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Clinical prediction models for in vitro fertilization outcomes: a systematic review, meta-analysis, and external
C H Tian1, L Y Liu1, Y F Huang1
1Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Study Question:
What is the best-performing model currently predicting live birth outcomes for IVF or ICSI?
Summary Answer:
Among the identified prognostic models, McLernon's post-treatment model outperforms other models in both the meta-analysis and external validation of a Chinese cohort.
What Is Known Already:
With numerous similar models available across different time periods and using various predictors in IVF prognostic models, there is a need to summarize and evaluate them, due to a lack of validated evidence distinguishing high-quality from low-quality prediction tools. However, there is a notable dearth of research in the form of meta-analysis or external validation assessing the performance of models in predicting live births in this field.
Study Design, Size, Duration:
The researchers conducted a comprehensive literature review in PubMed, EMBASE, and Web of Science, using keywords related to prognostic models and IVF/ICSI live birth outcomes. The search included studies published up to 3 April 2024, and was limited to English language studies.
Participants/Materials, Setting, Methods:
The review included studies that developed or validated prognostic models for IVF live birth outcomes while providing clear reports on model characteristics. Researchers extracted and analysed the data in accordance with the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and other model-related guidelines. For model effects in meta-analysis, the choice would be based on the heterogeneity assessed using the I2 statistic and the Cochrane Q test. Model performance was evaluated by assessing their area under the receiver operating characteristic curves (AUCs) and calibration plots in the studies.
Main Results And The Role Of Chance:
This review provides a comprehensive summary of data derived from 72 studies with an overall ROB of high or unclear. These studies contained a total of 132 predictors and 86 prognostic models, and then meta-analyses were performed for each of the five selected models. The total random effects of Templeton's, Nelson's, McLernon's pre-treatment and post-treatment model demonstrated AUCs of 0.65 (95% CI: 0.61-0.69), 0.63 (95% CI: 0.63-0.64), 0.67 (95% CI: 0.62-0.71), and 0.73 (95% CI: 0.71-0.75), respectively. The total fixed effects of the intelligent data analysis score (iDAScore) model estimated an AUC of 0.66 (95% CI: 0.63-0.68). The external validation of the initial four models in our cohort produced AUCs ranging from 0.53 to 0.58, and the calibration was confirmed through calibration plots.
Limitations, Reasons For Caution:
While the focus on English-language studies and live birth outcomes may constrain the generalizability of the findings to diverse populations, this approach equips clinicians, who view live births as the ultimate objective, with more precise and actionable reference guidelines.
Wider Implications Of The Findings:
This study represents the first meta-analysis in the field of IVF prediction models, definitively confirming the superior performance of McLernon's post-treatment model. The conclusion is reinforced by independent validation from another perspective. Nevertheless, further investigation is warranted to develop new models and to externally validate existing high-performing models for prognostic accuracy in IVF outcomes.
Study Funding/Competing Interest(S):
This study was supported by the National Natural Science Foundation of China (Grant No. 82174517). The authors report no conflict of interest.
Registration Number:
2022 CRD42022312018.
