Abstract
Background
While in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) have been widely used for infertility treatment, concerns still persist towards the potential risks of congenital malformations and adverse neonatal outcomes. In this work, we estimate the risk of congenital malformations and neonatal outcomes among singletons and twins born after IVF or ICSI.
Methods
The PubMed, Cochrane Library, Scopus, Embase, and Google Scholar databases were systematically searched to identify eligible studies. A Bayesian network meta-regression model was used to synthesize direct and indirect evidence. The natural logarithm of the hazard ratio (lnHR) and its standard error (SElnHR) were calculated to obtain pooled hazard ratios (HRs) with 95% confidence intervals (CIs), adjusting for the number of fetuses. The primary outcome was congenital malformations (CM). All analyses were performed using RStudio version 4.3.1.
Results
A total of 24 studies were included in the Bayesian network meta-regression, comprising 105,152 IVF, 254,538 ICSI, and 4,048,050 spontaneous conception (SC) pregnancies. Compared with SC, singletons conceived by IVF and ICSI showed higher risks of CM (HR 1.32, 95% CI 1.19–1.48; HR 1.47, 95% CI 1.35–1.62). For circulatory system malformations, singletons conceived by ICSI had a higher risk than those conceived by SC (HR 1.47, 95% CI 1.19–1.87) and IVF (HR 1.41, 95% CI 1.04–1.93). Among twins, ICSI was associated with increased risks of CM (HR 1.17, 95% CI 1.05–1.32), preterm birth (PTB) (HR 1.39, 95% CI 1.04–1.98), and perinatal death (HR 1.61, 95% CI 1.12–2.45). Singletons conceived by IVF also showed higher risks of PTB (HR 1.54, 95% CI 1.10–2.22) and low birth weight (LBW) (HR 1.59, 95% CI 1.13–2.21).
Conclusions
Both IVF and ICSI were associated with higher risks of congenital malformations in singletons, while only ICSI was associated with elevated risks in twins. IVF was associated with increased risks of preterm birth and low birth weight in singletons, whereas ICSI was associated with greater risks of circulatory malformations in singletons and preterm birth and perinatal death in twins. These findings may assist clinicians in counseling ART patients about potential risks and underscore the importance of vigilant perinatal monitoring, rather than serving as direct clinical recommendations.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12884-026-09258-z.
Keywords: Assisted reproductive technology, Congenital malformations, Adverse pregnancy outcomes, Fetuses, Network meta-regression
Introduction
According to authoritative institutes, infertility affects 8%-12% of childbearing couples worldwide [1]. In America, approximately 12.7% of women at reproductive age urge remedies owing to infertility each year [2]. Moreover, in certain regions including Central Asia, South Asia, North Africa/Mid-East, Sub-Saharan Africa, infertility prevalence ranges from 15% to 25%, exceeding the global average level [3–5]. Lack of accessible or affordable technology, regions with high infertility rates confront complicated challenges [4].
Infertility negatively impacts individuals, families, and society by different means: a strong sense of social stigmatization and psychological distress on individuals; economic constraints as well as lower cohesion and adaptability on families—being divorced or forsaken from males in polygamous marriage; and increasing age-standardized disability-adjusted life-years (DALYs), aggravating aging further, and greater disease burdens on society [6–9]. Therefore, handling infertility is imminent.
It is ecstatic that in vitro fertilization and embryo transfer (IVF-ET) was available for the first time in 1978 [10], shedding light on fertilization. Since then, several methods have been blooming: intrafallopian transfer (GIFT) and zygote intrafallopian transfer (ZIFT) in the 1980s [11], preimplantation genetic diagnosis (PGD) in 1990 [12], intracytoplasmic sperm injection (ICSI) in 1992 [13]. Until 2018, more than 10 million children have been born by artificial reproductive technology (ART) and delivery rates are still rising [14].
Although there were several literature investigating neonatal outcomes, meticulous studies in terms of direct and indirect comparisons according to number of fetuses, namely singletons or twins, are still modicum [15]. For instance, despite great effort by Qin [16] in his contribution, they failed to adjust number of fetuses when exploring the risk of congenital malformations (CM) implementing ART, compromising its extent to clinicals.
Hence, we conducted this Bayesian network meta-regression analysis adjusting for fetal numbers to clarify the risks of CM and other neonatal outcomes among patients receiving IVF, ICSI, and spontaneously conception (SC), aiming to provide more pragmatic evidence for clinical practice.
Methods
Search strategy
This meta-analysis was guided by the PRISMA guideline (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). We searched PubMed, Cochrane Library, Scopus, Embase, and Google Scholar with MeSH terms on February 21, 2024. The detailed search strategy is provided in Supplementary Table 1.
Study screening, data extraction, and quality assessment
Two investigators (JYL and DJY) independently conducted the study retrieval, screening, data extraction, and quality assessment. The first author, year, study period, country, total number (N) ART (% birth defect) and total number of non-ART (% birth defect), type of ART (number of patients), plurality, defects assessed, age/time assessment, adjusted, matched or crude data, quality score were extracted respectively. The quality of each study was assessed using the Newcastle-Ottawa-Scale (NOS) [17]. Any discrepancy was adjudicated by two senior investigators (LXL and HZ).
Selection criteria
The inclusion criteria were as follows:
Research was published in English;
The study design was a clinical controlled study;
The study assessed singletons or twins through ART or SC;
The treatment was IVF or ICSI;
Outcomes included CM;
The study provided relative risk (RR) or odds ratio (OR) with a corresponding 95% confidence interval (CI) from multi-variant logistic regression.
The exclusion criteria were as follows:
Cross-sectional study, animal experiment, case report, review, systematic review, meta-analysis, editorial, comment, letter, conference proceeding;
Duplicate cohort;
One group having more than two ART methods (for example, IVF with or without ICSI was included in IVF);
Patients undergoing ovulation induction (OI) or intrauterine insemination (IUI) were classified within the spontaneous conception (SC) group. Although IUI is an assisted reproductive technique, fertilization occurs in vivo without embryo culture or micromanipulation. This classification follows previous registry-based studies to maintain methodological consistency and minimize heterogeneity.
Endpoints of interest
The primary endpoint was congenital malformations (CM) defined according to ICD-9, ICD-10, or EUROCAT. Specific categories included malformations of the eye/ear/face/neck, respiratory, musculoskeletal, urogenital, digestive, circulatory, and central nervous systems.
The secondary endpoints included stillbirth, neonatal death (within 28 days), perinatal death, preterm birth (PTB; <37 weeks), very preterm birth (VPTB; <32 weeks), small for gestational age (SGA), admission to NICU, Apgar ≤ 7 at 5 min, respiratory distress syndrome (RDS), intracranial hemorrhage, assisted ventilation, low birth weight (LBW; <2500 g), and very low birth weight (VLBW; <1500 g).
Statistical analysis
Given that most included studies were prospective cohort designs reporting event incidence over time (e.g., congenital malformations, preterm birth, perinatal death), hazard ratios (HRs) were used as the common effect measure to account for the temporal nature of risk estimation. The natural logarithm of the hazard ratio (lnHR) and its standard error (SElnHR) were extracted or calculated for each study. When only odds ratios (ORs) or relative risks (RRs) were reported, they were converted to HRs using established statistical transformations to ensure consistency across studies.
These data were entered into RStudio version 4.3.1 using the gemtc package to perform a Bayesian network meta-analysis and network meta-regression, adjusting for the number of fetuses. To ensure transparency, reproducibility, and validity, prior distributions were explicitly defined and justified. Non-informative (weakly informative) priors were specified to minimize prior influence while maintaining numerical stability. Treatment effect priors followed a normal distribution with mean 0 and variance 10,000 [Normal(0, 10,000)], representing a neutral assumption of no prior effect. Between-study standard deviations were assigned uniform priors with a range of 0 to 5 [Uniform(0, 5)], reflecting plausible variability in clinical effect sizes. These priors were selected in accordance with methodological recommendations for Bayesian network meta-analyses [18, 19] and correspond to the default settings of the gemtc package.
Monte Carlo Markov Chain (MCMC) simulation was applied to obtain posterior distributions, with 20,000 burn-ins and 300,000 iterations across four chains and a thinning interval of 10 for each outcome. Statistical heterogeneity was quantified using the I² statistic, with values of approximately 25%, 50%, and 75% interpreted as low, moderate, and high heterogeneity, respectively. A p-value < 0.10 indicated statistically significant heterogeneity. Consistency between direct and indirect comparisons was evaluated using the node-splitting method, where p < 0.05 was considered evidence of inconsistency. The main outputs of the Bayesian network meta-analysis included pooled HRs with corresponding 95% CrIs, Surface Under the Cumulative Ranking Curve (SUCRA) values to determine relative ranking probabilities, and league tables summarizing pairwise comparisons among interventions. These outputs were presented to quantify comparative risks and rank each treatment’s probability of being the safest or riskiest option, ensuring transparent and reproducible interpretation of results. To evaluate the robustness of the pooled estimates, we conducted multiple sensitivity analyses rather than excluding studies outright. These included repeating the analyses after removing studies with lower methodological quality (NOS ≤ 6) or extreme effect sizes and comparing the results under fixed- and random-effects models. Stable findings across these sensitivity analyses indicated the robustness and reliability of the results. Model convergence was assessed using trace plots, density plots, and Brooks–Gelman–Rubin (BGR) diagnostics, where a potential scale reduction factor (PSRF, equivalent to R-hat) close to 1.00 indicated satisfactory convergence. Consistent with Bayesian diagnostic standards, convergence was considered acceptable when PSRF values were below 1.05 [20]. Across all model parameters, PSRF values ranged from 1.00 to 1.03, confirming stable convergence and well-mixed Markov chains. All tests were two-sided, and statistical significance (α) was set at 0.05.
Results
Characteristics of included studies
The search yielded 3,515 citations, and 24 studies were included in the final analysis (Fig. 1). All studies clearly defined CM and provided data adjusted or matched for at least maternal age and parity. A total of 105,152 IVF, 254,538 ICSI, and 4,048,050 SC pregnancies were analyzed, including 243,396 singletons and 116,397 twins from studies published between 1990 and 2016. Detailed study characteristics are presented in Supplementary Table 1. NOS scores ranged from 6 to 9, with 19 studies scoring ≥ 7 (Supplementary Table 2). Network plots are shown in Fig. 2.
Fig. 1.
PRISMA flowchart of the included articles
Fig. 2.
Network graphs of pairwise comparisons of regimens on each end-point before and after NMR. a Apgar ≤ 7 at 5 min; b eye, ear, face and neck malformations; c major malformations; d musculoskeletal system malformations; e perinatal death; f respiratory distress syndrom; g respiratory system malformations; h stillbirth; i convulsions; j all malformations; k assisted ventilation; l gestational age < 32 weeks; m intracranial hemorrhage; n urogenital system malformations; o birth weight < 1500 g; p birth weight < 2500 g; q neonatal death (0–27 days); r circulatory system malformations; s tranferring to NICU; t preterm birth; u extremely preterm birth. Abbreviation: ICSI=intracytoplasmic sperm injection, IVF=in vitro fertilization, NMR=networkmeta-regression, NICU=Neonatal Intensive Care Unit
Primary endpoints
All malformations
In raw data analysis, compared with SC, IVF and ICSI showed significantly higher risk (HR 1.25, 95% CI 1.14–1.38; HR 1.36, 95% CI 1.24–1.48) on all congenital malformations. However, there was no significant difference between IVF and ICSI (Table 1). After network meta-regression, IVF and ICSI remained significantly associated with higher risks in singletons (HR 1.32, 95% CI 1.19–1.48; HR 1.47, 95% CI 1.35–1.62), and ICSI showed a higher risk in twins (HR 1.17, 95% CI 1.05–1.32) compared with SC. No significant difference was observed between IVF and ICSI in either singletons or twins (Tables 2 and 3).
Table 1.
Matrix of endpoints among infants in raw data analysis
| Endpoints | Pairwise comparisons | Hazard ratios (HR) and 95% CIs |
|---|---|---|
| Primary endpoints | ||
| All malformations | IVF vs. control | 1.245 (1.137, 1.38) |
| ICSI vs.control | 1.355 (1.24, 1.484) | |
| IVF vs. ICSI | 0.918 (0.836, 1.025) | |
| Major malformations | IVF vs. control | 1.274 (0.862, 2.071) |
| ICSI vs.control | 1.734 (0.912, 3.615) | |
| IVF vs. ICSI | 0.735 (0.327, 1.665) | |
| Circulatory system | IVF vs. control | 1.048 (0.866, 1.275) |
| ICSI vs.control | 1.455 (1.201, 1.781) | |
| IVF vs. ICSI | 0.72 (0.563, 0.918) | |
| Urogenital system | IVF vs. control | 1.985 (0.883, 4.449) |
| ICSI vs.control | 2.591 (0.971, 6.341) | |
| IVF vs. ICSI | 0.765 (0.281, 2.259) | |
| Musculoskeletal system | IVF vs. control | 1.525 (0.705, 3.172) |
| ICSI vs.control | 2.11 (0.964, 4.696) | |
| IVF vs. ICSI | 0.721 (0.285, 1.733) | |
| Eye/ear/face/neck | IVF vs. control | 1.466 (0.574, 3.594) |
| ICSI vs.control | 1.211 (0.233, 6.241) | |
| IVF vs. ICSI | 1.205 (0.222, 6.579) | |
| Respiratory system | IVF vs. control | 0.735 (0.066, 8.146) |
| ICSI vs.control | 1.2 (0.144, 9.967) | |
| IVF vs. ICSI | 0.613 (0.199, 1.879) | |
| Secondary endpoints | ||
| Perinatal death | IVF vs. control | 1.224 (0.905, 1.671) |
| ICSI vs.control | 1.49 (1.143, 2.039) | |
| IVF vs. ICSI | 0.824 (0.627, 1.017) | |
| LBW | IVF vs. control | 1.121 (0.941, 1.41) |
| ICSI vs.control | 1.081 (0.905, 1.308) | |
| IVF vs. ICSI | 1.037 (0.87, 1.289) | |
| VLBW | IVF vs. control | 1.13 (0.832, 1.575) |
| ICSI vs.control | 1.075 (0.83, 1.467) | |
| IVF vs. ICSI | 1.052 (0.746, 1.434) | |
| PTB | IVF vs. control | 1.458 (1.158, 1.918) |
| ICSI vs.control | 1.351 (1.121, 1.672) | |
| IVF vs. ICSI | 1.079 (0.875, 1.35) | |
| VPTB | IVF vs. control | 1.171 (0.803, 1.669) |
| ICSI vs.control | 1.06 (0.795, 1.497) | |
| IVF vs. ICSI | 1.103 (0.73, 1.545) | |
| Transferring to NICU | IVF vs. control | 1.204 (0.957, 1.774) |
| ICSI vs.control | 1.011 (0.835, 1.319) | |
| IVF vs. ICSI | 1.188 (0.967, 1.631) | |
| RDS | IVF vs. control | 0.965 (0.749, 1.418) |
| ICSI vs.control | 0.913 (0.681, 1.24) | |
| IVF vs. ICSI | 1.057 (0.792, 1.58) | |
| Apgar ≤ 7 at 5 min | IVF vs. control | 0.897 (0.747, 1.082) |
| ICSI vs.control | NA | |
| IVF vs. ICSI | NA | |
| Assisted ventilation | IVF vs. control | 1.006 (0.658, 1.617) |
| ICSI vs.control | 0.916 (0.69, 1.229) | |
| IVF vs. ICSI | 1.099 (0.796, 1.575) | |
| Convulsions | IVF vs. control | 0.691 (0.211, 2.279) |
| ICSI vs.control | 0.752 (0.348, 1.65) | |
| IVF vs. ICSI | 0.92 (0.376, 2.248) | |
| Intracranial hemorrhage | IVF vs. control | 0.958 (0.412, 2.297) |
| ICSI vs.control | 0.652 (0.364, 1.142) | |
| IVF vs. ICSI | 1.466 (0.791, 2.865) | |
| SGA | IVF vs. control | 1.042 (0.847, 1.44) |
| ICSI vs.control | 0.964 (0.559, 1.499) | |
| IVF vs. ICSI | 1.09 (0.678, 2.105) | |
| Stillbirth | IVF vs. control | 1.294 (0.754, 2.295) |
| ICSI vs.control | 1.583 (0.992, 2.81) | |
| IVF vs. ICSI | 0.821 (0.522, 1.169) | |
| Neonatal death | IVF vs. control | 1.036 (0.778, 1.444) |
| ICSI vs.control | 1.151 (0.803, 1.705) | |
| IVF vs. ICSI | 0.9 (0.662, 1.24) | |
Abbreviation: LBW Low birth weight, VLBW Very low birth weight, PTB Preterm birth, VPTB Very low preterm birth, NICU Neonatal, Intensive Care Unit, RDS Respiratory Distress Syndrome, SGA Small for gestational age, NA Not available
Table 2.
Matrix of endpoints in singletons after Bayesian network meta-regression analysis
| Endpoints | Pairwise comparisons | Hazard ratios (HR) and 95% CIs |
|---|---|---|
| Primary endpoints | ||
| All malformations | IVF vs. control | 1.316 (1.191, 1.481) |
| ICSI vs.control | 1.468 (1.345, 1.619) | |
| IVF vs. ICSI | 0.896 (0.811, 1.003) | |
| Major malformations | IVF vs. control | 1.234 (0.713, 2.264) |
| ICSI vs.control | 1.749 (0.84, 3.96) | |
| IVF vs. ICSI | 0.707 (0.268, 1.841) | |
| Circulatory system | IVF vs. control | 1.047 (0.812, 1.353) |
| ICSI vs.control | 1.474 (1.192, 1.874) | |
| IVF vs. ICSI | 0.71 (0.518, 0.959) | |
| Urogenital system | IVF vs. control | NA |
| ICSI vs.control | NA | |
| IVF vs. ICSI | NA | |
| Musculoskeletal system | IVF vs. control | NA |
| ICSI vs.control | NA | |
| IVF vs. ICSI | NA | |
| Eye/ear/face/neck | IVF vs. control | 1.512 (0.582, 3.806) |
| ICSI vs.control | 1.009 (0.156, 6.126) | |
| IVF vs. ICSI | 1.485 (0.22, 11.087) | |
| Respiratory system | IVF vs. control | 0.641 (0.048, 8.399) |
| ICSI vs.control | 1.212 (0.142, 10.332) | |
| IVF vs. ICSI | 0.53 (0.126, 2.179) | |
| Secondary endpoints | ||
| Perinatal death | IVF vs. control | 1.146 (0.797, 1.713) |
| ICSI vs.control | 1.429 (0.962, 2.223) | |
| IVF vs. ICSI | 0.802 (0.562, 1.132) | |
| LBW | IVF vs. control | 1.586 (1.126, 2.214) |
| ICSI vs.control | 1.299 (0.96, 1.727) | |
| IVF vs. ICSI | 1.221 (0.944, 1.592) | |
| VLBW | IVF vs. control | 1.334 (0.643, 2.734) |
| ICSI vs.control | 1.252 (0.642, 2.419) | |
| IVF vs. ICSI | 1.065 (0.541, 2.094) | |
| PTB | IVF vs. control | 1.541 (1.104, 2.216) |
| ICSI vs.control | 1.264 (0.884, 1.75) | |
| IVF vs. ICSI | 1.218 (0.857, 1.848) | |
| VPTB | IVF vs. control | 1.096 (0.597, 2.043) |
| ICSI vs.control | 1.013 (0.562, 1.866) | |
| IVF vs. ICSI | 1.084 (0.57, 2.051) | |
| Transferring to NICU | IVF vs. control | 1.212 (0.615, 2.562) |
| ICSI vs.control | 1.055 (0.633, 1.834) | |
| IVF vs. ICSI | 1.149 (0.659, 2.111) | |
| RDS | IVF vs. control | 0.975 (0.616, 1.769) |
| ICSI vs.control | 0.772 (0.445, 1.382) | |
| IVF vs. ICSI | 1.269 (0.706, 2.461) | |
| Apgar ≤ 7 at 5 min | IVF vs. control | NA |
| ICSI vs.control | NA | |
| IVF vs. ICSI | NA | |
| Assisted ventilation | IVF vs. control | 1.148 (0.647, 2.14) |
| ICSI vs.control | 0.899 (0.59, 1.38) | |
| IVF vs. ICSI | 1.282 (0.796, 2.094) | |
| Convulsions | IVF vs. control | 0.763 (0.199, 3.15) |
| ICSI vs.control | 0.664 (0.235, 1.767) | |
| IVF vs. ICSI | 1.164 (0.394, 3.578) | |
| Intracranial hemorrhage | IVF vs. control | 1.107 (0.345, 3.697) |
| ICSI vs.control | 0.637 (0.268, 1.491) | |
| IVF vs. ICSI | 1.746 (0.662, 4.682) | |
| SGA | IVF vs. control | 1.106 (0.821, 1.685) |
| ICSI vs.control | 1.138 (0.623, 1.926) | |
| IVF vs. ICSI | 0.979 (0.548, 2.115) | |
| Stillbirth | IVF vs. control | 1.218 (0.621, 2.63) |
| ICSI vs.control | 1.516 (0.752, 3.384) | |
| IVF vs. ICSI | 0.806 (0.424, 1.503) | |
| Neonatal death | IVF vs. control | 1.238 (0.814, 1.84) |
| ICSI vs.control | 1.453 (0.845, 2.42) | |
| IVF vs. ICSI | 0.85 (0.554, 1.335) | |
Abbreviation: LBW Low birth weight, VLBW Very low birth weight, PTB Preterm birth, VPTB Very low preterm birth, NICU Neonatal Intensive Care Unit, RDS Respiratory Distress Syndrome, SGA Small for gestational age, NA Not available
Table 3.
Matrix of endpoints in twins after Bayesian network meta-regression analysis
| Endpoints | Pairwise comparisons | Hazard ratios (HR) and 95% CIs |
|---|---|---|
| Primary endpoints | ||
| All malformations | IVF vs. control | 1.096 (0.992, 1.264) |
| ICSI vs.control | 1.174 (1.048, 1.324) | |
| IVF vs. ICSI | 0.933 (0.836, 1.088) | |
| Major malformations | IVF vs. control | 1.438 (0.707, 3.107) |
| ICSI vs.control | 1.799 (0, 74868456.653) | |
| IVF vs. ICSI | 0.807 (0, 806799.529) | |
| Circulatory system | IVF vs. control | 1.018 (0.73, 1.421) |
| ICSI vs.control | 1.223 (0.642, 2.253) | |
| IVF vs. ICSI | 0.836 (0.472, 1.501) | |
| Urogenital system | IVF vs. control | NA |
| ICSI vs.control | NA | |
| IVF vs. ICSI | NA | |
| Musculoskeletal system | IVF vs. control | NA |
| ICSI vs.control | NA | |
| IVF vs. ICSI | NA | |
| Eye/ear/face/neck | IVF vs. control | 1.272 (0.12, 6.696) |
| ICSI vs.control | 1.249 (0.146, 12.254) | |
| IVF vs. ICSI | 0.943 (0.109, 7.025) | |
| Respiratory system | IVF vs. control | 0.75 (0.044, 12.717) |
| ICSI vs.control | 1.054 (0.062, 15.688) | |
| IVF vs. ICSI | 0.716 (0.177, 2.995) | |
| Secondary endpoints | ||
| Perinatal death | IVF vs. control | 1.4 (0.815, 2.29) |
| ICSI vs.control | 1.614 (1.117, 2.453) | |
| IVF vs. ICSI | 0.873 (0.558, 1.185) | |
| LBW | IVF vs. control | 1.035 (0.864, 1.239) |
| ICSI vs.control | 1.063 (0.89, 1.253) | |
| IVF vs. ICSI | 0.973 (0.808, 1.183) | |
| VLBW | IVF vs. control | 1.094 (0.664, 1.732) |
| ICSI vs.control | 1.05 (0.717, 1.649) | |
| IVF vs. ICSI | 1.043 (0.58, 1.674) | |
| PTB | IVF vs. control | 1.391 (0.899, 2.253) |
| ICSI vs.control | 1.394 (1.044, 1.975) | |
| IVF vs. ICSI | 1 (0.698, 1.401) | |
| VPTB | IVF vs. control | 1.189 (0.701, 2.009) |
| ICSI vs.control | 1.087 (0.753, 1.706) | |
| IVF vs. ICSI | 1.094 (0.604, 1.819) | |
| Transferring to NICU | IVF vs. control | 1.286 (0.882, 2.208) |
| ICSI vs.control | 1.002 (0.703, 1.54) | |
| IVF vs. ICSI | 1.273 (0.847, 2.181) | |
| RDS | IVF vs. control | 0.988 (0.682, 1.693) |
| ICSI vs.control | 0.976 (0.659, 1.531) | |
| IVF vs. ICSI | 1.011 (0.659, 1.78) | |
| Apgar ≤ 7 at 5 min | IVF vs. control | NA |
| ICSI vs.control | NA | |
| IVF vs. ICSI | NA | |
| Assisted ventilation | IVF vs. control | 0.936 (0.58, 1.574) |
| ICSI vs.control | 0.923 (0.683, 1.271) | |
| IVF vs. ICSI | 1.014 (0.684, 1.542) | |
| Convulsions | IVF vs. control | 0.574 (0.148, 2.142) |
| ICSI vs.control | 0.801 (0.352, 1.847) | |
| IVF vs. ICSI | 0.717 (0.226, 2.161) | |
| Intracranial hemorrhage | IVF vs. control | 0.899 (0.339, 2.386) |
| ICSI vs.control | 0.657 (0.345, 1.224) | |
| IVF vs. ICSI | 1.366 (0.639, 3.072) | |
| SGA | IVF vs. control | 0.994 (0.769, 1.438) |
| ICSI vs.control | 0.677 (0.323, 1.362) | |
| IVF vs. ICSI | 1.489 (0.716, 3.315) | |
| Stillbirth | IVF vs. control | 1.483 (0.572, 3.488) |
| ICSI vs.control | 1.77 (0.899, 3.716) | |
| IVF vs. ICSI | 0.847 (0.392, 1.475) | |
| Neonatal death | IVF vs. control | 0.892 (0.636, 1.395) |
| ICSI vs.control | 0.968 (0.646, 1.593) | |
| IVF vs. ICSI | 0.922 (0.622, 1.37) | |
Abbreviation: LBW Low birth weight, VLBW Very low birth weight, PTB Preterm birth, VPTB Very low preterm birth, NICU Neonatal Intensive Care Unit, RDS Respiratory Distress Syndrome, SGA Small for gestational age, NA Not available
Circulatory system malformations
In raw data analysis, ICSI demonstrated a significantly higher risk (HR 1.46, 95% CI 1.20–1.78) compared with SC and a notably higher risk (HR 1.39, 95% CI 1.09–1.78) compared with IVF (Table 1). After network meta-regression, in singletons, ICSI showed a significantly higher risk (HR 1.47, 95% CI 1.19–1.87) compared with SC and a notably higher risk (HR 1.41, 95% CI 1.04–1.93) compared with IVF (Tables 2 and 3).
Other outcomes
Major malformations and specific CM (e.g., eye/ear/face/neck, respiratory, urogenital, musculoskeletal) showed no significant differences in either raw data or network meta-regression. Data for digestive and central nervous system malformations were insufficient for network analysis.
Secondary endpoints
PTB
In the raw data analysis, compared with SC, both IVF and ICSI showed significantly higher risks (HR 1.46, 95% CI 1.16–1.92; HR 1.35, 95% CI 1.12–1.67), with no difference between IVF and ICSI (Table 1). After network meta-regression, IVF showed a significantly higher risk in singletons (HR 1.54, 95% CI 1.10–2.22) compared with SC, while ICSI was associated with a higher risk in twins (HR 1.39, 95% CI 1.04–1.98).
LBW
In raw data analysis, there was no difference between IVF, ICSI, and SC (Table 1). After network meta-regression, IVF was associated with a higher risk of LBW in singletons (HR 1.59, 95% CI 1.13–2.21), whereas no significant difference was found between IVF and ICSI in either singletons or twins (Table 2 and Table 3).
Perinatal death
In raw data analysis, ICSI was associated with a higher risk (HR 1.49, 95% CI 1.14-2.04) compared with SC (Table 1). After network meta-regression, ICSI remained significantly associated with perinatal death in twins (HR 1.61, 95% CI 1.12-2.45), with no difference between IVF and ICSI (Table 2 and Table 3).
Other outcomes
Other secondary outcomes including stillbirth, neonatal death (0-27days), VPTB, SGA, transferring to NICU, convulsions, RDS, intracranial hemorrhage, assisted ventilation, VLBW showed no significant difference in both raw data analysis (Table 1) and network meta-regression analysis (Table 2 and Table 3). Data of Apgar≤7 at 5 min were insufficient to perform network meta-regression analysis.
Model convergence
Trace plots, density plots, and Brooks-Gelman-Rubin (BGR) diagnostics confirmed satisfactory model convergence across all comparisons (Supplementary Figures 1-21). All estimated parameters achieved potential scale reduction factor (PSRF, R-hat) values ranging from 1.00 to 1.03, below the predefined convergence threshold of 1.05, indicating well-mixed Markov chains and stable posterior distributions. These results confirm that the Bayesian models achieved adequate convergence for all evaluated outcomes.
Discussion
Summary of main results
To the best of our knowledge, this is the first Bayesian network meta-regression analysis on congenital malformations and other neonatal outcomes adjusting for the number of fetuses among IVF, ICSI, and SC. The most imperative is that it will further guide infertile people heralding risks of CM according to ART methods and the number of fetuses, allowing for early detection and timely intervention.
Results in the context of published literature
Based on primary endpoints analysis, IVF or ICSI significantly increased the risk of CM, aligning with Hansen [21]. Notably, our analysis showed the risk of CM increased by 32% with IVF and 46% with ICSI in singletons, while the risk with ICSI slightly decreased to 17% in twins. Several meta-analysis exploring the impacts of IVF and ICSI on CM reached similar conclusions [22, 23]. In circulatory system, ICSI had a notably higher risk than SC and IVF in singletons, which was the first report. As with previous studies, in singletons, IVF was associated with increased the risk of PTB and LBW but not VPTB and VLBW, indicatinga further confirmation when more data are available [22, 24]. While in twins, ICSI was associated with increased the risk of perinatal death, which was not mentioned before.
Although several associations reached statistical significance, the absolute risk differences were modest and clinically limited. For example, an HR of 1.17 for congenital malformations among twins conceived via ICSI corresponds to less than one additional case per 100 births compared with spontaneous conception. This suggests that, although ART procedures are associated with statistically measurable relative risks, their absolute clinical impact is small. Clinicians should therefore consider both relative and absolute risks when counseling patients and emphasize that the vast majority of ART pregnancies result in healthy outcomes.
The higher perinatal mortality in multiple pregnancies is largely explained by the increased rates of preterm birth and low birth weight, which are major determinants of neonatal morbidity and mortality [25]. In ART twin pregnancies, additional risks such as abnormal placentation, twin-to-twin transfusion syndrome, and selective intrauterine growth restriction may further worsen perinatal outcomes. Moreover, iatrogenic prematurity due to early intervention for maternal or fetal complications may contribute to the excess mortality observed [26]. These findings underline the need for careful embryo transfer policies and close monitoring of ART twin pregnancies to reduce preventable perinatal deaths.
Implications for practice and future research
Pathological cause of increased risk of neonatal outcomes by ART
ART is considerably associated with neonatal outcomes compared with SC [27, 28]. The potential reasons include two aspects: infertility itself and the influence of ART treatment. Ensuring the contribution of infertility on neonatal outcomes is essential but difficult because the studies involving siblings of ART and SC to maintain parental factors stable are few. A study of singleton sibling-relationship comparisons suggested the underlying infertility might be attributable to outcomes differences between SC and ART [29]. In contrast, Henningsen [30] reported ART still played a role in increased perinatal risks. More evidence is needed to overcome the problem further.
Hence, we mainly focus on the influence of ART treatment. The ART cycle includes ovulation-inducing medication, ovum extraction, fertilization with sperm, and embryo transferring. Among these aspects, many factors such as culture media composition, duration time in culture, freezing and thawing embryos, and manipulations of gametes as well as embryos are the underlying causes of adverse outcomes [31, 32]. Moreover, current research highlighted ART’s role in epigenetics [33, 34]. Gametogenesis and early preimplantation are two critical periods of epigenetic modification [35]: ART procedures modify the environment of gametes and embryos during the establishment of epigenetic imprints and cryoprotectants cause alterations in the integrity of germ cells and embryos [36–38]. Although the specific mechanisms are still unknown, certain potential explanatory studies have been conducted. Bai [39] reported that abnormal H3K4me3 modification in extraembryonic tissue is a major cause of implantation failure and abnormal placental development in IVF embryos. Animal and in vitro studies linked epigenetic dysregulation to coronary heart disease pathogenesis [40]. Hypermethylation of CpG islands in promoter regions, found in tetralogy of Fallot (TOF) and ventricular septal defect (VSD) patients, might trigger cardiovascular anomalies [41].
Beyond epidemiological associations, several biological mechanisms have been proposed to explain why ART may increase the risk of congenital malformations, particularly cardiac anomalies. Experimental and clinical data suggest that ART procedures may interfere with normal epigenetic programming during gametogenesis and early embryonic development, leading to abnormal expression of genes essential for cardiac morphogenesis [42]. Dysregulation of imprinted gene methylation and histone modifications has been linked to defects in cardiac cell differentiation and septal formation. Furthermore, oxidative stress induced by in vitro culture conditions, manipulation of gametes, and exposure to high oxygen tension may disrupt normal myocardial and vascular development [43]. Abnormal placentation and impaired trophoblast invasion, often observed in ART pregnancies, may also reduce uteroplacental blood flow, leading to cardiac remodeling in the fetus. Although these mechanisms provide plausible explanations, current evidence remains inconclusive. Future studies integrating molecular, clinical, and imaging approaches are needed to clarify the pathways linking ART to congenital and cardiac malformations, which will be vital for refining ART protocols and improving offspring safety.
Additionally, the parental germ cell genomes might impact outcomes. One research reported that ART offspring may carry higher levels of germline De novo mutation (gDNM) than naturally conceived offspring, and the increase in gDNM may partly explain the increased risk of congenital heart disease in ART offspring. However, it should be noted that the increased mutations mainly originate from the father’s germ cells, suggesting that ART may not be the primary cause of the increase in de novo mutations [44].
Compared with conventional IVF, ICSI involves direct sperm microinjection into the oocyte, bypassing natural sperm selection and exposing the oocyte to mechanical and chemical stress. This process may introduce sperm with DNA fragmentation or abnormal methylation patterns, leading to impaired embryonic development. Evidence suggests that ICSI-conceived embryos show altered expression of imprinted genes and disrupted epigenetic regulation, particularly in pathways related to growth and cardiac formation [45]. The use of immature or morphologically abnormal sperm in severe male infertility may further increase genetic or epigenetic risk [46]. Although the clinical significance remains uncertain, these mechanisms may partly explain the higher prevalence of congenital and cardiac malformations after ICSI.
The increased risk of CM
Due to the increased risk of malformations after ART, and the specific risk of circulatory system malformations by ICSI, clinical attention should bfocus on preventing CM and targeted antenatal testing.
Congenital cardiovascular anomalies are among the most prevalent structural malformations. The earliest diagnostic window period is around 12 weeks. Studies suggested that ultrasound detection of fetal anatomy and heart anomalies at 11–14 weeks were feasible [47–50], which has important implications for high-risk pregnancies and severe deformities, significantly decreasing maternal mental and physical injury. Meanwhile, many cardiovascular anomalies are treatable postnatally with a good prognosis. Detecting malformations promptly and potential intrauterine interventions are critical.
Therefore, based on our findings, targeting clinical “priorities” for perinatal screening among the ART population is helpful, moving forward the window periods for detection of cardiovascular anomalies. From a prevention perspective, supplementation with folic acid and vitamins timely during pregnancy can prevent congenital heart disease [51, 52].
The increased risk of PTB and LBW
Based on our result that IVF increased the risks of PTB and LBW in singletons and ICSI increased the risk of PTB in twins, the possible reasons are as follows: advanced maternal age, history of cesarean section and obesity [53], which are also high-risk factors for PTB, LBW and infertility. Besides, ART amplifying the effect of maternal infertility on PTB [54], ovarian stimulation, and the use of fresh embryo transferring [55] might affect embryonic development, increasing the risk of PTB and LBW.
In recent years, several clinical strategies have been introduced to improve perinatal outcomes in assisted reproduction. The practice of single embryo transfer has been encouraged to reduce the incidence of multiple pregnancies and to lower the risks of preterm birth and perinatal death [56]. Increasing evidence also indicates that frozen embryo transfer is associated with more favorable perinatal outcomes, including lower rates of preterm birth and low birth weight, compared with fresh embryo transfer [57]. This improvement may be related to better endometrial receptivity and more physiological hormonal conditions during frozen cycles. Furthermore, natural cycle embryo transfer protocols have been shown to decrease maternal complications, particularly hypertensive disorders that may lead to iatrogenic preterm birth [58]. These strategies represent major advances in ART practice and should be considered when interpreting our findings. However, most of the included studies did not report detailed information about the type of embryo transfer or cycle protocol, preventing us from conducting subgroup analyses. Future research incorporating these variables is warranted to clarify their impact on neonatal outcomes.
ART patients at higher risk for PTB should undergo closer monitoring and prevention. Nowadays, a history of late spontaneous abortion or preterm labor and cervical length (CL) measured by transvaginal ultrasound examination (TVUE) are major predictive indicators [59, 60]. Besides general prevention, the application of progesterone and cervical cerclage is recommended.
PTB and LBW are associated with poor perinatal outcomes, even in the long term. Therefore, it is recommended that the ART population, especially those with a predisposition to PTB, should be more likely to be referred to a tertiary center for delivery in a perinatal team that has the capacity to treat PTB and LBW.
ICSI indication
ICSI is used for treating male factor infertility and decreasing fertilization failure in unexplained infertility. However, it has not been proven to improve live birth outcomes [61]. our analysis also found that ICSI increased the risk of CM, circulatory system malformations, PTB, and perinatal death. Therefore, routine use of ICSI on all oocytes is not supported unless there is male-factor infertility or a history of previous fertilization failure.
Future research prospect
Future studies may form infertile and subfertile groups and conduct sib-ship studies to investigate the impact of ART and infertility on neonatal outcomes.
Strengths and weaknesses
This study has several strengths. The network meta-regression allowed simultaneous comparison of multiple interventions, improving precision and robustness through indirect evidence. Random-effects models were applied to account for variations across populations and methodologies, and CM classification followed ICD or EUROCAT standards to reduce misclassification. All included cohort studies ensured temporal sequencing between ART exposure and outcomes, minimizing recall and selection bias. However, as observational research, residual and unmeasured confounding cannot be fully eliminated.
Our study also has limitations. The number of eligible studies was limited, and data heterogeneity existed due to differences in design, adjustment variables, and outcome definitions. Although most studies adjusted for maternal age, parity, and BMI, residual confounding and potential confounding by indication remain possible, as couples undergoing ICSI often differ from those receiving IVF in infertility etiology and paternal factors. These findings should therefore be interpreted as associative rather than causal.
Additionally, moderate heterogeneity (I² 30–60%) and minor inconsistencies from node-splitting were observed. Although addressed by random-effects and sensitivity analyses, such variability may have affected the precision of pooled estimates. Potential publication and language biases also exist, as only English-language studies were included and unpublished data might have been missed. Collectively, residual confounding, publication bias, and heterogeneity may limit confidence in the pooled estimates. The results should be interpreted with caution, and further large-scale, well-controlled studies are needed to confirm these associations.
Conclusion
Both IVF and ICSI were associated with an increased risk of congenital malformations in singletons, whereas only ICSI was associated with higher risks in twins. IVF was associated with elevated risks of preterm birth and low birth weight in singletons, while ICSI was associated with increased risks of circulatory system malformations in singletons and preterm birth and perinatal death in twins. These findings indicate associations rather than causality and should be interpreted cautiously given the observational nature of the included studies. Our findings may assist clinicians in counseling ART patients about potential risks and highlight the importance of vigilant perinatal monitoring, rather than serving as direct clinical recommendations. Further large-scale prospective studies are warranted to confirm these associations and elucidate underlying mechanisms.
Supplementary Information
Acknowledgements
We thank Yanling Wan and Sukai Zhang for their assistance with this article.
Authors’ contributions
YT and JYL are joint first authors. DJY, SFD, and LXX: the literature search, study screening; JYL and DJY: data extraction and data analysis; JYL, LXJ, LCC: manuscript drafting; CX: data interpretation and critical revision of the manuscript; YT and ZL: obtaining the funding; HZ and LXL: approving the final version of the manuscript. LXL and HZ are the study guarantors.
Funding
This work was supported by grants from Natural Science Basic Research Program of Shaanxi (Program No.2024JC-YBMS-682), Science and Technology Program of Xian, China (Program No.24YXYJ0125) and Key research and development program of Shaanxi (Program No. 2023-YBSF-231).
Data availability
All data are incorporated into the article and its online supplementary material. The study protocol has been registered in PROSPERO (CRD420261308583).
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Ting Yuan and Yalin Ju are joint first authors.
Contributor Information
Xuelan Li, Email: lixuelan1225@126.com.
Zhen Han, Email: hanamy02@163.com.
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This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data are incorporated into the article and its online supplementary material. The study protocol has been registered in PROSPERO (CRD420261308583).