/ICSI, presented at least one type of ORT, and reported on live birth or ongoing pregnancy. Authors of eligible studies were contacted to share their individual participant data (IPD). IPD and information within publications were used to determine the risk of bias. Generalized linear mixed multilevel models were applied for predictor selection and model development.
OUTCOMES
A total of 14 RCTs with data of 3455 participants were included. After extensive modeling, women aged 39 years and over were excluded, which resulted in the definitive inclusion of 2907 women. The optimal prediction model for live birth included six predictors: age, gonadotropin starting dose, body mass index, AFC, IVF/ICSI, and AMH. This model had an area under the curve (AUC) of 0.557 (95% confidence interval (CI) from 0.536 to 0.577). The clinically feasible live birth model included age, starting dose, and AMH and had an AUC of 0.554 (95% CI from 0.530 to 0.578). Two models were selected as the optimal model for combined treatment risk, as their performance was equal. One included age, starting dose, AMH, and bFSH; the other also included gonadotropin-releasing hormone (GnRH) analog. The AUCs for both models were 0.769 (95% CI from 0.729 to 0.809). The clinically feasible model for combined treatment risk included age, starting dose, AMH, and GnRH analog, and had an AUC of 0.748 (95% CI from 0.709 to 0.787).
WIDER IMPLICATIONS
The aim of this study was to create a model including patient characteristics whereby gonadotropin starting dose was predictive of both live birth and treatment risks. The model performed poorly on predicting live birth by modifying the FSH starting dose. On the contrary, predicting treatment risks in terms of OHSS occurrence and management by modifying the gonadotropin starting dose was adequate. This dose-selection model, consisting of easily obtainable patient characteristics, aids in the choice of the optimal gonadotropin starting dose for each individual patient to lower treatment risks and potentially reduce treatment costs.
Keywords: Ovarian stimulation, individualized dosing, gonadotropin starting dose, IPD-MA, prediction model