First-trimester screening and small for gestational age in twin pregnancies: a single center cohort study.

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Alexandra Sofia Queirós, Ana Bernardo, Cláudia Rijo, Ana Carocha, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4916119/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2024 Read the published version in Archives of Gynecology and Obstetrics → Version 1 posted 5 You are reading this latest preprint version Abstract Objective : This study aimed to investigate the association between maternal factors and first-trimester biophysical and biochemical markers with small for gestational age (SGA) neonates in twin pregnancies (TwPs). Methods : Single center retrospective cohort study of TwPs followed from January 2010 to December 2022 at a tertiary perinatal center, Lisbon, Portugal. Inclusion criteria consisted of 572 TwPs. Maternal and pregnancy characteristics, mean arterial pressure, pregnancy-associated plasma protein-A (PAPP-A), β-human chorionic gonadotropin (β-HCG), and uterine artery pulsatility index (UtA-PI) were analyzed. Univariable, multivariable logistic regression (LR) and receiver-operating characteristic curve analyses were performed. The main outcomes measures considered were: SGA <3 rd , <5 th and <10 th percentile, composite outcome of SGA concurrent with preterm birth (PTB) (<32, <34, and <36 weeks). Results : TwPs affected with SGA <3 rd , <5 th or <10 th percentiles were 120/572 (20.9%), 157/572 (27.4%) and 190/572 (33.2%), respectively. SGA <3 rd percentile was associated with higher rate of PTB, 59.0% of cases <32 weeks, OR 6.4 (95%CI: 3.2-12.7, p<0.001). UtA-PI and PAPP-A were identified as significant independent risk factors associated with SGA, as well as with the composite outcome of SGA concurrent with PTB. A LR model was obtained for the composite outcome SGA <3 rd percentile and PTB <32 weeks, with an AUC of 0.765, a sensitivity rate of 70%, and a false positive rate of 20%. Conclusion: SGA concurrent with prematurity significantly impacts TwPs, and the majority of pregnancies at risk for this outcome can be detected in the first trimester. However, larger datasets are necessary to develop robust predictive models. Synopsis: The association between first-trimester screening data and SGA concurrent with very preterm birth in twin pregnancies was determined in most of the cases. twin pregnancies small for gestational age fetal growth restriction preterm birth first-trimester screening aspirin prophylaxis. What does this study adds to the clinical work In singletons, first-trimester screening data can moderately predict small for gestational age, but no predictive models exist this outcome in twins, despite the higher impact in this group. In this study, the association between first-trimester screening data and small for gestational age concurrent with very preterm birth in twin pregnancies was found using regression analysis, though large datasets are needed to develop well-fitted predictive models. Introduction Fetal growth restriction (FGR) is one of the most significant causes of perinatal morbidity and mortality that significantly impacts Twin Pregnancies (TwPs) [ 1 – 2 ]. The chances of having a newborn that weighs < 1500 g is 10 times greater in TwPs than in singleton pregnancies, and at least 60% of all twins are born before the 37th week [ 3 ]. The incidence of FGR and small for gestational age (SGA) in twins depends on the definition of FGR and birthweight references adopted [ 4 ]. Previous studies have estimated the incidence of FGR to be approximately 19.7% and 10.5%, while the incidence of SGA < 10th percentile (customized for TwPs charts) is reported to be 8.6% and 6.8%, respectively, in monochorionic (MC) twins and dichorionic (DC) twins [ 5 , 6 ]. Perinatal mortality rates were found to be 75.1/1000 and 33.0/1000 in MC twins and DC twins, respectively [ 7 ]. In singleton pregnancies, first-trimester screening for aneuploidies can be extended to predict SGA [ 8 ]. However, for TwPs, first-trimester screening performance for SGA or FGR is lacking, despite the higher incidence and deleterious impact in this group [ 9 ]. This study aimed to investigate the association between maternal factors and first-trimester biophysical and biochemical markers with SGA neonates in TwPs. Materials and Methods This is a single-center, retrospective cohort analysis of twin pregnancies followed at a tertiary perinatal center, the Maternity Dr. Alfredo da Costa, São José Local Health Unit, Lisbon, Portugal, between January 2010 and December 2022. Approval for the present study was granted by the Local Ethical Committees of Nova Medical Scholl (nº 81/2020/CEFCM) and ULSSJOSE (nº 950/2020). Verbal informed consent for anonymous data collection and publication was obtained from all subjects before their inclusion in the Twins Study Group database. Informed written consent was not sought before 2020 for the present study because of the retrospective nature and anonymous data collection. After 2020, written consent was obtained from all subjects for participation and publication. Routine first-trimester screening for aneuploidies was performed by Fetal Medicine Foundation certified obstetricians. Gestational age was derived from the last menstrual period that was confirmed or corrected by the measurement of fetal crown–rump length of the larger twin in the first-trimester scan or from the day of oocyte retrieval in pregnancies after assisted reproductive techniques (ART). Chorionicity was established by ultrasonographic criteria—lambda and T-sign in dichorionic (DC) and monochorionic (MC) twins, respectively —and confirmed by examination of the delivered placenta by experienced obstetricians and histopathologists. Maternal parameters (weight and height, conception method and ethnicity) and medical history (parity, cigarette smoking, chronic hypertension, diabetes mellitus, systemic lupus erythematosus or antiphospholipid syndrome, previous PTB, FGR or preeclampsia (PE)) were included in the first-trimester screening. Maternal blood samples were collected at 10 + 0 -13 + 6 weeks into tubes in our laboratory, and serum pregnancy-associated plasma protein-A (PAPP-A), and β-human chorionic gonadotropin (β-hCG) were obtained from two immunoassay systems: Kryptor (Thermo Fisher Scientific, Clinical Diagnostics, Brahms GmbH, Henningsdorf, Germany) and Cobas (Roche Diagnostics, Basel, Switzerland) and converted to multiples of the median (MoM) using Astraia® software. First-trimester uterine artery (UtA) dopplers were obtained according to the International Society of Ultrasound in Obstetrics and Gynecology recommendations [ 10 ]. UtA was visualized transabdominally along the side of the cervix at the level of the internal os and the mean pulsatility indices (PIs) of the left and right arteries were calculated and considered according to chorionicity [ 11 ]. Inclusion criteria for the study were two live fetuses at the first-trimester scan, and subsequent birth at ≥ 24 weeks gestation. Exclusion criteria were monoamniotic twins, chromosomal and major fetal structural abnormalities, single fetal demise before 24 weeks, abnormal umbilical cord (two vessels or velamentous insertions), TORCH infections, preterm deliveries related to COVID-19, twin-to twin transfusion syndrome (TTTS) or Twin anemia polycythemia sequence (TAPS) and lost to follow-up. Because this was an observational study, obstetric interventions were conducted in accordance with clinical guidelines and followed individualized practices. In normally progressing gestations, we offered elective termination of pregnancy at 36–38 completed weeks of gestation and iatrogenic preterm deliveries were carried out based on maternal and/or fetal conditions. Hypertensive disorders of pregnancy (HDP) were defined according to the International Society for the Study of Hypertension in Pregnancy classification, diagnosis, and management recommendations for international practice [ 12 ]. FGR was defined according to “Consensus definition” in TwPs described by Khalil A et al [ 13 ]. SGA was defined as birthweight falling below the 10th, 5th, or 3rd percentile for each gestational age. To establish our own population birthweight percentiles (unpublished data), we analyzed all twins followed in our institution who had uncomplicated pregnancies (excluding maternal, obstetric and fetal diseases) and delivered two live newborns after 36 weeks between the years 1994 and 2002. Our twin birthweight population percentiles for each gestational age were adjusted for chorionicity and based on ultrasound fetal weight estimations between 20 and 35 weeks, as well as birthweight data after 36 weeks. The evaluated outcomes were SGA in one or both twins per pregnancy, as well as the composite outcome of SGA concurrent with PTB (< 32, < 34 and < 36weeks). Statistical analysis The association of SGA with all demographic and clinical variables was analyzed using Mann-Whitney, Chi-square, or Fisher’s exact tests, as appropriate. For the multivariable models, all the variables that attained a p-value ≤ 0.25 in the univariable analysis were selected. Adjusted odds ratios (OR) were estimated with corresponding 95% confidence intervals (95% CI). Discriminative ability and calibration of these models were assessed by the area under the receiver-operating characteristic curve (AUC) and the Hosmer-Lemeshow (HL) goodness-of-fit test, respectively. Although a significance level of α = 0.05 was considered, several variables of clinical relevance were maintained in the final models despite having a p-value > 0.05. Data analysis was performed using the Statistical Package for the Social Sciences for Windows (IBM Corp. Released 2021.Version 28.0). Results Of the 1175 TwPs followed between January 2010 and December 2022 in our center, the dataset included 572 TwPs that met the inclusion criteria: 450 (78.7%) DC and 122 (21.3%) MC. Of these, 464 completed first-trimester serum biochemical screening, 390 underwent UtA-PI evaluation, and 371 had complete data with all biomarkers. Maternal and pregnancy characteristics stratified per birthweight percentiles are summarized in Table 1. Overall, pregnancies affected with one or both infants classified as SGA <3 rd , <5 th or <10 th percentiles were 120/572 (20.9%), 157/572 (27.4%) and 190/572 (33.2%), respectively (Table 1). Newborns classified as SGA <3 rd , <5 th and <10 th percentiles accounted for 145/1142 (12.7%), 191/1142 (16.7%) and 247/1142 (21.6%), respectively. Considering maternal and pregnancy characteristics, MC pregnancies exhibited similar incidence of SGA <3 rd and <10 th percentile compared to DC: 22.1% vs 20.7%, OR 0.9 (95%CI: 0.5-1.5, p = 0.725) and 39.3% vs 31.6%, OR 0.7 (95%CI: 0.5-1.0, p = 0.105), respectively (Table 1). Women with low body mass index (BMI) (<20Kg/m 2 ) showed increased odds of SGA <3 rd and <10 th percentiles, 31.1% vs 19.5%, OR 1.8 (95%CI: 1.0-3.1, p=0.022), and 51.4% vs 30.5% OR 2.4 (95%CI: 1.4-3.9, p3 rd <10 th percentile, 24.1% vs 11.0%, OR 2.5 (95%CI: 1.3-5.0, p=0.005), but not to SGA <3 rd percentile: 20.4% vs 21.0%, OR 0.9 (95%CI: 0.5-1.9, p=0.908) (Table 1). Within parous women, those with a previous history of SGA had increased odds of SGA <3 rd and <10 th percentile, 53.8% vs 16.7%, OR 5.8 (95%CI: 1.8-18.4, p=0.004) and 61.5% vs 25.8%, OR 4.6 (95%CI: 1.4-14.7, p=0.009). Additionally, women under aspirin prophylaxis revealed increased odds for SGA <5 th percentile, 39.7% vs 25.7%, OR 1.9 (95%CI: 1.1-3.1, p=0.012) (Supplementary Table 1). In univariable analysis, women with high UtA-PI ≥95 th percentile (TwPs’ references) showed and increased odds of SGA <3 rd and SGA <5 th percentile, 54.2% vs 21.0 %, OR 4.4 (95%CI: 1.9-10.2, p<0.001) and 58.3% vs 26.8%, OR 3.8 (95%CI: 1.6-8.9, p<0.001), respectively (Table 2 and Supplementary Table 2). Additionally, women with low PAPP-A MoM (≤10 th percentile, corresponding to 0.50 MoM) also had increased odds of SGA <3 rd percentile, 34.8% vs 19.2% OR 2.2 (95%CI: 1.1-4.3, p<0.014) (Table 2). The incidence of SGA <3 rd or <10 th percentile concurrent with PTB <32 weeks was found to be 4.0% and 4.9%, respectively, with more than half (71.8%) of premature births <32 weeks being associated with the presence of SGA <10 th percentile in one or both twins. Before 32 weeks, SGA <3 rd was present in 23 (59.0%) vs 97 (18.2%) cases, OR 6.4 (95%CI: 3.2-12.7, p<0.001); <34 weeks, in 40 (48.8%) vs 80 (16.3%) cases, OR 4.8 (95%CI: 2.9-8.0, p<0.001) and <36 weeks, in 73 (37.4%) cases vs 47 (12.5%) cases, OR 4.2 (95%CI: 2.7-6.4, p<0.001). Iatrogenic PTB <32, 34 and 36 weeks occurred in 46.2% (50.0% MC and 45.5% DC), 40.2% (47.7% MC and 38.1% DC) and 47.7% (62.5% MC and 41.7% DC), respectively. Among women with iatrogenic PTB <34 weeks, 30 (90.9%) had prenatal suspicion of FGR and 31 (93.9 %) had one or both SGA <10 th percentile. Conversely, the incidence found of early onset-PE/HELLP <34 weeks was 1.0% (unshown data). Two single fetal demises occurred in two DC pregnancies, one at 25 weeks and another at 36 weeks. Neither fetus was suspected to be growth-restricted, although in the latter case, the surviving co-twin was SGA <3 rd percentile. Both neonatal and perinatal death rates were higher in the groups affected by SGA <5 th and <3 rd percentile, with observed incidences of 1.3% and 1.6%, and 1.7% and 2.1%, respectively; however without reaching statistical significance. Additionally, these groups experienced prolonged stays (≥ 8 days) in the neonatal care unit, particularly pronounced in MC twins, totaling 65.7% and 70.3%, p<0.001 in SGA <5 th and <3 rd percentiles, respectively (Table 3 and Supplementary Table 3). In the multivariable LR analysis, the best association model was obtained for one or both SGA <3 rd percentile concurrent with to PTB <32 weeks, with an AUC 0.765, a sensitivity rate of 70% and a false positive rate of 20% (Table 4). Lowering the false positive rate to 10% resulted in a decrease in the detection rate to approximately 35%. We also performed a similar LR model with the same covariates but using UtA-PI references in singletons of our population and observed that the sensitivity lowers to 65% and 29% for the same false positive rates considered (unshown data). UtA-PI and PAPP-A were important independent risk factors found to be associated with SGA as well the composite outcome of SGA concurrent with PTB. The highest odds (OR 6.4, 95%IC 2.5-16.4, p<0.001) were observed in women with UtA-PI ≥ 95th percentile for SGA <3 rd percentile concurrent with PTB <36 weeks (Table 4). In our cohort, there was no association between β-hCG levels and any of the evaluated outcomes (Table 2). Discussion We found a significant incidence of pregnancies affected by one or both infants SGA (< 10th percentile) in this study, approximately one-third, using TwPs’ birthweight references. The incidence of FGR and SGA in twins depends on factors such as the definition of growth restriction, the birthweight references used, and the exclusion criteria applied [ 4 ]. In our study, we excluded velamentous cord insertions due to their frequent association with FGR, particularly in MC twins [ 15 ]. Perhaps for that reason, in this dataset, we found similar rate of SGA < 3rd percentile in MC and DC twins. FGR, especially when diagnosed before term, is considered to be related to poor placental perfusion, frequently occurring alongside early-onset HDP, which predispose to iatrogenic PTB [ 14 ]. Very PTB (< 32 weeks) has a high association with FGR/SGA with a rate 8.0 times higher compared to early-onset HDP [ 11 ]. Considering first-trimester biomarkers, high UtA-PI achieved the highest odds for adverse outcomes. Indeed, almost half of the women with higher UtA-PI experienced PTB < 36 weeks concurrent with SGA < 3rd percentile. These findings highlight the clinical impact of different outcomes related to placental dysfunction in TwPs, with early FGR leading to PTB being the major manifestation. First-trimester screening is relevant for stratifying high-risk pregnancies to establish prophylactic treatments that can reduce morbidity. In singletons, first-trimester screening for SGA < 3rd percentile and PTB < 32 weeks, utilizing a predictive model (with maternal factors, PAPP-A and UtA-PI), in a large dataset (57,131 women) achieved moderate performance (estimated AUC of 0.819, with a detection rate of 55% and 72% at false positive rates of 10% and 20%, respectively) [ 16 ]. In the case of singleton pregnancies, the use of aspirin for the prevention of preterm PE in high-risk patients is well established and is accompanied by a decrease in the risk of PTB and FGR [ 17 ]. In the ASPRE trial, use of aspirin reduced the overall incidence of SGA < 10th percentile by about 40% in newborns at < 37 weeks' gestation and by about 70% in newborns at < 32 weeks [ 18 ]. In our practice, we opted to prescribe aspirin exclusively to women with significant risk conditions for hypertensive disorders (12.8% under prophylaxis in this cohort). We cannot precisely estimate the potential reduction in the occurrence of adverse outcomes with this option, however, the incidence of SGA (< 5th percentile) was doubled in women under aspirin prophylaxis, likely due to inherent factors that cannot be mitigated. More importantly, most higher-risk women (78.2%), identified by the fitted LR model, were not under aspirin prophylaxis. Would the outcomes be different if a different approach was implemented and if we could correctly identify women at risk in the first-trimester? Some societies recommend, by consensus, the use of aspirin to reduce preeclampsia in twin pregnancies [ 19 , 20 ]. However, it is unclear if these women will also benefit in reduction in the incidence of FGR and related PTB and therefore there are no clear guidelines in this population for this particular outcome. A systematic review and meta-analysis showed that administering aspirin to women with TwPs reduced the risk of PE but not FGR. The overall quality of evidence is low, highlighting the need for randomized controlled trials to elucidate this question [ 21 ]. A multicentric clinical trial (ASPRE-T) is being conducted to clarify the use of aspirin for the prevention of PE in TwPs [ 22 ]. Although the primary outcome measurement is the incidence of PE requiring delivery < 37 weeks gestation, the iatrogenic PTB for FGR is a secondary outcome aimed to be analyzed. Given that FGR leading to PTB occurs more frequently than early-onset PE, its potential impact on this outcome may be clinically more significant. Thus, there remains hope to reduce the morbidity associated with TwPs through the institution of pharmacological prophylaxis that can optimize fetal growth and reduce the incidence of PTB associated with this condition [ 23 – 24 ]. Strengths and Limitations - Our small dataset does not allow us to develop a well-fitted predictive model for SGA. However, we believe that predictive models can be established with larger multicenter data. Also, we do not routinely measure PLGF in the first trimester, and this biomarker has demonstrated to be slightly superior to PAPP-A in improving predictive models [ 16 ]. Despite these limitations, to our knowledge, this is the first reported multivariable model associating SGA in TwPs with first-trimester screening data. We emphasize the importance of employing TwPs’ specific references for UtA Dopplers, fetal growth, and birthweight in clinical studies involving TwPs, as outlined in our methodology. Conclusions SGA concurrent with prematurity significantly impacts TwPs, and the majority of pregnancies at risk for this outcome can be detected in the first trimester. However, larger datasets are necessary to develop robust predictive models. Declarations Acknowledgements No acknowledgments to declare. Conflicts of interest and Competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Authorship All authors contributed to the study conception and design. Data collection was performed by Alexandra Queirós, Ana Bernardo, Cláudia Rijo, Ana Carocha, Leonor Ferreira, Ana Teresa Martins, Álvaro Cohen and Teresinha Simões. Statistical analysis was performed by Alexandra Queirós, Marta Alves and Ana Luís Papoila. The first draft of the manuscript was written by Alexandra Queirós and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Ethical approval The present study was sought and granted approval by the Local Ethical Committees of Nova Medical School-Universidade Nova de Lisboa (No. 81/2020/CEFCM) and CHULC (reference number 950/2020). Both retrospective analyses before 2020 and prospective inclusions after 2020 were approved by the ethical committees, according to institutional policies and national laws. Consent to participate and for publication Verbal informed consent was obtained from all subjects for participation and publication of data collection and obstetric outcomes in this cohort. Written consent was not sought for the present study before 2020 due to its retrospective nature and anonymous data collection. After 2020, written consent was obtained from all subjects for participation and publication. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the authors used ChatGTP 3.5 only in order to improve language and readability. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the publication's content. Funding No funding was obtained for the present study. References Santolaya J. Twins-twice More Trouble ? Clin Obstet Gynecol 2012; 55: 296–306. Wainstock T, Yoles I, Sergienko R, Sheiner E. Twins vs singletons-Long-term health outcomes. Acta Obstet Gynecol Scand. 2023 Aug; 102(8): 1000-1006. Chauhan SP, Scardo JA, Hayes E, Abuhamad AZ, Berghella V. Twins : prevalence, problems, and preterm births. YMOB 2010; 203: 305–15. Kalafat E, Khalil A. Assessment of fetal growth in twins: Which method to use? 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London: National Institute for Health and Care Excellence (NICE); 2019 Jun 25. ISBN-13: 978-1-4731-3434-8 D'Antonio F, Khalil A, Rizzo G, Fichera A, Herrera M, Prefumo F et al. Aspirin for prevention of preeclampsia and adverse perinatal outcome in twin pregnancies: a systematic review and meta-analysis. Am J Obstet Gynecol MFM. 2023 Feb; 5(2): 100803. https://doi.org/10.1186/ISRCTN86684235 Bettiol A, Avagliano L, Lombardi N, et al. Pharmacological Interventions for the Prevention of Fetal Growth Restriction: A Systematic Review and Network Meta-Analysis. Clin Pharmacol Ther. 2021 Jul;110(1):189-199. doi: 10.1002/cpt.2164. Epub 2021 Feb 26. PMID: 33423282. Spencer RN, Carr DJ, David AL. Treatment of poor placentation and the prevention of associated adverse outcomes - what does the future hold? Prenat Diagn. 2014 Jul;34(7):677-84. doi: 10.1002/pd.4401. Epub 2014 May 29. PMID: 24799349; PMCID: PMC4265258. Tables Table 1 Maternal and pregnancy characteristics grouped by neonate birthweight: pregnancies unaffected and affected with one or both neonate small for gestational age <10 th and <3 rd birthweight percentiles. Total n =572 Unaffected n=382 (66.8%) SGA <10 th n= 190 (33.2%) SGA < 3 rd n= 120 (20.9%) Mean(SD) or n(%) Mean(SD) or n(%) OR (95%CI) p-value Mean(SD) or n(%) OR (95%CI) p-value Maternal age (years) 32.6 (5.2) 33.0 (5.3) 0.797 33.0 (5.3) 0.405 Age ≥40 years 25 (6.5%) 13 (6.8%) 1.0 (0.5-2.0) 0.893 10 ( 8.3%) 1.3 (0.6-2.9) 0.403 BMI (Kg/m2) 24.7 (4.4) 23.7 (4.1) 0.002 23.7 (4.1) 0.076 BMI <20 36 (9.4%) 38 (20.0%) 2.4 (1.4-3.9) <0.001 23 (19.2%) 1.8 (1.1-3.1) 0.022 BMI ≥20<25 201 (52.6%) 95 (50.0%) 0.9 (0.6-1.2) 0.555 59 (49.2%) 0.8 (0.6-1.3) 0.524 BMI ≥25<30 102 (26.7%) 38 (20.0%) 0.6 (0.4-1.0) 0.079 24 (20.0%) 0.7 (0.4-1.2) 0.200 BMI ≥30 43 (11.3%) 19 (10.0%) 0.8 (0.4-1.5) 0.649 14 (11.7%) 1.1 (0.6-2.0) 0.743 Ethnicity Caucasian 327 (85.6%) 166 (87.4%) 1.1 (0.7-1.9) 0.564 101 (84.2%) 0.8 (0.5-1.4) 0.470 African 46 (12.0%) 17 (8.9%) 0.7 (0.4-1.2) 0.266 12 (10.0%) 0.8 (0.4-1.7) 0.690 South Asian 8 (2.1%) 7 (3.7%) 1.7 (0.6-5.0) 0.275 7 (5.8%) 3.4 (1.2-9.6) 0.013 Mixed 1 (0.3%) 0 (0.0%) 0.480 0 (0.0%) 1.000 Nulliparous 230 (60.2%) 131 (68.9%) 1.4 (1.0-2.1) 0.041 80 (66.7%) 1.5 (1.0-2.3) 0.364 Parous with prior PTB 13/152 (8.5%) 9/59 (15.3%) 1.9 (0.7-4.7) 0.153 7/40 (17.5%) 2.2 (0.8-5.8) 0.146 Parous with prior SGA 5/152 (3.3%) 8/59 (13.6%) 4.6 (1.4-14.7) 0.009 7/40 (17.5%) 5.8 (1.8-18.4) 0.004 Parous with prior PE 5/152 (3.3%) 3/59 (5.1%) 1.5 (0.3-6.8) 0.689 3/40 (7.5%) 2.6 (0.6-11.7) 0.177 Method of conception Spontaneous 236 (61.8%) 109 (57.4%) 0.8 (0.5-1.1) 0.310 70 (58.3%) 0.9 (0.6-1.3) 0.618 Ovulation inductions 15 (3.9%) 6 (3.2%) 0.7 (0.3-2.0) 0.645 3 (2.5%) 0.6 (0.2-2.1) 0.443 ART 131 (34.3%) 75 (39.5%) 1.2 (0.8-1.7) 0.224 47 (39.2%) 1.2 (0.8-1.8) 0.418 Chorionicity Monochorionic diam. 74 (19.4%) 48 (25.3%) 0.7 (0.5-1.0) 0.105 27 (22.5%) 0.9 (0.6-1.5) 0.725 Dichorionic 308 (80.6%) 142 (74.7%) 93 (77.5%) Smoker 30 (7.9%) 24 (12.6%) 1.6 (0.9-2.9) 0.066 11 (9.2%) 0.9 (0.5-1.9) 0.908 Chronic hypertension 17 (4.5%) 12 (6.3%) 1.4 (0.7-3.0) 0.338 8 (6.7%) 1.4 (0.6-3.3) 0.370 SLE/APS/Thrombophilia 11 (2.9%) 4 (2.1%) 0.7 (0.2-2.3) 0.783 3 (2.5%) 0.9 (0.3-3.4) 1.000 Aspirin prophylactic intake (started <20 weeks) 44 (11.5%) 29 (15.3%) 1.3 (0.8-2.2) 0.231 19 (15.8%) 1.3 (0.8-2.4) 0.257 1 st trimester Mean Arterial Pressure 85.4 (8.5) 86.0 (8.7) 0.824 86.4 (8.5) 0.582 ART- artificial reproductive techniques; APS- Antiphospholipid syndrome; β-hCG- β-human chorionic gonadotropin; BMI- body mass index; CI – confidence interval; OR -odds ratio; PE- preeclampsia; PTB- preterm birth; SGA- small for gestational age; SD- standard deviation; SLE- Systemic lupus erythematosus; Table 2 First trimester serum biomarkers and uterine artery doppler grouped by neonate birthweight: pregnancies unaffected and affected with one or both neonate small for gestational age <10 th and <3 rd birthweight percentiles. Total Unaffected SGA <10 th SGA < 3 rd n n(%) n(%) OR (95%CI) p-value n(%) OR (95%CI) p-value Uterine artery available data 390 254 (65.1%) 136 (34.8%) 90 (23.1%) Uterine artery PI ≥ 90 th perc. 42 21 (8.3%) 21 (15.4%) 2.0 (1.1-3.8) 0.029 18 (20.0%) 2.8 (1.4-5.5) 0.001 Uterine artery PI ≥ 95 th perc. 24 10 (3.9%) 14 (10.3%) 2.8 (1.2-6.5) 0.013 13 (14.4%) 4.4 (1.9-10.2) < 0.001 Biomarkers available data 464 314 (67.2%) 150 (32.3%) 97 (20.8%) PAPP-A MoM ≤ 10 th perc. 46 26 (8.3%) 20 (13.1%) 1.6 (0.9-3.0) 0.103 16 (16.5%) 2.2 (1.1-4.3) 0.014 β-hCG MoM ≤ 10 th perc. 46 28 (8.9%) 18 (11.8%) 1.3 (0.7-2.5) 0.332 13 (13.4%) 1.5 (0.8-3.1) 0.187 β-hCG - β-human chorionic gonadotropin; OR- odds ratio; PAPP-A - pregnancy-associated plasma protein-A; perc. – percentile; PI – pulsatility index; SGA – small for gestational age. Table 3 Association of small for gestational age <10 th and <3 rd birthweight percentiles (one or both neonates per pregnancy) with obstetric and perinatal outcomes in MC and DC twin pregnancies Total n=572 Unaffected n=382 (MC=74, DC=308) SGA <10 th n= 190 ( MC= 48 , DC= 142 ) SGA < 3 rd n= 120 (MC=27, DC=93) Mean(SD) or n(%) Mean(SD) or n(%) OR (95%CI) p-value Mean(SD) or n(%) OR (95%CI) p-value Prenatal suspicion of Fetal Growth Restriction (one or both fetus per pregnancy) All twins 32 (8.4%) 120 (63.2%) 18.7 (11.7-29.9) < 0.001 92 (76.7%) 21.4 (12.9-35.4) <0.001 Monochorionic 5 (6.8%) 39 (81.3%) 59.8 (18.7-191.0) < 0.001 25 (92.6%) 50.0 (10.8-229.8) <0.001 Dichorionic 27 (8.8%) 81 (57.0%) 13.8 (8.2-23.1) < 0.001 67 (72.0%) 19.8 (11.3-34.6) <0.001 Fetal abnormal Doppler findings (one or both fetus per pregnancy) All twins 28 (7.5%) 66 (34.7%) 6.7 (4.1-10.9) <0.001 53 (44.2%) 7.7 (4.7-12.6) <0.001 Monochorionic 8 (10.8%) 20 (41.7%) 5.8 (2.3-14.9) < 0.001 17 (63.0%) 12.9 (4.7-35.4) <0.001 Dichorionic 20 (6.7%) 46 (32.4%) 6.9 (3.8-12.3) <0.001 36 (38.7%) 6.7 (3.8-11.8) <0.001 Single fetal demise ≥ 24 weeks a Dichorionic 1 (0.3%) 1 (0.5%) 2.1 (0.1-35.0) 0.532 1 (0.8%) 3.8 (0.2-62.4) 0.371 Gestational age at delivery (weeks) Monochorionic 35.3 (1.5) 34.2 (1.6) <0.001 33.9 (1.9) <0.001 Dichorionic 36.2 (1.6) 34.3 (2.7) <0.001 34.0 (2.7) <0.001 Mean birthweight (g) Monochorionic 2370 (323) 1816 (306) <0.001 1684 (325) <0.001 Dichorionic 2515 (346) 1872 (446) <0.001 1788 (440) <0.001 Birthweight discrepancy ≥ 25% All twins 2 (0.5%) 37 (19.5%) 45 (10.7-189.9) <0.001 34 (28.3%) 34.8 (13.2-91.6) <0.001 Monochorionic 0 (0.0%) 7 (14.6%) 0.001 b 6 (22.2%) 26.5 (3.0-232.5) <0.001 Dichorionic 2 (0.7%) 30 (21.1%) 40.3 (9.4-171.4) <0.001 28 (30.1%) 37.4 (12.7-110.4) <0.001 PTB < 32 weeks All twins 11 (2.9%) 28 (14.7%) 5.8 (2.8-11.9) <0.001 23 (19.2%) 6.4 (3.3-12.7) <0.001 Monochorionic 2 (2.7%) 4 (8.3%) 3.2 (0.5-18.6) 0.210 4 (14.8%) 8.0 (1.3-46.8) 0.021 Dichorionic 9 (2.9%) 24 (16.9%) 6.7 (3.0-14.9) <0.001 19 (20.4%) 6.2 (3.0-13.1) <0.001 PTB < 34 weeks All twins 27 (7.1%) 55 (28.9%) 5.3 (3.2-8.8) <0.001 40 (33.3%) 4.8 (2.9-8.0) <0.001 Monochorionic 7 (9.5%) 12 (25.0%) 3.1 (1.1-8.8) 0.021 9 (33.3%) 4.2 (1.5-11.9) 0.013 Dichorionic 20 (6.5%) 43 (30.3%) 6.2 (3.5-11.1) <0.001 31 (33.3%) 5.0 (2.8-8.9) <0.001 PTB < 36 weeks All twins 82 (21.5%) 113 (59.5%) 5.3 (3.6-7.8) <0.001 73 (60.8%) 4.2 (2.7-6.4) <0.001 Monochorionic 17 (23.0%) 39 (81.3%) 14.5 (5.8-35.9) <0.001 23 (85.2%) 10.8 (3.4-33.8) <0.001 Dichorionic 65 (21.1%) 74 (52.1%) 4.0 (2.6-6.2) <0.001 50 (53.8%) 3.5 (2.1-5.6) <0.001 Neonatal Death a Dichorionic 4/763 (0.5%) 0/379 (0.0%) 0.397 b 4/239 (1.7%) 3.8 (0.8-17.0) 0.129 Perinatal Death a Dichorionic 5/764 (0.6%) 1/379 (0.3%) 0.4 (0.01-2.9) 0.708 5/240 (2.1%) 3.8 (1.0-14.3) 0.078 Neonatal Care Unit admission ≥ 8 days All twins 78/759 (10.2%) 177/379 (30.8%) 7.6 (5.5-10.5) <0.001 120/235 (51.0%) 5.9 (4.3-8.1) <0.001 Monochorionic 22/148 (14.8%) 58/96 (60.4%) 8.6 (4.7-16.2) <0.001 38/54 (70.3%) 11.2 (5.8-22.6) <0.001 Dichorionic 56/611 (9.2%) 119/283 (42.0%) 7.1 (5.0-10.3) <0.001 82/181 (45.3%) 5.5 (3.8-7.9) <0.001 a) all events in dichorionic pregnancies; b) The odds ratio could not be calculated due to zero counts in one of the groups DC- Dichorionic; MC – Monochorionic; OR- odds ratio; PTB- preterm birth; SD- standard deviation; SGA- small to gestational age; Table 4 Multivariable regression analyses for SGA and concurrent PTB in twin pregnancies. Outcomes and independent variables Adjusted OR (95%CI) p-value AUC (95%CI) Sensitivity to False Positive rate of 20% Significance value One or both SGA < 3 rd percentile PAPP-A MoM < 10 th percentile 2.1 (0.9-4.6) 0.050 0.582 (0.509-0.656) S 37% 0.826 UtA-PI ≥ 95 th percentile 5.3 (2.1-12.9) <0.001 One or both SGA < 5 th percentile Maternal Age ≥40 years 2.0 (0.9-4.7) 0.088 0.666 (0.604-0.728) S 45% 0.385 Nulliparas 1.7 (0.9-2.8) 0.053 BMI 95 th percentile 4.8 (1.8-12.2) <0.001 One or both SGA < 10 th percentile Nulliparas 1.8 (1.1-2.9) 0.008 0.643 (0.584-0.702) S 39% 0.846 BMI 95 th percentile 2.9 (1.2-6.8) 0.014 One or both SGA < 3 rd percentile and PTB < 32 weeks Smoking habits 3.3 (0.9-12.1) 0.061 0.765 (0.650-0.881) S 70% 0.658 MAP (continuous) 1.0 (1.0-1.1) 0.031 PAPP-A MoM < 10 th percentile 3.2 (0.9-11.5) 0.066 UtA-PI ≥ 95 th percentile 6.0 (1.6-21.8) 0.006 One or both SGA < 3 rd percentile and PTB < 34 weeks MAP (continuous) 1.0 (0.9-1.0) 0.124 0.665 (0.557-0.773) S 43% 0.773 PAPP-A MoM (continuous) 0.4 (0.1-0.9) 0.043 UtA-PI ≥ 95 th percentile 4.7 (1.6-13.4) 0.004 One or both SGA < 3 rd percentile and PTB < 36 weeks PAPP-A MoM (continuous) 0.5 (0.3-0.9) 0.041 0.641 (0.555-0.727) S 40 % 0.468 UtA-PI ≥ 95 th percentile 6.4 (2.5-16.4) <0.001 One or both SGA < 10 th percentile and PTB < 32 weeks Smoking habits 2.6 (0.7-8.8) 0.125 0.710 (0.590-0.830) S 50% 0.880 MAP (continuous) 1.0 (0.9-1.1) 0.053 PAPP-A MoM < 10 th percentile 2.6 (0.7-8.8) 0.120 UtA-PI ≥ 95 th percentile 4.7 (1.3-16.2) 0.014 One or both SGA < 10 th percentile and PTB 95 th percentile 3.3 (1.2-9.4) 0.020 One or both SGA < 10 th percentile and PTB 95 th percentile 3.5 (1.4-8.9) 0.007 AUC- area under the receiver-operating characteristic curve; BMI- body mass index; MAP – mean arterial pressure; MoM- Multiple of the Median; OR- odds ratio; PAPP-A- Pregnancy-Associated Plasma Protein-A; PTB- Preterm Birth; SGA- Small for Gestational Age; UtA-PI – uterine artery pulsatility index. Cite Share Download PDF Status: Published Journal Publication published 26 Dec, 2024 Read the published version in Archives of Gynecology and Obstetrics → Version 1 posted Reviewers agreed at journal 07 Sep, 2024 Reviewers invited by journal 04 Sep, 2024 Editor invited by journal 16 Aug, 2024 Editor assigned by journal 16 Aug, 2024 First submitted to journal 14 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4916119","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":349710321,"identity":"34f7319e-5444-43fc-bdde-2c24f0a9d6f6","order_by":0,"name":"Alexandra Sofia Queirós","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-1736-1825","institution":"Maternidade Doutor Alfredo da Costa","correspondingAuthor":true,"prefix":"","firstName":"Alexandra","middleName":"Sofia","lastName":"Queirós","suffix":""},{"id":349710322,"identity":"1c7baa86-8778-4e7e-8ec5-1c38b8cf0ab4","order_by":1,"name":"Ana Bernardo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Bernardo","suffix":""},{"id":349710323,"identity":"18d36a11-bbee-4a2a-a103-0e88d8b4d3dc","order_by":2,"name":"Cláudia Rijo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Cláudia","middleName":"","lastName":"Rijo","suffix":""},{"id":349710324,"identity":"ad419946-1c58-40a9-ab93-9b0b2b089105","order_by":3,"name":"Ana Carocha","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Carocha","suffix":""},{"id":349710325,"identity":"957016a2-02a2-4281-ad01-f3ac59682e19","order_by":4,"name":"Leonor Ferreira","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Leonor","middleName":"","lastName":"Ferreira","suffix":""},{"id":349710326,"identity":"d6706a37-cfb5-49ab-b7cc-a535398f8459","order_by":5,"name":"Ana Teresa Martins","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Teresa","lastName":"Martins","suffix":""},{"id":349710327,"identity":"65667d9d-944b-4b49-9a4e-5ad32aa5c937","order_by":6,"name":"Álvaro Cohen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Álvaro","middleName":"","lastName":"Cohen","suffix":""},{"id":349710328,"identity":"ff86c35f-aab5-43bb-a666-d721b34fc45c","order_by":7,"name":"Marta Alves","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Alves","suffix":""},{"id":349710329,"identity":"74622318-9cfd-4762-9171-ff908673fc31","order_by":8,"name":"Ana Luísa Papoila","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Luísa","lastName":"Papoila","suffix":""},{"id":349710330,"identity":"51dc6f18-58f9-487f-ad01-ceb6fd8db9be","order_by":9,"name":"Teresinha Simões","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Teresinha","middleName":"","lastName":"Simões","suffix":""}],"badges":[],"createdAt":"2024-08-14 22:29:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4916119/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4916119/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00404-024-07884-6","type":"published","date":"2024-12-26T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72640535,"identity":"e9cdb2a1-12d4-4ecd-abb4-c2aadd5c7986","added_by":"auto","created_at":"2024-12-30 16:06:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":893221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4916119/v1/e9dc28b1-b967-4a09-8a2b-01ce83ffaeca.pdf"}],"financialInterests":"","formattedTitle":"First-trimester screening and small for gestational age in twin pregnancies: a single center cohort study.","fulltext":[{"header":"What does this study adds to the clinical work","content":"\u003cp\u003eIn singletons, first-trimester screening data can moderately predict small for gestational age, but no predictive models exist this outcome in twins, despite the higher impact in this group. In this study, the association between first-trimester screening data and small for gestational age concurrent with very preterm birth in twin pregnancies was found using regression analysis, though large datasets are needed to develop well-fitted predictive models.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eFetal growth restriction (FGR) is one of the most significant causes of perinatal morbidity and mortality that significantly impacts Twin Pregnancies (TwPs) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The chances of having a newborn that weighs\u0026thinsp;\u0026lt;\u0026thinsp;1500 g is 10 times greater in TwPs than in singleton pregnancies, and at least 60% of all twins are born before the 37th week [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The incidence of FGR and small for gestational age (SGA) in twins depends on the definition of FGR and birthweight references adopted [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous studies have estimated the incidence of FGR to be approximately 19.7% and 10.5%, while the incidence of SGA\u0026thinsp;\u0026lt;\u0026thinsp;10th percentile (customized for TwPs charts) is reported to be 8.6% and 6.8%, respectively, in monochorionic (MC) twins and dichorionic (DC) twins [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Perinatal mortality rates were found to be 75.1/1000 and 33.0/1000 in MC twins and DC twins, respectively [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn singleton pregnancies, first-trimester screening for aneuploidies can be extended to predict SGA [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, for TwPs, first-trimester screening performance for SGA or FGR is lacking, despite the higher incidence and deleterious impact in this group [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to investigate the association between maternal factors and first-trimester biophysical and biochemical markers with SGA neonates in TwPs.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis is a single-center, retrospective cohort analysis of twin pregnancies followed at a tertiary perinatal center, the Maternity Dr. Alfredo da Costa, S\u0026atilde;o Jos\u0026eacute; Local Health Unit, Lisbon, Portugal, between January 2010 and December 2022. Approval for the present study was granted by the Local Ethical Committees of Nova Medical Scholl (n\u0026ordm; 81/2020/CEFCM) and ULSSJOSE (n\u0026ordm; 950/2020). Verbal informed consent for anonymous data collection and publication was obtained from all subjects before their inclusion in the Twins Study Group database. Informed written consent was not sought before 2020 for the present study because of the retrospective nature and anonymous data collection. After 2020, written consent was obtained from all subjects for participation and publication.\u003c/p\u003e \u003cp\u003eRoutine first-trimester screening for aneuploidies was performed by Fetal Medicine Foundation certified obstetricians. Gestational age was derived from the last menstrual period that was confirmed or corrected by the measurement of fetal crown\u0026ndash;rump length of the larger twin in the first-trimester scan or from the day of oocyte retrieval in pregnancies after assisted reproductive techniques (ART). Chorionicity was established by ultrasonographic criteria\u0026mdash;lambda and T-sign in dichorionic (DC) and monochorionic (MC) twins, respectively \u0026mdash;and confirmed by examination of the delivered placenta by experienced obstetricians and histopathologists.\u003c/p\u003e \u003cp\u003eMaternal parameters (weight and height, conception method and ethnicity) and medical history (parity, cigarette smoking, chronic hypertension, diabetes mellitus, systemic lupus erythematosus or antiphospholipid syndrome, previous PTB, FGR or preeclampsia (PE)) were included in the first-trimester screening. Maternal blood samples were collected at 10\u003csup\u003e+\u0026thinsp;0\u003c/sup\u003e-13\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e weeks into tubes in our laboratory, and serum pregnancy-associated plasma protein-A (PAPP-A), and β-human chorionic gonadotropin (β-hCG) were obtained from two immunoassay systems: Kryptor (Thermo Fisher Scientific, Clinical Diagnostics, Brahms GmbH, Henningsdorf, Germany) and Cobas (Roche Diagnostics, Basel, Switzerland) and converted to multiples of the median (MoM) using Astraia\u0026reg; software. First-trimester uterine artery (UtA) dopplers were obtained according to the International Society of Ultrasound in Obstetrics and Gynecology recommendations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. UtA was visualized transabdominally along the side of the cervix at the level of the internal \u003cem\u003eos\u003c/em\u003e and the mean pulsatility indices (PIs) of the left and right arteries were calculated and considered according to chorionicity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Inclusion criteria for the study were two live fetuses at the first-trimester scan, and subsequent birth at \u0026ge;\u0026thinsp;24 weeks gestation. Exclusion criteria were monoamniotic twins, chromosomal and major fetal structural abnormalities, single fetal demise before 24 weeks, abnormal umbilical cord (two vessels or velamentous insertions), TORCH infections, preterm deliveries related to COVID-19, twin-to twin transfusion syndrome (TTTS) or Twin anemia polycythemia sequence (TAPS) and lost to follow-up.\u003c/p\u003e \u003cp\u003eBecause this was an observational study, obstetric interventions were conducted in accordance with clinical guidelines and followed individualized practices. In normally progressing gestations, we offered elective termination of pregnancy at 36\u0026ndash;38 completed weeks of gestation and iatrogenic preterm deliveries were carried out based on maternal and/or fetal conditions. Hypertensive disorders of pregnancy (HDP) were defined according to the International Society for the Study of Hypertension in Pregnancy classification, diagnosis, and management recommendations for international practice [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. FGR was defined according to \u0026ldquo;Consensus definition\u0026rdquo; in TwPs described by Khalil A et al [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. SGA was defined as birthweight falling below the 10th, 5th, or 3rd percentile for each gestational age. To establish our own population birthweight percentiles (unpublished data), we analyzed all twins followed in our institution who had uncomplicated pregnancies (excluding maternal, obstetric and fetal diseases) and delivered two live newborns after 36 weeks between the years 1994 and 2002. Our twin birthweight population percentiles for each gestational age were adjusted for chorionicity and based on ultrasound fetal weight estimations between 20 and 35 weeks, as well as birthweight data after 36 weeks.\u003c/p\u003e \u003cp\u003eThe evaluated outcomes were SGA in one or both twins per pregnancy, as well as the composite outcome of SGA concurrent with PTB (\u0026lt;\u0026thinsp;32, \u0026lt;\u0026thinsp;34 and \u0026lt;\u0026thinsp;36weeks).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe association of SGA with all demographic and clinical variables was analyzed using Mann-Whitney, Chi-square, or Fisher\u0026rsquo;s exact tests, as appropriate. For the multivariable models, all the variables that attained a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.25 in the univariable analysis were selected. Adjusted odds ratios (OR) were estimated with corresponding 95% confidence intervals (95% CI). Discriminative ability and calibration of these models were assessed by the area under the receiver-operating characteristic curve (AUC) and the Hosmer-Lemeshow (HL) goodness-of-fit test, respectively. Although a significance level of α\u0026thinsp;=\u0026thinsp;0.05 was considered, several variables of clinical relevance were maintained in the final models despite having a p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eData analysis was performed using the Statistical Package for the Social Sciences for Windows (IBM Corp. Released 2021.Version 28.0).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf the 1175 TwPs followed between January 2010 and December 2022 in our center, the dataset included 572 TwPs that met the inclusion criteria:\u0026nbsp;450 (78.7%) DC and 122 (21.3%) MC. Of these, 464 completed first-trimester serum biochemical screening, 390 underwent UtA-PI evaluation, and 371 had complete data with all biomarkers. Maternal and pregnancy characteristics stratified per birthweight percentiles are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003eOverall, pregnancies affected with one or both infants classified as SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e, \u0026lt;5\u003csup\u003eth\u003c/sup\u003e or \u0026lt;10\u003csup\u003eth\u003c/sup\u003e percentiles were 120/572 (20.9%), 157/572 (27.4%) and 190/572 (33.2%), respectively (Table 1). Newborns classified as SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e, \u0026lt;5\u003csup\u003eth\u003c/sup\u003e and \u0026lt;10\u003csup\u003eth\u003c/sup\u003e percentiles accounted for 145/1142 (12.7%), 191/1142 (16.7%) and 247/1142 (21.6%), respectively.\u003c/p\u003e\n\u003cp\u003eConsidering maternal and pregnancy characteristics, MC pregnancies exhibited similar incidence of SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e and \u0026lt;10\u003csup\u003eth\u003c/sup\u003e percentile compared to DC: 22.1% vs 20.7%, OR 0.9 (95%CI: 0.5-1.5, p = 0.725) and 39.3% vs 31.6%, OR 0.7 (95%CI: 0.5-1.0, p = 0.105), respectively (Table 1). Women with low body mass index (BMI) (\u0026lt;20Kg/m\u003csup\u003e2\u003c/sup\u003e) showed increased odds of SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e and \u0026lt;10\u003csup\u003eth\u003c/sup\u003e percentiles, 31.1% vs 19.5%, OR 1.8 (95%CI: 1.0-3.1, p=0.022), and 51.4% vs 30.5% OR 2.4 (95%CI: 1.4-3.9, p\u0026lt;0.001), respectively. Furthermore, women who reported smoking habits in the first trimester had increased odds\u0026nbsp;of SGA in the subgroup \u0026gt;3\u003csup\u003erd\u003c/sup\u003e \u0026lt;10\u003csup\u003eth\u003c/sup\u003e percentile, 24.1% vs 11.0%, OR 2.5\u0026nbsp;(95%CI: 1.3-5.0, p=0.005), but not to SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e percentile: 20.4% vs 21.0%,\u0026nbsp;OR 0.9\u0026nbsp;(95%CI: 0.5-1.9, p=0.908) (Table 1).\u003c/p\u003e\n\u003cp\u003eWithin parous women, those with a previous history of SGA had increased odds of SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e and \u0026lt;10\u003csup\u003eth\u003c/sup\u003e percentile, 53.8% vs 16.7%, OR 5.8\u0026nbsp;(95%CI: 1.8-18.4, p=0.004) and 61.5% vs 25.8%, OR 4.6 (95%CI: 1.4-14.7, p=0.009). Additionally, women under aspirin prophylaxis revealed increased odds for SGA \u0026lt;5\u003csup\u003eth\u003c/sup\u003e percentile, 39.7% vs 25.7%, OR 1.9 (95%CI: 1.1-3.1, p=0.012) (Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003eIn univariable analysis, women with high UtA-PI ≥95\u003csup\u003eth\u003c/sup\u003e percentile (TwPs’ \u0026nbsp;references) showed and increased odds of SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e and SGA \u0026lt;5\u003csup\u003eth\u003c/sup\u003e percentile, 54.2% vs 21.0 %, OR 4.4 (95%CI: 1.9-10.2, p\u0026lt;0.001) and 58.3% vs 26.8%, OR 3.8 (95%CI: 1.6-8.9, p\u0026lt;0.001), respectively (Table 2 and Supplementary Table 2). Additionally, women with low PAPP-A MoM (≤10\u003csup\u003eth\u003c/sup\u003e percentile, corresponding to 0.50 MoM) also had increased odds of SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e percentile, 34.8% vs 19.2% OR 2.2 (95%CI: 1.1-4.3, p\u0026lt;0.014) (Table 2).\u003c/p\u003e\n\u003cp\u003eThe incidence of SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e or \u0026lt;10\u003csup\u003eth\u003c/sup\u003e percentile concurrent with PTB \u0026lt;32 weeks was found to be 4.0% and 4.9%, respectively, with more than half (71.8%) of premature births \u0026lt;32 weeks being associated with the presence of SGA \u0026lt;10\u003csup\u003eth\u003c/sup\u003e percentile in one or both twins. Before 32 weeks, \u0026nbsp;SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e\u0026nbsp; was present in 23 (59.0%) vs 97 (18.2%) cases, OR 6.4 (95%CI: 3.2-12.7, p\u0026lt;0.001); \u0026lt;34 weeks, in 40 (48.8%) vs 80 (16.3%) cases, OR 4.8 (95%CI: 2.9-8.0, p\u0026lt;0.001) and \u0026lt;36 weeks, in 73 (37.4%) cases vs 47 (12.5%) cases, OR 4.2 (95%CI: 2.7-6.4, p\u0026lt;0.001). Iatrogenic PTB \u0026lt;32, 34 and 36 weeks occurred in 46.2% (50.0% MC and 45.5% DC), 40.2% (47.7% MC and 38.1% DC) and 47.7% (62.5% MC and 41.7% DC), respectively. Among women with iatrogenic PTB \u0026lt;34 weeks, 30 (90.9%) had prenatal suspicion of FGR and 31 (93.9 %) had one or both SGA \u0026lt;10\u003csup\u003eth\u003c/sup\u003e percentile. Conversely, the incidence found of early onset-PE/HELLP \u0026lt;34 weeks was 1.0% (unshown data).\u003c/p\u003e\n\u003cp\u003eTwo single fetal demises occurred in two DC pregnancies, one at 25 weeks and another at 36 weeks. Neither fetus was suspected to be growth-restricted, although in the latter case, the surviving co-twin was SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e percentile. Both neonatal and perinatal death rates were higher in the groups affected by SGA \u0026lt;5\u003csup\u003eth\u003c/sup\u003e and \u0026lt;3\u003csup\u003erd\u003c/sup\u003e percentile, with observed incidences of 1.3% and 1.6%, and 1.7% and 2.1%, respectively; however without reaching statistical significance. Additionally, these groups experienced prolonged stays (≥ 8 days) in the neonatal care unit, particularly pronounced in MC twins, totaling 65.7% and 70.3%, p\u0026lt;0.001 in SGA \u0026lt;5\u003csup\u003eth\u003c/sup\u003e and \u0026lt;3\u003csup\u003erd\u003c/sup\u003e percentiles, respectively (Table 3 and Supplementary Table 3).\u003c/p\u003e\n\u003cp\u003eIn the multivariable LR analysis, the best association model was obtained for one or both SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e percentile concurrent with to PTB \u0026lt;32 weeks, with an AUC 0.765, a sensitivity rate of 70% and a false positive rate of 20% (Table 4). Lowering the false positive rate to 10% resulted in a decrease in the detection rate to approximately 35%. We also performed a similar LR model with the same covariates but using UtA-PI references in singletons of our population and observed that the sensitivity lowers to 65% and 29% for the same false positive rates considered (unshown data).\u003c/p\u003e\n\u003cp\u003eUtA-PI and PAPP-A were important independent risk factors found to be associated with SGA as well the composite outcome of SGA concurrent with PTB. The highest odds (OR 6.4, 95%IC 2.5-16.4, p\u0026lt;0.001) were observed in women with UtA-PI ≥ 95th percentile for SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e percentile concurrent with PTB \u0026lt;36 weeks (Table 4). In our cohort, there was no association between β-hCG levels and any of the evaluated outcomes (Table 2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe found a significant incidence of pregnancies affected by one or both infants SGA (\u0026lt;\u0026thinsp;10th percentile) in this study, approximately one-third, using TwPs\u0026rsquo; birthweight references. The incidence of FGR and SGA in twins depends on factors such as the definition of growth restriction, the birthweight references used, and the exclusion criteria applied [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In our study, we excluded velamentous cord insertions due to their frequent association with FGR, particularly in MC twins [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Perhaps for that reason, in this dataset, we found similar rate of SGA\u0026thinsp;\u0026lt;\u0026thinsp;3rd percentile in MC and DC twins.\u003c/p\u003e \u003cp\u003eFGR, especially when diagnosed before term, is considered to be related to poor placental perfusion, frequently occurring alongside early-onset HDP, which predispose to iatrogenic PTB [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Very PTB (\u0026lt;\u0026thinsp;32 weeks) has a high association with FGR/SGA with a rate 8.0 times higher compared to early-onset HDP [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Considering first-trimester biomarkers, high UtA-PI achieved the highest odds for adverse outcomes. Indeed, almost half of the women with higher UtA-PI experienced PTB\u0026thinsp;\u0026lt;\u0026thinsp;36 weeks concurrent with SGA\u0026thinsp;\u0026lt;\u0026thinsp;3rd percentile. These findings highlight the clinical impact of different outcomes related to placental dysfunction in TwPs, with early FGR leading to PTB being the major manifestation.\u003c/p\u003e \u003cp\u003eFirst-trimester screening is relevant for stratifying high-risk pregnancies to establish prophylactic treatments that can reduce morbidity. In singletons, first-trimester screening for SGA\u0026thinsp;\u0026lt;\u0026thinsp;3rd percentile and PTB\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks, utilizing a predictive model (with maternal factors, PAPP-A and UtA-PI), in a large dataset (57,131 women) achieved moderate performance (estimated AUC of 0.819, with a detection rate of 55% and 72% at false positive rates of 10% and 20%, respectively) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the case of singleton pregnancies, the use of aspirin for the prevention of preterm PE in high-risk patients is well established and is accompanied by a decrease in the risk of PTB and FGR [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the ASPRE trial, use of aspirin reduced the overall incidence of SGA\u0026thinsp;\u0026lt;\u0026thinsp;10th percentile by about 40% in newborns at \u0026lt;\u0026thinsp;37 weeks' gestation and by about 70% in newborns at \u0026lt;\u0026thinsp;32 weeks [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In our practice, we opted to prescribe aspirin exclusively to women with significant risk conditions for hypertensive disorders (12.8% under prophylaxis in this cohort). We cannot precisely estimate the potential reduction in the occurrence of adverse outcomes with this option, however, the incidence of SGA (\u0026lt;\u0026thinsp;5th percentile) was doubled in women under aspirin prophylaxis, likely due to inherent factors that cannot be mitigated. More importantly, most higher-risk women (78.2%), identified by the fitted LR model, were not under aspirin prophylaxis. Would the outcomes be different if a different approach was implemented and if we could correctly identify women at risk in the first-trimester?\u003c/p\u003e \u003cp\u003eSome societies recommend, by consensus, the use of aspirin to reduce preeclampsia in twin pregnancies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, it is unclear if these women will also benefit in reduction in the incidence of FGR and related PTB and therefore there are no clear guidelines in this population for this particular outcome. A systematic review and meta-analysis showed that administering aspirin to women with TwPs reduced the risk of PE but not FGR. The overall quality of evidence is low, highlighting the need for randomized controlled trials to elucidate this question [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A multicentric clinical trial (ASPRE-T) is being conducted to clarify the use of aspirin for the prevention of PE in TwPs [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Although the primary outcome measurement is the incidence of PE requiring delivery\u0026thinsp;\u0026lt;\u0026thinsp;37 weeks gestation, the iatrogenic PTB for FGR is a secondary outcome aimed to be analyzed. Given that FGR leading to PTB occurs more frequently than early-onset PE, its potential impact on this outcome may be clinically more significant. Thus, there remains hope to reduce the morbidity associated with TwPs through the institution of pharmacological prophylaxis that can optimize fetal growth and reduce the incidence of PTB associated with this condition [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eStrengths and Limitations -\u003c/b\u003e Our small dataset does not allow us to develop a well-fitted predictive model for SGA. However, we believe that predictive models can be established with larger multicenter data. Also, we do not routinely measure PLGF in the first trimester, and this biomarker has demonstrated to be slightly superior to PAPP-A in improving predictive models [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Despite these limitations, to our knowledge, this is the first reported multivariable model associating SGA in TwPs with first-trimester screening data. We emphasize the importance of employing TwPs\u0026rsquo; specific references for UtA Dopplers, fetal growth, and birthweight in clinical studies involving TwPs, as outlined in our methodology.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSGA concurrent with prematurity significantly impacts TwPs, and the majority of pregnancies at risk for this outcome can be detected in the first trimester. However, larger datasets are necessary to develop robust predictive models.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo acknowledgments to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest and Competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design.\u0026nbsp;Data collection was performed by\u0026nbsp;Alexandra Queirós,\u0026nbsp;Ana Bernardo, Cláudia Rijo, Ana Carocha, Leonor Ferreira, Ana Teresa Martins, Álvaro Cohen and Teresinha Simões.\u0026nbsp;Statistical analysis was performed by Alexandra Queirós, Marta Alves and Ana Luís Papoila. The first draft of the manuscript was written by Alexandra Queirós and all authors commented on previous versions of the manuscript. All authors read and approved the final\u0026nbsp;manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was sought and granted approval by the Local Ethical Committees of Nova Medical School-Universidade Nova de Lisboa (No. 81/2020/CEFCM) and CHULC (reference number 950/2020). Both retrospective analyses before 2020 and prospective inclusions after 2020 were approved by the ethical committees, according to institutional policies and national laws.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efor publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVerbal informed consent was obtained from all subjects for participation and publication of data collection and obstetric outcomes in this cohort. Written consent was not sought for the present study before 2020 due to its retrospective nature and anonymous data collection. After 2020, written consent was obtained from all subjects for participation and publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the authors used ChatGTP 3.5 only in order to improve language and readability. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the\u0026nbsp;publication's content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was obtained for the present study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSantolaya J. Twins-twice More Trouble ? Clin Obstet Gynecol 2012; 55: 296\u0026ndash;306. \u003c/li\u003e\n\u003cli\u003eWainstock T, Yoles I, Sergienko R, Sheiner E. Twins vs singletons-Long-term health outcomes. 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First-trimester serum biomarkers in twin pregnancies and adverse obstetric outcomes-a single center cohort study. Arch Gynecol Obstet. 2024 May 12. doi: 10.1007/s00404-024-07547-6. Epub ahead of print. PMID: 38734998.\u003c/li\u003e\n\u003cli\u003eISUOG Practice Guidelines (updated): performance of 11\u0026ndash;14-week ultrasound scan. Ultrasound Obstet Gynecol 2023; 61: 127\u0026ndash;143\u003c/li\u003e\n\u003cli\u003eQueir\u0026oacute;s A, Domingues S, Gomes L, Pereira I, Brito M, Cohen \u0026Aacute;, Alves M, Papoila AL, Sim\u0026otilde;es T. First-trimester uterine artery Doppler and hypertensive disorders in twin pregnancies: Use of twin versus singleton references. Int J Gynaecol Obstet. 2024 May 27. doi: 10.1002/ijgo.15706. Epub ahead of print. PMID: 38800867.\u003c/li\u003e\n\u003cli\u003eBrown MA et al. Hypertensive disorders of pregnancy: ISSHP classification, diagnosis, and management recommendations for international practice. Hypertension. 2018; 72: 24-43.\u003c/li\u003e\n\u003cli\u003eKhalil A, Beune I, Hecher K, et al. Consensus definition and essential reporting parameters of selective fetal growth restriction in twin pregnancy: a Delphi procedure. Ultrasound Obstet Gynecol. 2019 Jan; 53(1): 47-54. \u003c/li\u003e\n\u003cli\u003eKalafat E, Thilaganathan B, Papageorghiou A, Bhide A, Khalil A. Significance of placental cord insertion site in twin pregnancy. Ultrasound Obstet Gynecol. 2018 Sep;52(3):378-384. doi: 10.1002/uog.18914. PMID: 28976606.\u003c/li\u003e\n\u003cli\u003eMifsud W, Sebire NJ. Placental pathology in early-onset and late-onset fetal growth restriction. Fetal Diagn Ther. 2014;36(2):117-28. doi: 10.1159/000359969. Epub 2014 Feb 21. PMID: 24577279.\u003c/li\u003e\n\u003cli\u003ePapastefanou I, Wright D, Syngelaki A, Souretis K, Chrysanthopoulou E, Nicolaides KH. Competing-risks model for prediction of small-for-gestational-age neonate from biophysical and biochemical markers at 11-13 weeks\u0026apos; gestation. Ultrasound Obstet Gynecol. 2021 Jan;57(1):52-61. doi: 10.1002/uog.23523. Epub 2020 Dec 9. PMID: 33094535.\u003c/li\u003e\n\u003cli\u003ePoon LC, Shennan A, Hyett JA, et al. The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first-trimester screening and prevention. Int J Gynaecol Obstet. 2019 May; 145 Suppl 1: 1-33. \u003c/li\u003e\n\u003cli\u003eTan MY, Poon LC, Rolnik DL, et al. Prediction and prevention of small-for-gestational-age neonates: evidence from SPREE and ASPRE. Ultrasound Obstet Gynecol. 2018 Jul;52(1):52-59. doi: 10.1002/uog.19077. Epub 2018 Jun 5. PMID: 29704277.\u003c/li\u003e\n\u003cli\u003eLow dose aspirin use during pregnancy. ACOG Committee Opinion No.743. American College of Obstetricians and Gynecologists. Obstet Gynecol 2018; 132:e44-52\u003c/li\u003e\n\u003cli\u003eHypertension in pregnancy: diagnosis and management. NICE Guideline, No. 133. London: National Institute for Health and Care Excellence (NICE); 2019 Jun 25. ISBN-13: 978-1-4731-3434-8\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Antonio F, Khalil A, Rizzo G, Fichera A, Herrera M, Prefumo F et al. Aspirin for prevention of preeclampsia and adverse perinatal outcome in twin pregnancies: a systematic review and meta-analysis. Am J Obstet Gynecol MFM. 2023 Feb; 5(2): 100803. \u003c/li\u003e\n\u003cli\u003ehttps://doi.org/10.1186/ISRCTN86684235\u003c/li\u003e\n\u003cli\u003eBettiol A, Avagliano L, Lombardi N, et al. Pharmacological Interventions for the Prevention of Fetal Growth Restriction: A Systematic Review and Network Meta-Analysis. Clin Pharmacol Ther. 2021 Jul;110(1):189-199. doi: 10.1002/cpt.2164. Epub 2021 Feb 26. PMID: 33423282.\u003c/li\u003e\n\u003cli\u003eSpencer RN, Carr DJ, David AL. Treatment of poor placentation and the prevention of associated adverse outcomes - what does the future hold? Prenat Diagn. 2014 Jul;34(7):677-84. doi: 10.1002/pd.4401. Epub 2014 May 29. PMID: 24799349; PMCID: PMC4265258.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"727\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 727px;\"\u003e\n \u003cp\u003eTable 1 Maternal and pregnancy characteristics grouped by neonate birthweight: pregnancies unaffected and affected with one or both neonate small for gestational age \u0026lt;10\u003csup\u003eth\u003c/sup\u003e\u0026nbsp; and \u0026lt;3\u003csup\u003erd\u003c/sup\u003e\u0026nbsp; birthweight percentiles.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eTotal n =572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cu\u003eUnaffected\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003en=382 (66.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cu\u003eSGA \u0026lt;10\u003csup\u003eth\u003c/sup\u003e\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003en= 190 (33.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cu\u003eSGA \u0026lt; 3\u003csup\u003erd\u003c/sup\u003e\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003en= 120 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eMean(SD) or n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eMean(SD) or n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMean(SD) or n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eMaternal age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e32.6 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e33.0 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e33.0 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eAge \u0026ge;40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e25 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e13 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.0 (0.5-2.0)\u003c/p\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e10 ( 8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.3 (0.6-2.9)\u003c/p\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eBMI (Kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e24.7 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e23.7 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e23.7 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eBMI \u0026lt;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e36 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e38 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.4 (1.4-3.9)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e23 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.8 (1.1-3.1)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eBMI \u0026ge;20\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e201 (52.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e95 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.9 (0.6-1.2)\u003c/p\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e59 (49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.8 (0.6-1.3)\u003c/p\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eBMI \u0026ge;25\u0026lt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e102 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e38 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.6 (0.4-1.0)\u003c/p\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e24 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.7 (0.4-1.2)\u003c/p\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eBMI \u0026ge;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e43 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e19 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.8 (0.4-1.5)\u003c/p\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e14 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.1 (0.6-2.0)\u003c/p\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e327 (85.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e166 (87.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.1 (0.7-1.9)\u003c/p\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e101 (84.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.8 (0.5-1.4)\u003c/p\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eAfrican\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e46 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e17 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.7 (0.4-1.2)\u003c/p\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e12 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.8 (0.4-1.7)\u003c/p\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eSouth Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e8 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.7 (0.6-5.0)\u003c/p\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e7 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.4 (1.2-9.6)\u003c/p\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eNulliparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e230 (60.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e131 (68.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.4 (1.0-2.1)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e80 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.5 (1.0-2.3)\u003c/p\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eParous with prior PTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e13/152 (8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e9/59 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.9 (0.7-4.7)\u003c/p\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e7/40 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.2 (0.8-5.8)\u003c/p\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eParous with prior SGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e5/152 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e8/59 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4.6 (1.4-14.7)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e7/40 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5.8 (1.8-18.4)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eParous with prior PE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e5/152 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3/59 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.5 (0.3-6.8)\u003c/p\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3/40 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2.6 (0.6-11.7)\u003c/p\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eMethod of conception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eSpontaneous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e236 (61.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e109 (57.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.8 (0.5-1.1)\u003c/p\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e70 (58.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.9 (0.6-1.3)\u003c/p\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eOvulation inductions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e15 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.7 (0.3-2.0)\u003c/p\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.6 (0.2-2.1)\u003c/p\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eART\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e131 (34.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e75 (39.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.2 (0.8-1.7)\u003c/p\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e47 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.2 (0.8-1.8)\u003c/p\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eChorionicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eMonochorionic diam.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e74 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e48 (25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.7 (0.5-1.0)\u003c/p\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e27 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.9 (0.6-1.5)\u003c/p\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eDichorionic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e308 (80.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e142 (74.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e93 (77.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e30 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e24 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.6 (0.9-2.9)\u003c/p\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e11 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.9 (0.5-1.9)\u003c/p\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eChronic hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e17 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e12 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.4 (0.7-3.0)\u003c/p\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e8 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.4 (0.6-3.3)\u003c/p\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eSLE/APS/Thrombophilia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e11 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.7 (0.2-2.3)\u003c/p\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.9 (0.3-3.4)\u003c/p\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eAspirin prophylactic intake (started \u0026lt;20 weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e44 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e29 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.3 (0.8-2.2)\u003c/p\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e19 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.3 (0.8-2.4)\u003c/p\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e trimester Mean Arterial Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e85.4 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e86.0 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e86.4 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eART- artificial reproductive techniques; APS- Antiphospholipid syndrome; \u0026beta;-hCG- \u0026beta;-human chorionic gonadotropin; BMI- body mass index; CI \u0026ndash; confidence interval; OR -odds ratio; PE- preeclampsia; PTB- preterm birth; SGA- small for gestational age; \u0026nbsp;SD- standard deviation; SLE- Systemic lupus erythematosus;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eTable 2 First trimester serum biomarkers and uterine artery doppler grouped by neonate birthweight: pregnancies unaffected and affected with one or both neonate small for gestational age \u0026lt;10\u003csup\u003eth\u003c/sup\u003e\u0026nbsp; and \u0026lt;3\u003csup\u003erd\u003c/sup\u003e\u0026nbsp; birthweight percentiles.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.454%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36196%;\"\u003e\n \u003cp\u003e\u003cu\u003eTotal\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e\u003cu\u003eUnaffected\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 26.0736%;\"\u003e\n \u003cp\u003e\u003cu\u003eSGA \u0026lt;10\u003csup\u003eth\u003c/sup\u003e\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 26.0736%;\"\u003e\n \u003cp\u003e\u003cu\u003eSGA \u0026lt; 3\u003csup\u003erd\u003c/sup\u003e\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.454%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36196%;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6564%;\"\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4172%;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.454%;\"\u003e\n \u003cp\u003eUterine artery available data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36196%;\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e254 (65.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e136 (34.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6564%;\"\u003e\n \u003cp\u003e90 (23.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4172%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.454%;\"\u003e\n \u003cp\u003eUterine artery PI \u0026ge; 90\u003csup\u003eth\u003c/sup\u003e perc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36196%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e21 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e21 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e2.0 (1.1-3.8)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6564%;\"\u003e\n \u003cp\u003e18 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4172%;\"\u003e\n \u003cp\u003e2.8 (1.4-5.5)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.454%;\"\u003e\n \u003cp\u003eUterine artery PI \u0026ge; 95\u003csup\u003eth\u0026nbsp;\u003c/sup\u003eperc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36196%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e10 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e14 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e2.8 (1.2-6.5)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6564%;\"\u003e\n \u003cp\u003e13 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4172%;\"\u003e\n \u003cp\u003e4.4 (1.9-10.2)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.454%;\"\u003e\n \u003cp\u003eBiomarkers available data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36196%;\"\u003e\n \u003cp\u003e464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e314 (67.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e150 (32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6564%;\"\u003e\n \u003cp\u003e97 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4172%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.454%;\"\u003e\n \u003cp\u003ePAPP-A MoM \u0026le; 10\u003csup\u003eth\u003c/sup\u003e perc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36196%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e26 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e20 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e1.6 (0.9-3.0)\u003c/p\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6564%;\"\u003e\n \u003cp\u003e16 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4172%;\"\u003e\n \u003cp\u003e2.2 (1.1-4.3)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.454%;\"\u003e\n \u003cp\u003e\u0026beta;-hCG\u0026nbsp;MoM \u0026le; 10\u003csup\u003eth\u003c/sup\u003e perc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.36196%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e28 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e18 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0368%;\"\u003e\n \u003cp\u003e1.3 (0.7-2.5)\u003c/p\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6564%;\"\u003e\n \u003cp\u003e13 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.4172%;\"\u003e\n \u003cp\u003e1.5 (0.8-3.1)\u003c/p\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026beta;-hCG - \u0026beta;-human chorionic gonadotropin; OR- odds ratio; PAPP-A - pregnancy-associated plasma protein-A; perc. \u0026ndash; percentile; PI \u0026ndash; pulsatility index; SGA \u0026ndash; small for gestational age.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"708\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 708px;\"\u003e\n \u003cp\u003eTable 3 Association of small for gestational age \u0026lt;10\u003csup\u003eth\u003c/sup\u003e\u0026nbsp; and \u0026lt;3\u003csup\u003erd\u003c/sup\u003e\u0026nbsp; birthweight percentiles (one or both neonates per pregnancy) with obstetric and perinatal outcomes in MC and DC twin pregnancies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003en=572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cu\u003eUnaffected\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003en=382 (MC=74, DC=308)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cu\u003eSGA \u0026lt;10\u003csup\u003eth\u003c/sup\u003e\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003en= 190 ( MC= 48 , DC= 142 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cu\u003eSGA \u0026nbsp;\u0026lt; 3\u003csup\u003erd\u003c/sup\u003e \u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003en= 120 (MC=27, DC=93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eMean(SD) or n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMean(SD) or n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMean(SD) or n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 708px;\"\u003e\n \u003cp\u003ePrenatal suspicion of \u0026nbsp;Fetal Growth Restriction (one or both fetus per pregnancy)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eAll twins\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e32 (8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e120 (63.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e18.7 (11.7-29.9)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e92 (76.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e21.4 (12.9-35.4)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMonochorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e5 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e39 (81.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e59.8 (18.7-191.0)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e25 (92.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e50.0 (10.8-229.8)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eDichorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e27 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e81 (57.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13.8 (8.2-23.1)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e67 (72.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e19.8 (11.3-34.6)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 708px;\"\u003e\n \u003cp\u003eFetal abnormal Doppler findings (one or both fetus per pregnancy)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eAll twins\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e28 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e66 (34.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e6.7 (4.1-10.9)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e53 (44.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7.7 (4.7-12.6)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMonochorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e8 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e20 (41.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5.8 (2.3-14.9)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e17 (63.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e12.9 (4.7-35.4)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eDichorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e20 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e46 (32.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e6.9 (3.8-12.3)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e36 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6.7 (3.8-11.8)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 708px;\"\u003e\n \u003cp\u003eSingle fetal demise \u0026ge; 24 weeks \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eDichorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2.1 (0.1-35.0)\u003c/p\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.8 (0.2-62.4)\u003c/p\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 293px;\"\u003e\n \u003cp\u003eGestational age at delivery (weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMonochorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e35.3 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e34.2 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e33.9 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eDichorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e36.2 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e34.3 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e34.0 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 708px;\"\u003e\n \u003cp\u003eMean birthweight (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMonochorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2370 (323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1816 (306)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1684 (325)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eDichorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2515 (346)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1872 (446)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1788 (440)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 708px;\"\u003e\n \u003cp\u003eBirthweight discrepancy \u0026ge; 25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eAll twins\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e37 (19.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e45 (10.7-189.9)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e34 (28.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e34.8 (13.2-91.6)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMonochorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e26.5 (3.0-232.5)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eDichorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e30 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e40.3 (9.4-171.4)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e28 (30.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e37.4 (12.7-110.4)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 708px;\"\u003e\n \u003cp\u003ePTB \u0026lt; 32 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eAll twins\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e11 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e28 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5.8 (2.8-11.9)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e23 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6.4 (3.3-12.7)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMonochorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3.2 (0.5-18.6)\u003c/p\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8.0 (1.3-46.8)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eDichorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e9 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e24 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e6.7 (3.0-14.9)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e19 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6.2 (3.0-13.1)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 708px;\"\u003e\n \u003cp\u003ePTB \u0026lt; 34 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eAll twins\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e27 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e55 (28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5.3 (3.2-8.8)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e40 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.8 (2.9-8.0)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMonochorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e7 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e12 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3.1 (1.1-8.8)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e9 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.2 (1.5-11.9)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eDichorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e20 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e43 (30.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e6.2 (3.5-11.1)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e31 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5.0 (2.8-8.9)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 708px;\"\u003e\n \u003cp\u003ePTB \u0026lt; 36 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eAll twins\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e82 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e113 (59.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5.3 (3.6-7.8)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e73 (60.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4.2 (2.7-6.4)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMonochorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e17 (23.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e39 (81.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e14.5 (5.8-35.9)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e23 (85.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10.8 (3.4-33.8)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eDichorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e65 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e74 (52.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4.0 (2.6-6.2)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e50 (53.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.5 (2.1-5.6)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNeonatal Death \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eDichorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4/763 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0/379 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.397\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4/239 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.8 (0.8-17.0)\u003c/p\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003ePerinatal Death \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eDichorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e5/764 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1/379 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.4 (0.01-2.9)\u003c/p\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5/240 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3.8 (1.0-14.3)\u003c/p\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 708px;\"\u003e\n \u003cp\u003eNeonatal Care Unit admission \u0026ge; 8 days\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eAll twins\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e78/759 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e177/379 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7.6 (5.5-10.5)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e120/235 (51.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5.9 (4.3-8.1)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eMonochorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e22/148 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e58/96 (60.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e8.6 (4.7-16.2)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e38/54 (70.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e11.2 (5.8-22.6)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cem\u003eDichorionic\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e56/611 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e119/283\u003c/p\u003e\n \u003cp\u003e(42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7.1 (5.0-10.3)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e82/181 (45.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5.5 (3.8-7.9)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ea) all events in dichorionic pregnancies; b) The odds ratio could not be calculated due to zero counts in one of the groups\u003c/p\u003e\n\u003cp\u003eDC- Dichorionic; MC \u0026ndash; Monochorionic; \u0026nbsp;OR- odds ratio; PTB- preterm birth; SD- standard deviation; SGA- small to gestational age;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"587\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 67.7843%;\"\u003e\n \u003cp\u003eTable 4 Multivariable regression analyses for SGA and concurrent PTB in twin pregnancies.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eOutcomes and independent variables\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003eAdjusted OR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2298%;\"\u003e\n \u003cp\u003eAUC (95%CI)\u003c/p\u003e\n \u003cp\u003eSensitivity to False Positive rate of 20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6916%;\"\u003e\n \u003cp\u003eSignificance value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 42.4591%;\"\u003e\n \u003cp\u003eOne or both SGA \u0026lt; 3\u003csup\u003erd\u0026nbsp;\u003c/sup\u003epercentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28.9214%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003ePAPP-A MoM \u0026lt; 10\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e2.1 (0.9-4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 17.2298%;\"\u003e\n \u003cp\u003e0.582 (0.509-0.656)\u003c/p\u003e\n \u003cp\u003eS 37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11.6916%;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eUtA-PI \u0026ge; 95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e5.3 (2.1-12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 42.4591%;\"\u003e\n \u003cp\u003eOne or both SGA \u0026lt; 5\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28.9214%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eMaternal Age \u0026ge;40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e2.0 (0.9-4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 17.2298%;\"\u003e\n \u003cp\u003e0.666 (0.604-0.728)\u003c/p\u003e\n \u003cp\u003eS 45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 11.6916%;\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eNulliparas\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e1.7 (0.9-2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eBMI \u0026lt; 20 (Kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e1.9 (1.0-3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003ePAPP-A MoM (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e0.6 (0.4-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eUtA-PI \u0026gt; 95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e4.8 (1.8-12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 42.4591%;\"\u003e\n \u003cp\u003eOne or both SGA \u0026lt; 10\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28.9214%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eNulliparas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e1.8 (1.1-2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 17.2298%;\"\u003e\n \u003cp\u003e0.643 (0.584-0.702)\u003c/p\u003e\n \u003cp\u003eS 39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11.6916%;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eBMI \u0026lt; 20 (Kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e1.7 (0.9-3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003ePAPP-A MoM (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e0.7 (0.4-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eUtA-PI \u0026gt; 95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e2.9 (1.2-6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 42.4591%;\"\u003e\n \u003cp\u003eOne or both SGA \u0026lt; 3\u003csup\u003erd\u003c/sup\u003e percentile and PTB \u0026lt; 32 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28.9214%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eSmoking habits\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e3.3 (0.9-12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 17.2298%;\"\u003e\n \u003cp\u003e0.765 (0.650-0.881)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eS 70%\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11.6916%;\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eMAP (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e1.0 (1.0-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003ePAPP-A MoM \u0026lt; 10\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e3.2 (0.9-11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eUtA-PI \u0026ge; 95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e6.0 (1.6-21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 42.4591%;\"\u003e\n \u003cp\u003eOne or both SGA \u0026lt; 3\u003csup\u003erd\u003c/sup\u003e percentile and PTB \u0026lt; 34 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 28.9214%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eMAP (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 17.2298%;\"\u003e\n \u003cp\u003e0.665 (0.557-0.773)\u003c/p\u003e\n \u003cp\u003eS 43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11.6916%;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003ePAPP-A MoM (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e0.4 (0.1-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eUtA-PI \u0026ge; 95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e4.7 (1.6-13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 42.4591%;\"\u003e\n \u003cp\u003eOne or both SGA \u0026lt; 3\u003csup\u003erd\u003c/sup\u003e percentile and PTB \u0026lt; 36 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6916%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003ePAPP-A MoM (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e0.5 (0.3-0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 17.2298%;\"\u003e\n \u003cp\u003e0.641 (0.555-0.727)\u003c/p\u003e\n \u003cp\u003eS 40 %\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11.6916%;\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eUtA-PI \u0026ge; 95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e6.4 (2.5-16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 42.4591%;\"\u003e\n \u003cp\u003eOne or both SGA \u0026lt; 10\u003csup\u003eth\u003c/sup\u003e percentile and PTB \u0026lt; 32 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.2298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6916%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eSmoking habits\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e2.6 (0.7-8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 17.2298%;\"\u003e\n \u003cp\u003e0.710 (0.590-0.830)\u003c/p\u003e\n \u003cp\u003eS 50%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11.6916%;\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eMAP (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e1.0 (0.9-1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003ePAPP-A MoM \u0026lt; 10\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e2.6 (0.7-8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eUtA-PI \u0026ge; 95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e4.7 (1.3-16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 42.4591%;\"\u003e\n \u003cp\u003eOne or both SGA \u0026lt; 10\u003csup\u003eth\u003c/sup\u003e percentile and PTB \u0026lt; 34 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6916%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eBMI \u0026ge; 30 (Kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e2.2 (0.9-5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 17.2298%;\"\u003e\n \u003cp\u003e0.652 (0.561-0.744)\u003c/p\u003e\n \u003cp\u003eS 44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11.6916%;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003ePAPP-A MoM (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e0.5 (0.2-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eUtA-PI \u0026gt; 95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e3.3 (1.2-9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 42.4591%;\"\u003e\n \u003cp\u003eOne or both SGA \u0026lt; 10\u003csup\u003eth\u003c/sup\u003e percentile and PTB \u0026lt; 36 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.2298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6916%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eNulliparas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e2.3 (1.2-4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 17.2298%;\"\u003e\n \u003cp\u003e0.669 (0.602-0.736)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eS 41%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11.6916%;\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eMonochorionic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e1.8 (0.9-3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003ePAPP-A MoM (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e0.6 (0.3-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.6603%;\"\u003e\n \u003cp\u003eUtA-PI \u0026gt; 95\u003csup\u003eth\u003c/sup\u003e percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6762%;\"\u003e\n \u003cp\u003e3.5 (1.4-8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAUC- area under the receiver-operating characteristic curve; BMI- body mass index; MAP \u0026ndash; mean arterial pressure; MoM- Multiple of the Median; OR- odds ratio; PAPP-A- Pregnancy-Associated Plasma Protein-A; PTB- Preterm Birth; SGA- Small for Gestational Age; \u0026nbsp;UtA-PI \u0026ndash; uterine artery pulsatility index.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"archives-of-gynecology-and-obstetrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arch","sideBox":"Learn more about [Archives of Gynecology and Obstetrics](https://www.springer.com/journal/404)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/arch/default.aspx","title":"Archives of Gynecology and Obstetrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"twin pregnancies, small for gestational age, fetal growth restriction, preterm birth, first-trimester screening, aspirin prophylaxis. ","lastPublishedDoi":"10.21203/rs.3.rs-4916119/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4916119/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study aimed to investigate the association between maternal factors and first-trimester biophysical and biochemical markers with small for gestational age (SGA) neonates in twin pregnancies (TwPs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Single center retrospective cohort study of TwPs followed from January 2010 to December 2022 at a tertiary perinatal center, Lisbon, Portugal. Inclusion criteria consisted of 572 TwPs. Maternal and pregnancy characteristics, mean arterial pressure, pregnancy-associated plasma protein-A (PAPP-A), β-human chorionic gonadotropin (β-HCG), and uterine artery pulsatility index (UtA-PI) were analyzed. Univariable, multivariable logistic regression (LR) and receiver-operating characteristic curve analyses were performed. The main outcomes measures considered were: SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e, \u0026lt;5\u003csup\u003eth\u003c/sup\u003e and \u0026lt;10\u003csup\u003eth\u003c/sup\u003e percentile, composite outcome of SGA concurrent with preterm birth (PTB) (\u0026lt;32, \u0026lt;34, and \u0026lt;36 weeks).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: TwPs affected with SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e, \u0026lt;5\u003csup\u003eth\u003c/sup\u003e or \u0026lt;10\u003csup\u003eth\u003c/sup\u003e percentiles were 120/572 (20.9%), 157/572 (27.4%) and 190/572 (33.2%), respectively. SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e percentile was associated with higher rate of PTB, 59.0% of cases \u0026lt;32 weeks, OR 6.4 (95%CI: 3.2-12.7, p\u0026lt;0.001). UtA-PI and PAPP-A were identified as significant independent risk factors associated with SGA, as well as with the composite outcome of SGA concurrent with PTB. A LR model was obtained for the composite outcome SGA \u0026lt;3\u003csup\u003erd\u003c/sup\u003e percentile and PTB \u0026lt;32 weeks, with an AUC of 0.765, a sensitivity rate of 70%, and a false positive rate of 20%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e SGA concurrent with prematurity significantly impacts TwPs, and the majority of pregnancies at risk for this outcome can be detected in the first trimester. However, larger datasets are necessary to develop robust predictive models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSynopsis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between first-trimester screening data and SGA concurrent with very preterm birth in twin pregnancies was determined in most of the cases.\u003c/p\u003e","manuscriptTitle":"First-trimester screening and small for gestational age in twin pregnancies: a single center cohort study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-07 12:04:20","doi":"10.21203/rs.3.rs-4916119/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-09-08T03:25:56+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-04T23:00:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Archives of Gynecology and Obstetrics","date":"2024-08-16T14:15:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-16T08:13:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Gynecology and Obstetrics","date":"2024-08-14T18:28:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-gynecology-and-obstetrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arch","sideBox":"Learn more about [Archives of Gynecology and Obstetrics](https://www.springer.com/journal/404)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/arch/default.aspx","title":"Archives of Gynecology and Obstetrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"73e5cd74-2455-4c2e-b737-07a2a736f1ab","owner":[],"postedDate":"October 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T15:59:44+00:00","versionOfRecord":{"articleIdentity":"rs-4916119","link":"https://doi.org/10.1007/s00404-024-07884-6","journal":{"identity":"archives-of-gynecology-and-obstetrics","isVorOnly":false,"title":"Archives of Gynecology and Obstetrics"},"publishedOn":"2024-12-26 15:57:13","publishedOnDateReadable":"December 26th, 2024"},"versionCreatedAt":"2024-10-07 12:04:20","video":"","vorDoi":"10.1007/s00404-024-07884-6","vorDoiUrl":"https://doi.org/10.1007/s00404-024-07884-6","workflowStages":[]},"version":"v1","identity":"rs-4916119","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4916119","identity":"rs-4916119","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}