COVID Infection after Embryo Transfer

This prospective study looked at the association between outcomes in early pregnancy after fresh embryo transfer and infection with SARS-CoV-2 (aka “COVID-19”).

Study Background

WHAT

• An examination of pregnancy rates and miscarriage rates after becoming infected with COVID-19 in the ten weeks after fresh embryo transfer

WHY

• Limited information has been published on COVID-19 infection and the impact on early pregnancy after IVF/ICSI embryo transfer

WHERE/WHEN

• A public IVF center at Sichuan Jinxin Xinan Women and Children’s Hospital in Southwest China

• Enrollment September - December 2022; followed through March 2023

HOW

• Inclusion Criteria: age 20-39 years, BMI 18-30 kg/m2, COVID-19 infection within ten weeks after fresh embryo transfer from IVF or ICSI (gametes not infected prior to embryo transfer)

• Exclusion Criteria: infection pre-embryo transfer, incomplete info, frozen embryos, “mental disorders/inability to answer questions” (n=5), “other reasons” (n=117)

• Followed infection status via nucleic acid testing at official sites or home antigen test; severity and spectrum of symptoms or recorded

• controlled ovarian stimulation protocol (COS) / IVF/ICSI protocols not explained, referred readers to prior publications

• Embryo transfers

  • Day 1 = one day after embryo transfer; transfers occurred on Day 3 or Day 5

  • 1 or 2 embryos transferred

• Organized patients into three groups post-embryo transfer:

  Group #1) ≤14 days

  Group #2) ≤28 days

  Group #3) ≤10 weeks (= 12 weeks spontaneous conception)

Statistics

• Continuous variables presented as medians (IQR), used Mann-Whitney U Test or Student’s t tests

• Categorial variables expressed as number (n) and percentage (%), used Chi-square test

• Early pregnancy outcomes analyzed from multivariable logistic regression for adjusted odds ratio (aOR) and 95% CI; p < 0.05 considered statistically significant

• “To mitigate the impact of confounding factors and address baseline characteristic imbalances, a multivariate logistic regression model was employed for adjustment” (repeated in results)

Results

• Baseline Characteristics Uneven

  • Group #1: 136 infected vs. 721 uninfected

    • significant differences: type and # of transferred embryos

    • no significant differences in: age, BMI, infertility duration, hormone levels, antral follicle count, vaccination status, vaccination-to-transfer interval, GN, infertility cause, infertility type, COS protocol, fertilization type, number of retrieved oocytes, and number of transferred embryos

  • Group #2: 171 infected vs. 686 uninfected

    • significant differences: fertilization type + type and # transferred embryos

    • no significant differences in: age, BMI, infertility duration, hormone levels, antral follicle count, vaccination status, vaccination-to-transfer interval, GN, infertility cause, infertility type, COS protocol, fertilization type, number of retrieved oocytes, and number of transferred embryos

  • Group #3: 696 infected vs. 161 uninfected

    • significant differences: FSH, AMH, and COS protocols

    • no significant differences in: age, BMI, infertility duration, hormone levels, antral follicle count, vaccination status, vaccination-to-transfer interval, GN, infertility cause, infertility type, COS protocol, fertilization type, number of retrieved oocytes, and number of transferred embryos

• To mitigate the impact of confounding factors and address baseline characteristic imbalances, the authors shifted from single-factor analysis to a multivariate logistic regression model to adjust for confounding factors and address the baseline imbalances (see Table 5 below)—> after adjustments found no statistically significant differences in implantation rates, clinical pregnancy rates, or miscarriage rates between the infected and uninfected

Authors’ Thoughts

• Study strengths: prospective design, inclusion/exclusion criteria, evaluation of infection at three time points

• Study limitations: small sample size led to relatively low power (52.4%) / inability to meet statistical validity, potential for bias in self-reported data, short-term follow-up misses opportunity to study mid and late-term pregnancy outcomes, and single-center

This Pharmacist’s Thoughts

• Unclear which data was self-reported, unclear what is meant by exclusion criteria of “mental disorders/inability to answer questions,” methodology noted assessment of severity but not mentioned again, could have improved on discussion of differences in baseline demographics (i.e. type and # of high-quality embryos transferred),

• No data collected on smoking status or comorbidities

• For an observational study, it was disappointing to see no additional discussion on the differences in patient baseline demographics between infected and uninfected that occurred in all three study time periods

• “Vaccination and embryo transfer in our study exceeded six months in each stage” - due to coincidence or unstated inclusion criteria?

• While forthright in declaring the mediocre power, did they estimate the sample size needed for higher power before the study started?

• Future studies need greater subject variability (wider range of BMIs, race/ethnicity, etc.)

•  They also didn’t note that, despite opening the study up to women with a BMI up to 30, almost all of the women had a BMI considered healthy between 18.5-25, so we don’t know the impact on women with a higher BMI

Conclusions

Value from this study based on relative novelty of study design and currently best research into this specific topic (early pregnancy outcomes in +COVID patients post- fresh embryo transfer). Multi-site studies, with more patients and greater diversity, could easily yield different outcomes.

Resources

Chen X, Shi H, Li C, et al. The effect of SARS-CoV-2 infection on human embryo early development: a multicenter prospective cohort study. Sci China Life Sci. 2023;66(7):1697-1700. doi:10.1007/s11427-023-2291-0

Li XF, Zhang YJ, Yao YL, et al. The association of post-embryo transfer SARS-CoV-2 infection with early pregnancy outcomes in in vitro fertilization: a prospective cohort study. Am J Obstet Gynecol. Published online December 20, 2023. doi:10.1016/j.ajog.2023.12.022

Ranganathan P, Pramesh CS, Aggarwal R. Common pitfalls in statistical analysis: Logistic regression. Perspect Clin Res. 2017;8(3):148-151. doi:10.4103/picr.PICR_87_17

Roberts C, Torgerson DJ. Understanding controlled trials: baseline imbalance in randomised controlled trials. BMJ. 1999;319(7203):185. doi:10.1136/bmj.319.7203.185