The aim of this study was to develop a nomogram for predicting the risk of preterm birth in women undergoing in vitro fertilization (IVF) cycles.n = 827) and the full-term delivery group (n = 3439). A nomogram was established based on the multivariate logistic regression analysis results. The area under the curve (AUC) was calculated to assess the prediction accuracy of the nomogram model. The calibration curve was used to measure the calibration of the nomogram.A retrospective study of 4266 live birth cycles collected from January 2016 to October 2021 at the Center for Reproductive Medicine, First Hospital of Jilin University was performed. The sample size was sufficient based on the minimal ten events per variable (EPV) rule. The primary outcome of this study was preterm birth. The cycles were divided into the preterm birth group , antral follicle count (AFC) of more than 24 , multiple pregnancies , gestational hypertension  and gestational diabetes  were the independent risk factors for preterm birth in IVF patients. The area under curve (AUC) under the receiver operating characteristic (ROC) curve in the prediction model was 0.781(95%CI: 0.763–0.799). The calibration curve of the nomogram showed that the prediction model had a good calibration.Multivariate logistic regression analyses showed that female obesity or overweight (We used five risk factors to conduct a nomogram to predict preterm birth rates for patients undergoing IVF cycles. This nomogram can provide a visual assessment of the risk of preterm birth for clinical consultation.The online version contains supplementary material available at 10.1186/s12884-023-05646-x. Assisted reproductive technology (ART) has been more and more widely used. Although ART has contributed to the increase in the live birth rate, the safety problems caused by it should not be ignored. As a complication of ART, preterm birth is one of the leading causes of infant death and neonatal morbidity . ApproxiPrevious studies have demonstrated that preterm birth is associated with many factors, such as maternal demographic characteristics, nutritional status, pregnancy history, adverse behaviors, and so on . It was Researches on risk factors influencing preterm birth in IVF cycles remain inconsistent. Moreover, most studies have looked only at singleton pregnancies. Regarding the safety of the offspring born by ART, it is necessary to predict and intervene the risk factors of preterm birth as early as possible. There is a lack of intuitive and effective methods to assess the risk of preterm birth. This study aimed to establish a nomogram that could predict the risk of preterm birth in IVF cycles and provide early prediction and intervention for preterm birth among Infertile patients.This was a retrospective study, and the data were collected from electronic medical records at the Center for Reproductive Medicine, First Hospital of Jilin University . Inclusion criteria were patients undergoing IVF cycles with live birth from January 2016 and October 2021. The Sperm donation, preimplantation genetic diagnosis, and preimplantation genetic screening cycles were excluded. Figure The variables in the analyses included patients’ sociodemographic characteristics, personal histories, primary diagnosis for infertility, cycle type, chromosome examinations, body mass index (BMI), antral follicle count (AFC), anti-Müllerian hormone (AMH) levels, fertilization method, the thickness of endometrium, embryo transfer stage, number of embryos transferred, and follow-up information of pregnancy. Preterm birth was defined as a live birth with a gestational age between 28 and 37 weeks. Full-term birth was defined as a live birth with a gestational age between 37, not over 42 weeks. BMI was divided into four groups based on the World Health Organization criteria . Based o p < 0.05 (two-tailed).SPSS software (Version 27.0) was used to analyse the data. Continuous variables were tested for normality using Kolmogorov–Smirnov tests. Nonnormally distributed data were represented as the median (M) and interquartile range . Mann–Whitney U tests were used to investigate group differences. The comparisons of rates between the two groups were performed using the chi-square tests. The significant variables from univariate analyses were put into multivariable logistic regression to determine the independent influencing factors for preterm birth. Statistical package R (version 4.2.2) was used to establish and validate the nomogram. The nomogram was developed based on the results of the multivariate logistic regression analysis and the obtained variables were included in the nomogram to predict the preterm birth rate. The nomogram is based on proportionally converting each regression coefficient in multivariate logistic regression to a scale of 0–100 points. 100 points are assigned to the variable with the highest standardized β coefficient. The points for each independent variable were added together to get the total score for the predicted probability of the preterm birth. The area under the curve (AUC) was calculated to assess the prediction accuracy of the nomogram model. The calibration curve was used to measure the calibration of the nomogram. Statistical significance was set top < 0.05) in the two groups.A total of 4266 cycles were included in this study. There were 827 cycles in the preterm birth group and 3439 cycles in the full-term birth group. The incidence of preterm birth was 19.39% (827/ 4266). Table p < 0.05). . Female patients with a BMI of at least 25 and less than 30 had increased odds of preterm births compared to those in the normal BMI range . Female patients with a BMI of 30 or more were also more likely to have preterm births than those in the normal BMI range . Female patients with AFC of more than 24 had increased odds of preterm births compared to those with AFC between 12 and 24 . Multiple pregnancies increased the odds of preterm birth compared to singleton pregnancy . Moreover, gestational hypertension and gestational diabetes were significantly associated with preterm birth, respectively . Furthermore, we performed stratified analyses based on the number of pregnancies. For singleton pregnancies, female underweight or obesity , gestational hypertension  and gestational diabetes  were associated with preterm birth. For multiple pregnancies, female overweight , AFC of more than 24 , gestational hypertension  and gestational diabetes  were associated with preterm birth. (See Supplementary Table 2, Additional File Table Based on the multivariate logistic regression analyses, the five independent factors were included to establish the nomogram for predicting the risk of preterm birth Fig. . The recThere is a lack of studies using nomograms to predict the risk of preterm birth in IVF cycles. This nomogram we developed aimed to predict the individual probability of preterm birth for infertility patients undergoing IVF cycles intuitively. The nomogram showed that the predictors of preterm birth in our model were female BMI, AFC, multiple pregnancies, gestational hypertension, and gestational diabetes.BMI is a known influencing factor for preterm birth. Our results suggested that obesity and overweight increased the odds of preterm birth compared to patients with normal BMI. These were consistent with previous findings , 17. ObeAFC can be used as a valuable parameter for predicting ovarian response and pregnancy during ART cycles . There wAnother strong risk factor in this article was gestational hypertension. It is one of the most common pregnancy complications and causes of maternal morbidity and mortality. A meta-analysis confirmed that ART pregnancies had higher odds of gestational hypertension than spontaneous pregnancies . The mecTo the best of our knowledge, this was the first nomogram prediction model for predicting the risk of preterm birth in patients undergoing IVF cycles. We visualized the prediction model by nomogram to make it easier to understand. The points corresponding to each risk factor were obtained in the nomogram. Total points were obtained by adding five points together. The preterm birth rate based on the total points was each patient's probability of preterm birth. Furthermore, two of our five predictors were maternal factors that could predict preterm birth risk before IVF treatment. And for those predicting factors that arise during pregnancies, timely monitoring should be conducted to reduce the probability of occurrence of risk factor events to reduce the risk of preterm birth. However, several limitations of this study should be noted. First, this is a retrospective study, and the evidence is less strong than in prospective studies. Secondly, this single-center study brings about selection bias and requires further validation with data from multiple centers for external verification. Moreover, our variables came from the electronic medical records, and other relevant variables, such as the causes of preterm birth, needed to be adequately collected. Therefore, we could not stratify spontaneous and therapeutic preterm births, although there would be differences in risk factors for these two subtypes of preterm birth. Finally, the subgroup of early preterm births was not analysed, and there may be different predictors for such patients.We used the female BMI, AFC, multiple pregnancy, gestational hypertension, and gestational diabetes to conduct a nomogram for predicting preterm birth rates for patients undergoing IVF cycles. This nomogram can provide a visual assessment of the risk of preterm birth, clinical consultation, and preventive advice for patients before they enter the cycles of IVF treatment.Additional file 1.


Measurement error in gestational age (GA) may bias the association of GA with a health outcome. Ultrasound-based GA is considered the gold standard and is not readily available in low-resource settings. We corrected for measurement error in GA based on fundal height (FH) and date of last menstrual period (LMP) using ultrasound from the sub-cohort and adjusted for the bias in associating GA with neonatal mortality and low birth weight .N = 2,750). We used regression calibration to correct for measurement error in FH- and LMP-based GA accounting for maternal and child characteristics. We applied logistic regression to associate GA with neonatal mortality and low birth weight, with and without calibrating FH- and LMP-based GA.We used data collected from 01/2015 to 09/2019 from pregnant women enrolled at two public hospitals in Siaya county, Kenya (Calibration improved the precision of LMP (correlation coefficient, Calibrating FH/LMP-based GA improved the accuracy and precision of GA estimates and strengthened the association of GA with neonatal mortality/LBW. When assessing GA, neonatal public health and clinical interventions may benefit from calibration modeling in settings where ultrasound may not be fully available. Gestational age (GA) at birth is closely linked with neonatal and infant health outcomes. Accurate assessment of GA is important to guide patient care and public health interventions to improve pregnancy outcomes and infant health . More thStudies have focused mainly on assessing the validity and accuracy of alternative GA assessment methods relative to the ultrasound , 13. CurFrom January 2015 to September 2019, the Kenya Medical Research Institute (KEMRI) and the Centers for Disease Control and Prevention (CDC) in Kenya conducted a cohort study of pregnant women to assess the burden of influenza disease and its impact on birth outcomes in two rural public hospitals in Siaya County, namely, Siaya County Referral Hospital and Bondo sub-County Hospital . The stuGestational age was assessed at enrolment by ultrasound, FH, and/or LMP. Ultrasound machines were available initially through routine care as part of the Ministry of Health antenatal care follow-up. However, they were not consistently available to the study staff due to competing priority use for routine care in other services, technician unavailability, or malfunctioning of the machine. In 2018, an ultrasound machine (DP-10 Mindray) was purchased for each study site, and clinicians were trained before using the ultrasound to measure GA. Using this machine, ultrasound GA was calculated according to the parameters obtained in the following measurements: GA in obstetrics (OB) items, average ultrasound age (AUA), and composite ultrasound age (CUA). The GA in the OB items was calculated by the related GA formulae, and AUA is the average of GAs that are calculated according to biparietal diameter, head circumference, abdomen circumference, femur length, gestational sac, and crown rump length, among other parameters. The CUA is calculated using formulae based on biparietal diameter, head circumference, abdomen circumference, and femur length . FurtherWe used a regression calibration method to correct for measurement error in FH- and LMP-based GA before relating GA to a health outcome. Using regression calibration, calibrated GA was defined as the conditional expectation of true GA given error-prone FH- or LMP-based GA and predictors of GA assumed to be measured without error  , 19. Theed by Z) , 20–22. analysis . UltrasoP and FH , 19, 24.To adjust for the bias in estimating the association parameter p method –27. WithGestational age from LMP, FH, and ultrasound was summarized using descriptive statistics. We fitted a linear regression calibration model using ultrasound-based GA measurements as the response under the normality assumption. To eliminate implausible values in the response, we excluded ultrasound measurements ≤0.5th  or ≥ 99.5th  percentile of the distribution of ultrasound values; these implausible ultrasound values were considered extreme. We assumed the following variables as error-free predictors and included them in the regression calibration model: baby’s birth weight and sex, and for mothers, their age, HIV status, mid-upper arm circumference, malaria and syphilis diagnoses, first pregnancy, education level, trimester of current pregnancy, and hemoglobin levels at enrolment and delivery. The selection of these predictors was guided by their associations with GA at birth from previous studies . We, howr, using a Bernoulli random number generator, where ultrasound values are set to missing if r is zero. Statistical analyses were conducted in SAS software version 9.4 . The level of significance was defined as Subsequently, we estimated the association parameter using calibrated GA and hypothesized that the calibration process strengthens the association. To obtain calibrated log OR estimates, we used calibrated GA in the fitted logistic regression analyses that associate GA with neonatal mortality and low birth weight and compared the calibrated log OR estimates with uncalibrated log OR estimates. To obtain the standard error of the log OR estimate, we performed simple random sampling to obtain 1,000 bootstrap samples each of size 1,500. In each bootstrap sample, the log OR was estimated and the standard error obtained as the standard deviation of the log OR estimates from 1,000 bootstrap samples. To assess the effect of ultrasound measurements availability on the performance of regression calibration, we restricted the analysis to pregnant women with all the three GA measurements from ultrasound, FH, and LMP. Subsequently, we created random subsets based on percent ultrasound availability  by generating a random indicator variable, From January 2015 to September 2019, data for 2,750 pregnant women who enrolled in the cohort study were analyzed . The medUltrasound-based GA data were available for 1,176 (42.8%) pregnant women, FH for 2,521 (91.7%), and LMP for 2,720 (98.9%). The three assessment methods were used together in 1,068 women, constituting 38.8% of all the enrolled women. In fitting the calibration models, we excluded 19 records with potentially extreme ultrasound values. The observed means for GA estimates were 38.7 weeks using the LMP-based method, 38.8 weeks using FH, and 38.6 weeks using the calibrated LMP/FH-based method . The FH-p value = 0.001) and of calibrated LMP among those with and without ultrasound . The percentage of births that would be classified as preterm birth changed from 24.3 to 26.5% by calibrating the FH-based GA and from 19.9 to 11.3% by calibrating the LMP-based GA, suggesting more misclassification when using LMP than FH.We observed a similar mean distribution of calibrated GA between those with and without ultrasound measurements from the histograms and density plots . HoweverIn estimating the association of GA with neonatal mortality and low birth weight using continuous GA measurements, the log OR estimates from the calibrated GA were larger, in absolute values, than the log OR estimates from the uncalibrated GA, implying a strengthened association . An incrAccurate assessment of GA is important for obstetric and pediatric care and guides the timing of interventions at birth in reducing neonatal morbidity and mortality . RegardiAlthough the regression calibration method has been applied in other epidemiologic studies, especially in nutritional epidemiology , 20, thiNonetheless, our study has a few limitations. Despite the training of technicians, the reliability of the ultrasound results could have been hampered by inadequate experience, technical expertise of the technicians, and the stage of the pregnancy. In our study, GA was assessed only once, at enrollment, using different methods. Therefore, we were unable to quantify the magnitude of attenuation bias due to measurement error in FH -and LMP-based GA; quantifying attenuation requires multiple measurements per person from each assessment method . AdditioAccurate assessment of GA in low-resource settings poses a challenge to obstetric and pediatric care management. When assessing GA, neonatal public health and clinical interventions may benefit from a regression calibration approach in settings where ultrasound may not be fully available.ovl6@cdc.gov, after permission from the relevant authority to release the data upon reasonable request.The datasets presented in this article are available from the corresponding author, GA, Ethical clearance for the study was obtained from KEMRI Scientific and Ethics Review Unit (KEMRI SSC. 2880) and CDC’s institutional review board (CDC Protocol #6709) in February 2015. Informed consent was obtained from all participants before enrolment in the study. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.GA and SC conceptualized the study objective and the data analytic methods. GA analyzed the data and drafted the first manuscript. BN and NO cleaned the data and wrote the study design section. GA, JV, NO, BN, M-AW, and SC were involved in writing and revising the manuscript. All authors contributed to the article and approved the submitted version.


The negative impact of prenatal alcohol and tobacco exposure (PAE and PTE) on fetal development and birth outcomes are well described, yet pathophysiologic mechanisms are less clear. Our aim was to investigate (1) the associations between quantity, frequency and timing (QFT) of PAE and PTE with blood flow velocities in arteries of the fetal-placental-maternal circulation and (2) the extent to which combined effect of QFT of PAE and/or PTE and Doppler flow velocity waveforms (FWV) predict infant birth weight.The Safe Passage Study is a cohort based in urban Cape Town, South Africa. Recruitment occurred between 2007 and 2015. Information on QFT of PAE and PTE was collected prospectively at up to 4 occasions during pregnancy using a modified Timeline Follow-Back approach. Ultrasound examinations consisted of Doppler flow velocity waveforms of the uterine, umbilical (UA) and fetal middle cerebral arteries for the pulsatility index (PI) at 20–24 and 34–38 weeks. Exclusion criteria included: twin pregnancies, stillbirths, participants exposed to other drugs. The sample was divided into three groups  and included 1396 maternal-fetal-dyads assessed during the second trimester; 1398 assessed during the third trimester.PTE was associated with higher UA PI values in second and third trimesters (p < 0.001), compared to the PAE and control group. The total amount of cigarettes smoked during pregnancy was positively correlated with UA PI values . There was a positive correlation between cigarettes smoked per day in trimester one , and trimester two  and UA PI (in trimester two), as well as cigarettes smoked per day in trimester two  and trimester three  and the UA PI in trimester three. Generalized additive models indicated that PAE in trimester two, PTE in trimester one and Doppler FWV in trimester three were significant predictors of birth weight in this sample.In our study, PTE in trimesters two and three resulted in increased vascular resistance of the placenta. These findings highlight nuance in associations between PAE, PTE and blood flow velocities in arteries of the fetal-placental-maternal circulation and birth weight, suggesting that quantity and timing are important factors in these relationships.The online version contains supplementary material available at 10.1186/s12884-023-05881-2. Harmful prenatal substance exposure remains a global public health problem. Maternal consumption may result in detrimental effects on fetal development and subsequent child health. Alcohol and tobacco use by pregnant women has been well-described as being associated with poor fetal growth and increased risk of birth complications such as preterm delivery, stillbirth and sudden infant death syndrome (SIDS) , 2. FetaAlthough the potential adverse outcomes are known, the pathophysiology of fetal impairment associated with PAE and PTE remains poorly understood. One potential mechanism for how these exposures may impact fetal growth and other outcomes, is restricted blood flow in the vascular territories of the placenta. During pregnancy, hemodynamic changes are significant in order to fulfil the needs of the developing fetus , 9. DoppResearch on blood flow velocities in arteries of the fetal-placental-maternal associated with PAE has been mostly conducted in nonhuman primates and rat models, noting the differences between human and nonhuman primate pregnancy, including but not limited to length of gestation and placental architecture. A recent systematic review done by Pintican and colleagues  recognizTo address gaps in the literature, we used a South African birth cohort sample to investigate (1) associations between QFT of PAE and PTE with blood flow velocities in arteries of the fetal-placental-maternal circulation and (2) the extent to which combined effect of QFT of smoking and/or drinking and Doppler FWV predict infant birth weight.The longitudinal cohort Safe Passage Study, conducted by the Prenatal Alcohol in SIDS and Stillbirth (PASS) Network, is a unique international, community-linked, study. It was the first multi-site study of SIDS and stillbirth to integrate prospectively collected pregnancy exposure information with a range of biological information to investigate the role of PAE in the risk for SIDS, stillbirth, fetal alcohol syndrome and FASDs.As part of the Safe Passage Study of the PASS Network at Stellenbosch University, South Africa, 7060 pregnant women were recruited from the Bishop Lavis and Belhar residential areas for the period occupying August 2007 - January 2015. These sites were selected on account of a historical reputation for having a high prevalence of PAE and SIDS, and the need was recognized to include populations where the marked ethnic and socioeconomic disparities in SIDS remain understudied. Gestational age was determined by earliest ultrasound before the second antenatal visit. Of the participants who enrolled prior to 24 weeks gestation (representing 40% of participants), seven in ten were randomly selected and were invited to participate in the embedded study. The embedded study included the collection of additional information such as biometry and Doppler ultrasound velocimetry of uterine and fetal vessels. Detailed information on the participants and the study design have been described by Dukes et al. To investigate the aims stated above, data collected during the eight-year period was applied to analyse physiological profiles of participants who participated in the embedded study. Participants were excluded from the current analysis on the basis of (1) multiple births, (2) stillbirths, and (3) prenatal exposure to other recreational drugs (i.e. methamphetamine and marijuana). Pregnant women living with HIV, although not excluded from the birth cohort, were excluded from our analysis because of their higher risk of low birth weight infants .A timeline follow-back measurement (TLFB) was used to elicit detailed self-reported exposure information. A blank calendar is used by participants to record incidents unique to their lives during a specified time period prior to the interview. This type of retrospective tool allows the user to contemplate QFT patterns for specific substances . In order to improve the method for verifying alcohol exposure information, a feasibility study (N = 518) was conducted on the target population prior to the birth cohort. According to findings from the feasibility study that have not been published, women in the target population frequently share beverages and frequently use different container sizes than those described in the traditional TLFB. In addition, a statistically significant difference was found between the number of drinks reported in a typical week, as a result of a single question, and the number of standard drinks as determined by the traditional TLFB with a seven-day reference period. Therefore, the standard TLFB and associated aids were modified for the Safe Passage Study . In addiquantity of PAE was measured using the total amount of standard drinks consumed during pregnancy and the average number of drinks consumed per week. In the current dataset, total alcohol consumption also included total standard drinks consumed around the LMP (± 15 days). The frequency of PAE was measured using the total number of days with binge consumption (more than four drinks per sitting) as well as the number of drinks per drinking day. Timing of PAE identified the total amount of drinks consumed per trimester.At the recruitment interview, exposure information was collected for the time around conception ) and for 30 days prior to the participant’s last reported drinking day. At subsequent interviews, if the participant reported consumption since her previous visit, the reference period consisted of the 30 days prior to the last drinking day. Peri-conception (two weeks prior and two weeks following the LMP) alcohol intake information was also collected. Total grams of alcohol consumed per drink were calculated and converted into standard drinks using the National Institute on Alcohol Abuse and Alcoholism (NIAAA) definition of one standard drink equals 14 g of pure alcohol. The quantity of PTE was measured using the average number of cigarettes smoked per day and the total amount of cigarettes smoked throughout pregnancy, from LMP to time of delivery. The timing of PTE was calculated using the total amount of cigarettes smoked per trimester. There was no measure of frequency of PTE in each trimester.PTE information was also obtained up to four times during pregnancy in which women reported their smoking habits for their last reported smoking day and 30 days prior. The To incorporate all aspects of fetal circulation, we obtained Doppler measurements on different vascular beds  at two iThe participants were divided into three groups for preliminary analyses of the general characteristics of the sample population: the control group (did not smoke or drink), PAE group (mothers who drank during pregnancy), and PTE group (mothers who smoked during pregnancy). Exposure outliers (greater than or less than three standard deviations from the mean) were excluded from the analysis.2) distribution and post-hoc Dunn’s Tests were used to test for significance.All analyses were carried out using R (v4.2.0) and RStudio , 29. StaUtA and UA PI outliers were compared against reference values for a particular gestational age to determine whether there were any outlier values and whether these could be attributed to the PAE or PTE groups, as well as whether there is an association with an “outlier” doppler value and birthweight. “Outliers” were defined as PI values greater than or less than two standard deviations from the mean (PI > ± 2SD), and statistically significant differences were calculated using a t-test.A generalized additive model (GAM) was performed to further understand whether a combined effect of QFT of smoking and/or drinking and Doppler FWV results on birth weight was detectable in this sample . The usefj(.) are functions of the covariates/predictor variables [2 value combination before adding additional information to the model [where g and 1398 assessed in the third trimester  = 23.04, p < 0.001; Dunn’s: p < 0.001). Across the three groups, over 88% of the mothers gave birth at full term ( > = 37 weeks gestation). There were differences between gestational age across the three groups (χ2(2) = 6.25, p = 0.044) and this was primarily between the control (median = 276 days) and PTE group (median = 274 days) (Dunn’s: p = 0.013). The PAE group had a median gestational age of 275 days. The proportion of male to female fetuses was well balanced across the three groups with 48–49% of the newborns being male. There was a significant difference in the birth weights across the three groups, with the highest average birth weight recorded in the control group (median = 3150 g), significantly higher than both the PAE (median 3040 g) and the PTE groups (median 3020 g) (χ2(2) = 21.13, p < 0.001Dunn’s: p < 0.001). There was a fairly low incidence of low birth weight in the whole sample, with only 14% of the newborns weighing less than 2500 g.Distribution of participant characteristics Table  is dividThe distribution of alcohol and tobacco exposure by trimester in terms of quantity and frequency is presented in Table Within the PTE group, the average number of cigarettes smoked per day during pregnancy was 4.1 ± 0.11 SE. As with alcohol consumption, cigarette use also significantly decreased throughout the pregnancy  and average UtA PI  and birth weight in both the second and third trimesters . In addition, there was also a positive correlation between MCA PI in the third trimester and birth weight .There were differences between UA PI in trimester two and both substance use groups  = 5.15, p < 0.01). There were differences between the UA PI in the control group (mean = 1.19 ± 0.17) and the UA PI in the PAE  and PTE groups . Despite there being a negative correlation between PAE and birthweight in the second trimester (previous section), there were no significant differences between doppler data and birth weight in PAE and controls.When looking at quantity-timing aspects of PTE, there was a positive correlation between the total number of cigarettes smoked during pregnancy and the UA PI in trimester two . There were also correlations between the number of cigarettes smoked in both the second and third trimesters and doppler data, with a positive correlation between cigarettes per day in trimester one , and trimester two  and UA PI (in trimester two), as well as cigarettes per day in trimester two  and trimester three  and the UA PI in trimester three. Infants born to mothers who both smoked and drank had a stronger correlation between UA PI and lower birth weights , compared to mothers that only smoked  or drank .Overall in the sample, abnormal UtA values (defined as PI ± 2SD) were seen in 7% fetuses in trimester two and 10% of fetuses in trimester three, and only 2% of these abnormal indices were seen in both trimester two and trimester three for the same fetuses. Abnormal UA values were observed in 19% of fetuses in trimester two and 10% in trimester three, and 3% had abnormal UA PIs in both trimesters. Abnormal Doppler (both UtA and UA) was seen in both the UtA and UA in 23 fetuses in trimester two and nine fetuses in trimester three (2% and 1% respectively). Birth weights were significantly lower in fetuses that had an abnormal UA PI in both second (t(87.68) = 6.50, p < 0.001) and third trimesters (t(146.3) = 5.66, p < 0.001). Birth weight and UtA PI showed similar differences, with significantly lower birth weights associated with abnormal UtA PI in the second trimester (t(330.3) = 2.76 p < 0.05). However, no significant differences were seen between abnormal doppler indices across the groups.A stepwise manual backward elimination process was performed to further understand whether a combined effect of QFT of smoking and/or drinking and Doppler FWV results on birth weight was detectable in this sample. To mitigate the effects of gestational age on birth weight, weeks of gestation was included in the models. All the variables used in the models are listed in the tables below; however, only p values of significant factors (< 0.05) are reported in the table. The results of the final two GAMs are illustrated in Table Doppler data collected in the third trimester appeared to be a more significant predictor of birth weight in the models, across all levels, especially MCA PI. Both UA PI and MCA PI came out as significant non-linear predictors in the models, which explains why they didn’t appear as significant in the correlations above. When exposure variables were introduced, only two variables came out as statistically important, smoking in trimester one and drinking in trimester two, which both significantly contributed to a lower birth weight.Several key findings emerged from this study: (1) There were no significant effects of community levels of PAE on blood flow velocities in arteries of the fetal-placental-maternal circulation in this cohort; (2) In contrast, PTE was associated with higher UA PI values in both the second and third trimesters compared to the PAE and control group, suggesting increased fetoplacental resistance. There were quantity-timing effects between the number of cigarettes smoked in both of these trimesters and UA PI values with a positive correlation demonstrated; (3) When taking multiple covariates into account, PAE in trimester two and PTE in trimester one appeared to more significant predictors of birth weight and (4) Doppler data collected during the third trimester surfaced to be a more significant predictor of birth weight in comparison to data collected during the second trimester. Third trimester data was demonstrated to be most significant in predicting birth weight in all samples, especially MCA PI.In our sample, the group of infants with PAE had a significantly lower birth weight compared to the unexposed control group. Investigation of QFT within the PAE group revealed quantity-frequency effects on birth weight linked to specific timing. There were associations between the total number of drinks consumed during pregnancy (p < 0.05), average drinks consumed per week (p < 0.001), drinks consumed per drinking day (p < 0.05), with lower birth weight. However, these associations were mainly linked to maternal alcohol consumption during the second trimester, with first and third trimesters having a less clear association with lower birth weights in this cohort. Several studies have suggested that a dose-response relationship between PAE and birthweight exists, suggesting that heavy maternal alcohol consumption during pregnancy is associated with high risk of low birth weight , 36, whiIn this study PTE correlated with lower birth weight compared to control and PAE groups. There was a significant association between the number of cigarettes smoked per day and lower birth weight across all three trimesters during pregnancy (p < 0.001), but especially during the first trimester. This dose-dependent relationship between PTE and birth weight has been described in other studies in both middle-  and highThis study further reports an association between Doppler FWVs and birth weight. Our sample described negative correlations in the second and third trimesters between both the average UA PI and UtA PI values and birth weight (p < 0.001). There was a positive correlation between MCA PI in the third trimester and birth weight (p < 0.001). When we introduced other covariates such as hypertension and depression into the model, the fit improved, though slightly 38.1 to 40.2%), with both a history of depression prior to pregnancy and a history of pregnancy related hypertension in a previous pregnancy negatively affecting birth weight. Interestingly, by including socio-economic variables , there was no significant improvement in the model. This is probably due to the lack of variability in these variables for the purpose of the broader study. Several maternal demographic and lifestyle characteristics have been correlated with adverse pregnancy outcomes. The effect of these characteristics on uteroplacental circulation might explain associations, but inadequate information is available about stated characteristics modulating placental resistance indices. The Generation R study (n = 7660), conducted in the Netherlands, described that women with higher maternal age, lower maternal educational levels and non-European descent tend to have slightly higher UtA and UA resistance levels from the second trimester onward, but these results were not consistent .2%, with indicateIn our sample there were no demonstrated associations of PAE on arterial blood flow velocities in the fetal–placental–maternal circulation. Despite a strong negative correlation between the amount of alcohol consumed in the second trimester and a lower infant birth weight, there were also no differences in associations between Doppler data and birth weight between the infants of mothers in the drinking and non-drinking groups. Animal studies clearly demonstrate that alcohol freely crosses the placental barrier and enters the fetal circulation, with ovine  and baboIn our cohort, PTE was associated with raised UA PI values in both the second and third trimester (p < 0.001), suggesting increased fetoplacental resistance. This is similar to other studies that have been conducted in middle (i.e. Turkey) and high-income countries , which also described an association between PTE and increased UA PI in both second  and thirIn the present study, the UtA PI values were consistent with the results of previous work One of the unexpected findings was the lack of association between the interaction of PAE and PTE on arterial resistance adaptations in this sample. This is in contrast to a previous study from the same cohort n = 5806) that indicated that the PIs of the uterine and umbilical arteries were significantly increased in the high-drinking-high-smoking group in contrast to the single exposure groups 06 that i. Our modThe major strength of our study is its prospective design from early fetal life and the size of the cohort. The Safe Passage Study draws participants from a homogenous ethnic and socioeconomic group and has employed precise measures to assess maternal drinking and smoking throughout all trimesters of pregnancy, allowing for novel analysis by leveraging QFT of exposure with prospective data. Although measurement error is inevitable based on self-reports, a study done by Himes and colleagues  indicateSeveral limitations deserve emphasis. First, representativeness across cultures was limited for the South African cohort due to variations in drinking patterns, tobacco use, and environmental factors. Second, we included preterm births in this analysis, and thus prenatal exposure data might have been distorted by missed recruited visits due to prematurity. In a previous publication from the same cohort , women wWe identified that PTE in both the second and third trimesters is associated with increased vascular resistance of the placenta. Although community levels of PAE were not associated with increased vascular resistance, the potential relationship between the combined effect of PAE and PTE on maternal-fetal hemodynamic merits further investigation. Furthermore, when considering predictors of birth weight, PAE in trimester two, PTE in trimester one and Doppler data collected in trimester three appear to be most significant in this sample. The use of prospective QFT data extends our understanding of how important it may be to consider community levels of exposure when examining underlying pathophysiologic mechanisms contributing to adverse birth outcomes secondary to PAE and/or PTE.Below is the link to the electronic supplementary material.Supplementary Material 1


Trichomonas, including MET-resistant strains. Thus, the new cryogels with MET may be promising dosage forms for the treatment of vaginal infections.The development of polymeric carriers based on partially deacetylated chitin nanowhiskers (CNWs) and anionic sulfated polysaccharides is an attractive strategy for improved vaginal delivery with modified drug release profiles. This study focuses on the development of metronidazole (MET)-containing cryogels based on carrageenan (CRG) and CNWs. The desired cryogels were obtained by electrostatic interactions between the amino groups of CNWs and the sulfate groups of CRG and by the formation of additional hydrogen bonds, as well as by entanglement of carrageenan macrochains. It was shown that the introduction of 5% CNWs significantly increased the strength of the initial hydrogel and ensured the formation of a homogeneous cryogel structure, resulting in sustained MET release within 24 h. At the same time, when the CNW content was increased to 10%, the system collapsed with the formation of discrete cryogels, demonstrating MET release within 12 h. The mechanism of prolonged drug release was mediated by polymer swelling and chain relaxation in the polymer matrix and correlated well with the Korsmeyer–Peppas and Peppas–Sahlin models. In vitro tests showed that the developed cryogels had a prolonged (24 h) antiprotozoal effect against  One of the most common strategies to improve the efficacy of known drugs is their modification by incorporation into various polymeric systems based on natural polysaccharides ,2. For eCNWs are the product of partial deacetylation of the nitrogen-containing biopolymer α-chitin, which consists of N-acetylglucosamine residues linked by β-(1→4)-glycosidic linkages. As a result of partial deacetylation, D-glucosamine residues (not more than 50%) appear in the macromolecule. These modified polysaccharide molecules contain active cationic amino groups (surface cationization), and at the same time, they are insoluble in both water and acidic solutions. Thus, CNWs retain the beneficial properties of chitin and chitosan, demonstrating excellent biocompatibility, biodegradability, low immunogenicity, and antibacterial activity ,16. In aFor example, Lin et al.  developeCarrageenan (CRG)-based hydrogels are of interest as vaginal delivery systems for the treatment of various sexually transmitted infections because the spectrum of activity of sulfated polysaccharides extends to various enveloped viruses, including herpes simplex virus, cytomegalovirus, and human immunodeficiency virus . CRGs coTrichomonas vaginalis, is the most common non-viral sexually transmitted infection . N. N45]. NCNWs obtained by partial deacetylation of α-chitin contain amino groups (pKa ~ 6.5) on their surface, which are protonated and become positively charged in an acidic environment c. Due toThe formation of a two-phase CRG-CNW composite hydrogel occurred both by the formation of a polyelectrolyte complex due to the electrostatic interaction of negatively charged sulfate groups of CRG with positively charged amine groups of CNW and the formation of additional hydrogen bonds, and by the formation of a physical gel due to the entanglement of macromolecular chains. When water is added to the hydrogel, the physical gel slowly disintegrates and most of the CRG passes into the solution containing the CRG-CNW microparticles (microgel). To study the composition, the microparticles were isolated from the microgels by centrifugation, and the molar ratio between the monomeric units of CRG and CNWs was estimated using elemental analysis data.According to the elemental analysis data, the obtained polyelectrolyte complex CRG-CNW-MP has the ratio CRG:CNW = 0.33, i.e., almost all amino groups of CNWs with DDA of 0.40 are bound to CRG.According to electrophoretic light scattering data, in contrast to positively charged CNWs , CRG-CNW-MPs had a negative ζ-potential of about −13.4 ± 0.3 mV due to the presence of CRG molecules.Dh) of 50 ± 30 and 300 ± 10 nm, which was in good agreement with the SEM data [Dh = 56 ± 12 and 374 ± 108 nm, indicating the preservation of the dominant CNW structure in the resulting systems, which is characteristic of various objects with CNWs [Dynamic light scattering showed that CNWs had apparent hydrodynamic diameters , a complex biphasic hydrogel system is formed consisting of a three-dimensional network of CRG macromolecules with CNWs entrapped within .−1 to the lowest possible value (0.0001 s−1). The dependence of viscosity on shear rate  from 1000 sear rate , Table 2The Cox–Merz rule  did not hold for the tested samples, so we used only shear test data to analyze the rheological behavior. There is hysteresis when testing with decreasing and increasing strain rates, but it is not significant and only over a narrow range of strain rates, so it can be ignored. The graphs show only shear mode data with a decrease in shear rate.The contribution of the yield strength is insignificant at high speeds and manifests itself at a low shear rate, but determines the state of the gel. The calculation was performed according to Equation (3) by varying the parameters with an accuracy of 1%; the minimum standard deviation (SD) of the viscosity was the calculation criterion. The lowest Newtonian viscosity is usually the solvent viscosity, but in this case, we take a value of zero . The rheological properties of the samples tested are summarized in It should be noted that the most pronounced relationships characterizing the strength of the structure of the obtained hydrogels were observed at low shear rates .The analysis of the obtained results allows us to conclude that the strongest gel is formed at the introduction of 5% CNWs. Increasing the CNW content up to 10% leads to an increase in the number of local crosslinks and a violation of the gel homogeneity.3H, pKa ~2) in the CRG leads to a different type of interaction with CNWs, and when they are increased to 10%, the homogeneity of the crosslinked structure is broken, and a discrete composite hydrogel is formed.At the same time, the effect of CNWs on the structural organization of hydrogels depends significantly on the type of functional group and the structural organization of the polyacid. For example, for composite hydrogels based on sodium alginate  and CNWs, we previously found that the introduction of CNWs up to 10% strengthened the hydrogel structure while maintaining its homogeneity . The preThe resulting hydrogels were freeze dried to form cryogel systems: CRG-CG, CRG-CNW-CG-3, CRG-CNW-CG-5, and CRG-CNW-CG-10.The analysis of the specific surface area of the formed cryogels  shows th2 from the samples. SEM analysis of the cryogel surface morphology revealed a change in the structural organization of the CRG cryogel upon introduction of CNWs. The interaction of positively charged CNWs and negatively charged CRG chains resulted in the formation of more porous structures  and CRG-CNW-CG-10 (discrete gel), and loaded them with MET. As a result, the following samples were obtained: MET-CRG-CG, MET-CRG-CNW-CG-5, MET-CRG-CNW-CG-10. The content of MET in the resulting samples was calculated according to the Equation (1) and presented in To properly study drug release from vaginal delivery systems, the physiological conditions of the vaginal environment should be considered. The vaginal fluid contains several antimicrobial substances, including lysozyme, lactoferrin, fibronectin, and polyamines, as well as carbohydrates from epithelial glycogen, amino acids, aliphatic acids, and proteins . In addiThe study of MET release kinetics in vitro has shown that the rate and pattern of release depend on the amount of CNWs introduced, which determines the structure of the hydrogel/cryogel . In contTo evaluate the mechanism of MET release from the prepared polymeric carriers, the cumulative release curves were linearized according to the following mathematical models: zero-order kinetics (Equation (4)), first-order kinetics (Equation (5)), Higuchi (Equation (6)), Korsmeyer–Peppas (Equation (7)), and Peppas–Sahlin (Equation (8)) ,46,47,48n > 1). The introduction of 5% CNWs into the CRG resulted in a cryogel with a drug release mechanism following Case II transport/zero-order kinetics (n = 1). In this case, the prolongation of MET release was due to swelling of the polymer matrix/relaxation of the macrochains. A further increase in CNW content of up to 10% and destruction of the structured gel matrix increased the effect of relaxation on drug release (n = 1.1). The results are consistent with the Peppas–Sahlin model, which showed that polymer swelling and relaxation are the most important factors determining the pattern of MET release (Kr > Kd).The kinetics of MET release from cryogels were best described by the Korsmeyer–Peppas and Peppas–Sahlin models . AccordiThereby, the process of MET release from the developed systems can be represented as follows: diffusion of the solvent into the matrix, swelling, and formation of a gel layer in which the drug is dissolved and then released.T. vaginalis).Based on the studied release kinetics and structural data, MET-CRG-CNW-CG-5 was selected for testing pharmacological activity against the trichomoniasis pathogen . As a result, after 24 h of pre-incubation of MET-CRG-CNW-5 in TYM, the level of residual antiprotozoal activity was 21.7%, i.e., 2.0 ± 0.7 a.u. RAA against MET-resistant T. vaginalis ATCC 50143; this fact indicates the high pharmacological potential of this cryogel.For example, after 4 h of pre-incubation in TYM, the residual antiprotozoal activity of MET-CRG-CNW-5 was determined to be 83.4% of the initial level, which in absolute terms was 7.8 ± 1.5 a.u. RAA against T. vaginalis ATCC 50143 treated with cryogels preincubated in TYM , mildly resistant ; resistant . Despite differences in the susceptibility of clinical Trichomonas isolates to MET, the residual antiprotozoal activity of MET-CRG-CNW-5 remained at a level sufficient to kill all clinical isolates within 24 h  of the antiprotozoal activity of MET-CRG-CNW-5 against clinical isolates of In this work, we prepared a cryogel polymeric carrier based on CRG and CNWs for application as a vaginal delivery system of MET with modified release.First, we obtained CRG-CNW hydrogels formed by various interactions between CRG macrochains and CNWs: electrostatic interactions in the formation of polyelectrolyte complexes, hydrogen bonding, and entanglement of CRG macromolecules. We showed the effect of CNW concentration on the rheological properties of the formed hydrogels; the strength of CRG hydrogels increased when the CNW content was increased to 5%  and then dramatically decreased when 10% CNWs were added .Second, we prepared cryogels based on the developed hydrogel compositions and tested their properties as a function of CNW content. We found that the CNW-containing cryogels were more porous structures in contrast to the CNW-free system, and the study of the specific surface area confirmed that increasing the CNW content led to the disintegration of the cryogel structure with the formation of a discrete polymer system.Third, we loaded the developed formulations with the antiprotozoal drug MET and investigated the profile and mechanism of MET release. At the target vaginal pH of 4.2, sustained release of MET occurred in 24 and 10 h for 5% and 10% formulations, respectively. We demonstrated that the nature of the cryogel structure, mediated by the CNW content, influenced the rate and type of drug release. The release curves correlated well with the Korsmeyer–Peppas and Peppas–Sahlin models. The prolonged-release mechanism is mediated by polymer swelling and chain relaxation in the polymer matrix, which makes it possible to program the MET release rate by varying the rheological properties of the hydrogels (viscosity and relaxation time) by changing the CNW content.T. vaginalis. We found that MET-CRG-CNW-5 has a long-term (24 h) antiprotozoal effect against Trichomonas, including those resistant to MET. These results indicate that cryogels such as MET-CRG-CNW can be used as a basis for the development of new agents for the treatment of infections caused by pathogens with varying degrees of drug resistance.Finally, we evaluated the pharmacological activity of the developed cryogels against We suggest that MET-CRG-CNW cryogels could be considered as promising dosage forms for the treatment of vaginal infections.


Background: We developed a simplified IVF culture system (SCS) which has proven to be effective and safe in a selected IVF cohort. Methods: Preterm birth (PTB) and low birth weight (LBW) of 175 singletons born after using the SCS, 104 after fresh embryo transfer (ET), and 71 after frozen embryo transfer, were compared with all singletons born in Flanders between 2012 and 2020 conceived after natural conception, ovarian stimulation (OS), and assisted reproduction (IVF/ICSI). Findings: The proportion of preterm (<37 weeks) births was significantly higher in the case of IVF or ICSI, followed by hormonal treatment, compared to spontaneous pregnancies. There was no significant difference in PTB between SCS and any of the other groups. Concerning the average birth weight we found no significant difference between singletons born after natural conception and SCS. However, a significant difference in average birth weight was found between SCS singletons and singletons born after IVF, ICSI and hormonal treatment, with a significantly higher birth weight in the SCS group. This difference was also observed in the proportion of babies weighing less than 2500 g, with significantly more LBW babies in the IVF and ICSI group compared to the SCS newborns. Interpretation: Taking into account the small series, PTB and LBW rates in SCS singletons were found to be comparable with singletons born after natural conception. Compared to babies born after ovarian stimulation and IVF/ICSI, SCS singletons had a lower PTB and LBW rates, although the differences were not significant for PTB. Our results confirm previous reports on reassuring perinatal outcomes after using the SCS technology. With the birth of the first baby conceived by in vitro fertilisation in 1978 ,3,4,5. TAs part of the Walking Egg Project, a simplified, low-cost IVF culture system (SCS) was evolved, which turned out to be less expensive but equally effective and safe when compared to regular IVF and ICSI for a selected group of infertile couples with no severe male factor involved .The effectiveness of this method was studied in a prospective cohort study showing similar ongoing pregnancy and implantation rates for SCS when compared to ICSI followed by regular culturing and using sibling oocytes . In the Preterm birth and low birth weight are crucial issues when dealing with neonatal outcomes. The complications of PTB and LBW are leading causes of under-5 child mortality, serious morbidity, and long hospital admissions globally ,11. PretAlmost all studies describe a worse perinatal outcome after using IVF/ICSI when compared to natural conception, especially after the transfer of fresh embryos. In most studies, the preterm birth rate (PTB) for singletons born after IVF and/or ICSI procedures varies between 8.6 and 14.9%, and low birth weight (LBW) has been reported to vary between 5.9 and 7.0% ,16,17,18For naturally conceived singletons, a lower prevalence of PTB (5.9–6.4%) and LBW (4.4–5.8%) has been reported ,13,15.Several reports show that singletons born after the transfer of frozen embryos have a lower risk for PTB and LBW and a higher risk of being large for their gestational age (LGA) when compared to singletons born after the transfer of fresh embryos ,19,20. CIn a recent report and making use of BELRAP data (Belgian Register for Assisted Procreation), we found that for babies born after a fresh embryo transfer (ET), the prevalence of PTB and LBW was significantly lower in SCS singletons when compared to all singletons born after conventional IVF in Belgium during the same study period . BecauseTherefore, we compared the prevalence of LBW and PTB of 175 SCS singleton babies born after the transfer of fresh and frozen embryos with perinatal outcome results of all babies born in Flanders, the northern Dutch-speaking part of Belgium, between 2012 and 2020 , whether conceived naturally, after using hormonal stimulation or assisted reproduction (IVF related procedures).2 with considerable pressure that passes into the second culture tube containing the single-step culture medium. The atmosphere created with respect to CO2 and O2 concentration develops over several hours at 37 °C, and once the culture tube is disconnected, a stable pH between 7.29 and 7.35 is created. The conditions created remain unchanged with storage at normal refrigeration temperatures (10–15 °C). The atmosphere and pH in the stored tubes are stable for prolonged periods such that the culture medium could be equilibrated in advance of an IVF cycle. As SCS is an enclosed IVF system relaying on a self-contained, air-tight closed environment, changes in temperature, atmosphere or pH that could have adverse effects on developmental embryos are almost eliminated. The SCS system is consistent with human IVF, provided that the tube is kept at 37 °C during fertilisation and further embryo culture. Only a uniform temperature is necessary, which can be maintained quite simply and without the need for conventional and expensive incubators.A detailed description of the simplified IVF culture system was described previously . BrieflyThe reader is referred to the papers describing the SCS system in more detail ,8.Due to the nature of the SPE registration, it was impossible to distinguish between babies born after transfer of fresh or frozen embryos. Therefore, we compared our 175 SCS babies (fresh and frozen) with all registered SPE singletons born in Flanders between 2012 and 2020, excluding the SPE-registered SCS babies.All SCS singletons are part of a prospective study performed at the ZOL Hospitals in Genk, Belgium. The methodology of this study was described before ,9. All wAll babies born after the transfer of SCS fresh and cryo/thawing embryos were prospectively studied. Three months after the expected day of delivery, patients were contacted by phone from our study nurse to inquire about obstetrical and perinatal outcomes of the newborn(s). The data obtained by phone were always compared with the information we received from the obstetrical unit where the delivery took place and adjusted if necessary.In our prospective cohort study, we only observed 11 twin pregnancies (22 babies), and for this reason, only the 175 singletons were examined and statistically compared with the SPE-registered singletons.www.zorg-en-gezondheid.be) systematically collects data on the medical, obstetrical, and perinatal events of each birth in Flanders of babies weighing ≥500 g birth weight, or gestational age ≥22 weeks if birth weight is unknown. The registry captures individual-level data abstracted from birth certificates and from obstetrical and neonatal medical records in the early postnatal and neonatal periods. Data are subjected to an error detection program, and checked for accuracy and completeness with extreme (considered outliers) or missing values confirmed with individual maternity units where appropriate. Correction and completion are ensured by telephone calls, additional data correction forms sent to midwives, obstetricians, and paediatricians, and visits to local departments if necessary. This results in a yearly global report and individual report for each centre. These data include pregnancies following natural conception, after assisted reproduction, including ovarian stimulation with or without IUI (Intrauterine Insemination) and IVF/ICSI procedures.Since 1986 the SPE perinatal registry  account for 48% of the registered births and are collected by the Centre d’Épidémiologie Périnatale (CEpiP).The SPE registers characteristics of the mother , the childbirth (type of onset of labour and mode of delivery), the child  and more. In this particular study, we only use the mother’s age, the child’s gender, gestational age and birth weight, and the mode of conception .The prospective non-inferiority clinical trial comparing the effectiveness and perinatal outcome of SCS versus IVF/ICSI treatment cycles was approved by the Ethical Committees of Genk and the Free University of Brussels  and registered as B.U.N. 143201110348. The analysis comparing the cycle and perinatal outcome parameters of 175 SCS singletons and all singletons in Flanders during the period 2012–2020 was approved by the scientific committee of the SPE. The scientific committee granted approval for the analysis of the anonymised data. This study was exempt from approval by an Institutional Review Board because data were used for scientific purposes only.t-test statistic, as equal variances could not be assumed. Scheffe and Tukey were used as post hoc tests. We performed two logistic regressions, one for each outcome variable of interest (low birth weight and preterm birth), with all predictors included in each model. The predictors included the child’s gender, mother’s age, and gestational age or weight at birth as continuous variables, depending on the outcome variable. To ensure there was no multicollinearity, we checked the correlations between all variables, which were found to be below 0.60. The mode of conception was added as a moderator in all possible associations, but no significant results were found (p > 0.05), which is why only the main effects are reported in the results section. Finally, the odds ratios were calculated with their 95% confidence intervals (CI).Data were imported into SPSS27 for initial data cleaning and data manipulation. All statistical analyses were conducted with R software version 4.0.3. Simple descriptive statistics were analysed, and group differences in the variable of interest were computed using a (post hoc) chi-square test. Continuous variables were compared by using a one-way ANOVA. Levene’s Test was used to check for homogeneity of variance, which led to the use of Welch’s As shown in No significant difference in the average gestational age was observed for the SCS group with any of the other groups, except IVF, which had a significantly lower average gestational age compared to SCS. When compared to spontaneous pregnancies, the proportion of preterm (<37 weeks) births was significantly higher in the case of hormonal treatment, followed by IVF and ICSI. There was no significant difference in the proportion of PTB between SCS and any of the other groups.No significant difference was found between spontaneous pregnancies, hormonal treatment, IVF, and ICSI concerning the average birth weight. A significant difference in average birth weight was found between SCS and IVF, ICSI and hormonal treatment, with a significantly higher average birth weight in the SCS group. There was no significant difference in the proportion of babies weighing less than 2500 g between SCS and any of the other groups. However, spontaneous pregnancies and hormonal treatments had a significantly lower proportion of newborns that weighed less than 2500 g compared to IVF or ICSI.p< 0.05  with p< 0.05 . The risn = 1 each) in the SCS group. Therefore, we did not further investigate these variables as potential outcomes in our study. Nonetheless, we wish to report these cases as part of our findings.We noted one perinatal mortality after fresh embryo transfer caused by an abruption placenta at 28 weeks gestation. The baby died immediately after birth; no clear reason could be found. We also observed one congenital malformation after the transfer of a cryo-thawed embryo. Although both cases were included in our sample, we were unable to conduct a reliable statistical analysis on these variables due to their limited occurrence  ,7. It tuIn addition to its effectiveness, safety is the most important issue when introducing a new method of assisted reproduction. Previous reports have shown that the adverse outcome for babies born after using assisted reproductive technologies cannot be explained solely by the high prevalence of multiples. Additionally, singletons born after ART are more likely to have a poorer perinatal outcome when compared to naturally conceived singletons ,27,28,29We recently reported a favourable perinatal outcome for SCS singletons born after fresh embryo transfer when compared to singletons born after conventional IVF in Belgium . The lowIn this study comparing perinatal parameters between SCS singletons and all singletons born in Flanders during the same study period, it turned out that singletons born after using SCS, contrary to IVF and ICSI, did not differ from singletons born after natural conception in its proportion of preterm births, low birth weight rate and average birth weight. We also observed a significantly lower average birth weight for newborns born after hormonal stimulation and IVF/ICSI when compared to SCS babies. Above this, significantly more children weighing under 2500 g were observed in the IVF or ICSI group compared to SCS. Considering the safety of the simplified IVF culture system, the reassuring results obtained in this study confirm the findings of our previous report in which we also found significantly less babies weighing below 2500 g when compared to all IVF babies born in Belgium during the same study period .To explain this low prevalence of preterm birth and low birth weight for SCS babies, very similar to the figures obtained for naturally conceived babies, we postulate that our enclosed SCS environment closely imitates natural conception with temperature control as the only variable. Epigenetic reprogramming is susceptible to environmental changes and non-physiological conditions, such as those applied during in vitro culture, including changes in pH and temperature, oxygen tension, intracytoplasmic sperm injection, and cryopreservation of embryos ,31. It iA major strength of this study is that all SCS data were registered prospectively with a meticulous follow-up. Historically, SPE data are also considered very reliable, with regular feedback about missing data, errors, and inconsistencies.The most important limitation of this study is the low number of SCS babies when compared to the large group of SPE-registered singletons. Therefore, the results of this study should be interpreted with caution as the different groups (mode of conception) are very different. The absence of significant differences between the SCS group and the other groups is not surprising because the sample size is only 175. The lack of significant differences observed in this study is more likely due to a lack of sensitiveness (too different sample sizes) than due to a real non-difference. To address this limitation, we suggest using bootstrapping as a resampling technique to estimate the variability and distribution of the statistical parameters of interest, which could improve the reliability and validity of the analysis.Although we adjusted for females’ age and gender of the newborn when comparing the different groups, many residual confounding factors cannot be excluded, in particular, related to duration and cause of infertility or whether fresh or frozen embryos were transferred. All these parameters are important when studying perinatal outcomes, but we could not adjust for the parameters because these data were not available in the SPE registration, another limitation of this study. We recommend incorporating additional confounding factors in future analysis, such as the mother’s BMI, presence of hypertension and/or diabetes, and socio-demographic variables, all of which are available in the SPE registration.On the other hand, our first aim was to compare our SCS singletons with a large series of naturally conceived singletons, being aware of the lack of some important data in both registries and the small number of SCS newborns. Taking into account all these limitations, we still observed a low PTB and LBW for SCS singletons, similar to figures published for singletons born after natural conception.Due to the unexpectedly low prevalence of PTB and LBW for SCS babies reported in a previous study, we aimed to compare the perinatal outcome of SCS singletons and a large cohort of naturally conceived (NC) singletons. Our results show comparable PTB and LBW rates for SCS and NC singletons. This strengthens our belief that the SCS-IVF technology can be used as a safe method with an excellent perinatal outcome. Because of the low costs associated with SCS, it opens perspectives to increase the general applicability of assisted reproductive techniques in areas where conventional IVF laboratory costs are the limiting factor to have access to complex fertility treatments.


The purpose of this study was to construct a preterm birth prediction model based on electronic health records and to provide a reference for preterm birth prediction in the future.This was a cross-sectional design. The risk factors for the outcomes of preterm birth were assessed by multifactor logistic regression analysis. In this study, a logical regression model, decision tree, Naive Bayes, support vector machine, and AdaBoost are used to construct the prediction model. Accuracy, recall, precision, F1 value, and receiver operating characteristic curve, were used to evaluate the prediction performance of the model, and the clinical application of the model was verified.A total of 5411 participants were included and were used for model construction. AdaBoost model has the best prediction ability among the five models. The accuracy of the model for the prediction of “non-preterm birth” was the highest, reaching 100%, and that of “preterm birth” was 72.73%.By constructing a preterm birth prediction model based on electronic health records, we believe that machine algorithms have great potential for preterm birth identification. However, more relevant studies are needed before its application in the clinic. Preterm birth, defined as a live baby born before 37 weeks of completed gestation, is the main factor in 35% of infant mortality , 2. EachNo standardized criteria have been developed regarding the prediction of preterm birth due to the complexity of the mechanism . CurrentAn ongoing trend is to carry out an initial assessment of patient prognosis based on electronic health records (EHRs). EHRs are large, readily available, real-time databases that can be used to predict diseases , and theMachine learning, a branch of artificial intelligence, can prospectively predict patient clinical outcomes by extracting information from data, and has been increasingly used in the clinical settings in recent years . CurrentTherefore, in this research, relevant information was extracted from the EHRs for the construction of a preterm birth prediction model. By constructing the model and interpreting its internals, a reference is provided for the development of a preterm birth prediction model that can be put into clinical practice.This study used a retrospective cohort study design. The dataset comprised information on deliveries at the Second Hospital of Wenzhou Medical University from October 2019 to December 2020. To ensure data availability, mothers without complete medical records were excluded. Because of the lack of a specific sample size, which could lead to bias in the results, women with systemic lupus erythematosus and psychiatric disorders and those who delivered malformed fetuses or stillborn fetus were excluded, although these may also be influential factors in preterm birth to some extent. This study was approved by the Ethics Committee of Wenzhou Medical University . As a retrospective study, individual data were processed in this article in an anonymous way, and each piece of data remains unlinked to any individual, therefore consent was waived for this study with the permission of Wenzhou Medical University.Through literature review and expert group meetings, 21 factors that may be risk factors of preterm birth were preliminaries identified, among which parity and number of neck loops were numerical variables, parturient woman age, abortion, placenta previa, preterm premature rupture of membranes, hypertension, diabetes, anemia, multiple pregnancies, abnormal fetal position, prenatal hemorrhage, uterine abnormalities, scar uterus, pregnancy complicated with uterine fibroids, fetal distress, colonization of streptococcus agalactiae, amniotic fluid traits, abnormal amniotic fluid volume, assisted pregnancy and fetal weight abnormality were classified variables. The above data were extracted from the electronic health record system, and the modeling variables were screened by unconditional multi-factor logistic regression.In this study, the dataset contained 119,042 records, and the number of deletion cases was 12 . Parity and number of neck loops are continuous numerical data, which can be used for curve fitting. The values on the curve were selected by random number table method to fill the missing values. As the number of missing cases of hypertension is too small and the interpolation fitting effect is not good, it was deleted.The dependent variable was the outcome of preterm birth and was dichotomized into binary outcomes as 0 and 1. Thus, preterm birth was recorded as 1, and non-preterm birth was recorded as 0.In this study, the non-conditional logistic regression method was used to select statistically significant independent variables by univariate and multivariate analysis; the outcome of labor was the dependent variable, and 21 suspected factors of preterm birth were independent variables. The variables with statistical significance (P < 0.10) were analyzed by non-conditional multivariate analysis, and those with statistical significance (P < 0.10) in the non-conditional multivariate analysis were included as modeling variables  of a receiver operating characteristic (ROC) curve, sensitivity, specificity, and F1 score were used to evaluate the performance of the model.After comparing the five models, the prediction model with the best performance was chosen for external verification. The data from pregnant women who gave birth in the same hospital from November to and December 2020 were collected. The difference between the actual clinical outcome of premature birth and the decision-making result of the predictive model was comparable, expressed as the accuracy (%).SPSS 22.0 was used for data analysis. The 21 influencing factors and the outcome of premature birth were analyzed by logistic single factor analysis. According to the single factor analysis results, a stepwise regression method was utilized to screen the variables; P < 0.1 was regarded as statistically significant. Establish and verify different types of machine learning algorithms built on MATLAB.Totally, the dataset contained 5943 participants between October 2019 and October 2020. After excluding 429 participants with incomplete medical history information, 6 participants with systemic lupus erythematosus, 19 participants with a history of mental disorders, and 78 participants with terminated pregnancies due to fetal malformations or stillbirths, a total of 5411 participants (633 preterm births and 4778 non-preterm births) were included and used for model construction . The independent variables with statistical significance in univariate analysis were included as predicting factors in the multi-factor unconditional logistic stepwise regression analysis, and finally eleven variables were statistically significant (P < 0.10), , which is higher than the previous average of 0.84 for preterm birth prediction models . Among tInvestigation of factors influencing preterm birth is an essential foundation for predicting preterm birth. There are inextricable links between the influencing factors, which makes it harder to elucidate the specific mechanisms by which preterm birth occurs in individuals. In this study, based on variable importance from the AdaBoost, major determinants of preterm birth are preterm premature rupture of membranes, multiple pregnancies, placenta previa, prenatal hemorrhage, colonization of streptococcus agalactiae, scar uterus, abnormal fetal position, fetal weight abnormality, amniotic fluid traits, parturient woman age and diabetes.Preterm premature rupture of membranes had the highest weight of influencing preterm birth prediction. According to research, only about 8% of women with preterm premature rupture of membranes are able to heal . The rupConsistent with previous studies, multiple pregnancies appear to be another important influencing factor for predicting preterm birth . It has Placenta praevia and prenatal hemorrhage are also relatively important factors in predicting preterm labor. Prenatal hemorrhage can be caused by a variety of factors, such as injury, excessive oxytocin, obstructed labor, scarred uterus, multiple pregnancies, excessive amniotic fluid, fetal malposition, etc. However, the most common causes of prenatal hemorrhage in late pregnancy are placental abruption and placenta praevia . In womeBased on the results of AdaBoost in this study, it is imperative to remain vigilant regarding preterm premature rupture of membranes, placenta praevia, and prenatal hemorrhage. Pregnant women should be educated about identifying relevant risk symptoms to ensure timely medical assistance when early symptoms arise, thereby minimizing the likelihood of adverse pregnancy outcomes. Furthermore, greater attention needs to be devoted to managing the health of women with multiple pregnancies. They should adhere strictly to healthy diets and engage in moderate exercise in order to effectively control weight so as to prevent uterine overextension, which could potentially increase the risk of adverse pregnancy outcomes.Previously, researchers have developed machine learning-based models for predicting preterm birth. For instance, T Khatibi, N Kheyrikoochaksarayee and MM Sepehri  construcThe practical implementation of this model requires the integration of various resources and still faces challenges. Data based solely on EHRs inevitably mask characteristics that are prone to temporal fluctuations, such as blood glucose and blood pressure. This may explain the insignificant effect of blood pressure on preterm labor and the relatively low ranking of the importance of blood glucose in this study. Therefore, in practical clinical applications, healthcare professionals can leverage the convenience of the Internet or telephone to regularly collect and update dynamic maternal information, thereby enriching the data source of predictive models. Furthermore, establishing an e-platform based on the preterm labor prediction model would facilitate seamless information exchange and data collection. Pregnant women could timely upload and update relevant indicators while healthcare professionals integrate these new inputs with medical records to enhance preterm labor prediction using this model. However, it is important to acknowledge that despite its utility, this current model may still exhibit inaccuracies in predicting a small subset of cases; hence further refinement through collaboration with experienced physicians is warranted.One limitation of this study is the potential for retrospective leakage, as it relies heavily on EHRs. The reliability of EHRs is constrained by the frequency of maternal contact with the hospital, such as prenatal check-ups and hospitalizations, which may introduce irregularity in data sources. Additionally, variations in recording methods and data accuracy due to different recording personnel involved could lead to biased results and undermine the representativeness of the data in reflecting true clinical situations. Our current model lacks specification regarding its predictive accuracy for individual preterm births at different stages of pregnancy since it combines comprehensive data across an entire period of pregnancy. It is challenging to ensure that a model constructed based on such extensive data would exhibit consistent predictive accuracy in practical applications. Furthermore, using external validation derived from similar EHRs limits the generalizability and discriminatory validity when applying our model to other EHR systems. Given these limitations inherent in retrospective studies, future research should focus on prospective studies to further explore the application of machine learning techniques in predicting preterm birth.In this study, relevant information was extracted from the EHRs and used to construct a machine learning model for preterm birth prediction. All 21 parameters entered in our model can be obtained from EHRs. The model further confirms that preterm premature rupture of membranes, multiple pregnancies, placenta previa, prenatal hemorrhage, and colonization of streptococcus agalactiae are high-risk factors for preterm birth. However, possibly due to an imbalance in the number of preterm and non-preterm births in the dataset, 30% of actual preterm births were incorrectly assessed as non-preterm births by our model during external validation. We believe that these types of errors can be addressed by collecting data from multiple centers with larger data sizes. In a word, the feasibility of using machine learning techniques to predict preterm birth has been explored, yet before its real application in clinical settings, more expertise is needed and more scholars need to exploit a more systematic application model.


Despite the availability of exempted family planning services, a significant proportion of women in African countries continue to experience inadequately spaced pregnancies. To the authors’ knowledge, evidence of suboptimal birth intervals at the SSA level is lacking and previous studies have been limited to specific geographic area. Therefore, this analysis was aimed to estimate the pooled prevalence of suboptimal birth spacing and its predictors among childbearing women in SSA.Pooled DHS data from 35 SSA countries were used and a weighted sample of 221,098 reproductive-age women was considered in the analysis. The survey across all countries employed a cross-sectional study design and collected data on basic sociodemographic characteristics and different health indicators. Forest plot was used to present the overall and country-level prevalence of suboptimal birth spacing. Multilevel mixed-effects models with robust Poisson regression were fitted to identify the predictors of suboptimal birth spacing. Akaike’s and Bayesian information criteria and deviance were used to compare the models. In a multivariable regression model, a p-value less than 0.05 and an adjusted prevalence ratio with the corresponding 95% CI were used to assess the statistical significance of the explanatory variables.The pooled prevalence of suboptimal birth spacing among women in SSA was 43.91% (43.71%-44.11%), with South Africa having the lowest prevalence (23.25%) and Chad having the highest (59.28%). It was also found that 14 of the 35 countries had a prevalence above the average for SSA. Rural residence [APR (95% CI) = 1.10 (1.12–1.15)], non-exposure to media [APR (95% CI) = 1.08 (1.07–1.11)], younger maternal age [APR (95% CI) = 2.05 (2.01–2.09)], non-use of contraception [APR (95% CI) = 1.18 (1.16–1.20)], unmet need for family planning [APR (95% CI) = 1.04 (1.03–1.06)], higher birth order [APR (95% CI) = 1.31 (1.28–1.34)], and desire to have at least six children [APR (95% CI) = 1.14 (1.13–1.16)] were the predictors of suboptimal birth spacing practice.More than four out of ten reproductive-age women in SSA countries gave birth to a subsequent child earlier than the recommended birth spacing, with considerable variations across the countries. Thus, interventions designed at enhancing optimal birth spacing should pay particular attention to young and socioeconomically disadvantaged women and those residing in rural regions. Strengthening community health programs and improving accessibility and availabilities of fertility control methods that ultimately impacts optimal reproductive behaviors is crucial to address contraceptive utilization and unmet need. World Health Organization (WHO) defines a suboptimal birth interval as a duration of less than 33 months between two consecutive live births . Proper In addition to adverse neonatal outcomes, closely spaced pregnancies have shown to have a significant effect on maternal health, particularly during pregnancy and childbirth. It has been revealed that women with shorter birth intervals are at a higher risk of preeclampsia , 6, anemPrevious studies in different settings have reported a varying level of suboptimal birth spacing practice among reproductive-age women. Studies based on single-country data revealed that 23%, 26%, 47%, 49%, and 50% of reproductive-age women in Pakistan , BangladGlobally, the implementation of family planning program has witnessed a promising improvement in maternal and child survival in the multidimensional aspects, mainly by avoiding the risk of unintended and closely spaced pregnancies and its associated complication , 20. In However, despite the availability of exempted family planning services, a significant proportion of women in African countries continue to experience inadequately timed and spaced pregnancies , 18, 23 DHS data of 35 sub-Saharan African countries were used in the present analysis. Countries were selected based on the survey year, availability of a standardized and unrestricted dataset, and presence of observations on the outcome variable in the datasets. For the current analysis, we included the countries that have their recent DHS conducted between 2010 and 2021. The survey across all countries employed a cross-sectional study design and collected data on basic sociodemographic characteristics and different health indicators.https://dhsprogram.com/Methodology/index.cfm).All surveys used a multistage stratified cluster sampling technique to select the study participants. First, each country was divided into clusters, and clusters were randomly selected based on the probability proportional to their contribution to the overall country’s population. In the second stage, using the housing census as a sampling frame, a representative number of households was selected from each cluster. Survey data were collected using a standardized tool and face-to-face interviews. In the DHS, data on birth interval was collected by asking women about the interval between their last two successive live births. Thus, we have considered women of reproductive age who have had at least two births (second and higher order births) and those who had data on birth interval. On the contrary, women who were nulliparous, primiparous, had an abortion in between two live births, or had missing data for the birth interval variable were excluded from the study. For the current analysis, we used the women’s dataset (IR dataset), and a weighted sample of 221,098 reproductive-age women who had at least two successive live births was included in the final analysis. Details about DHS methodology can be accessed at , a model with individual-level explanatory variables (model II), a model with community-level explanatory variables (model III), and a model with both individual and community-level predictors (model IV). Akaike’s information criteria (AIC), Bayesian information criteria (BIC), Log-likelihood (LL), and deviance (i.e. -2*LL) values were used for model comparison. Model IV was selected as the best-fitted model since it had the lowest values on all three comparison parameters . Random variability in suboptimal birth spacing practice among reproductive-age women across clusters was examined with random effect parameters like intra-class correlation coefficient (ICC), proportion change in variance (PCV), and median odds ratio (MOR). Collinearity diagnostic was assessed using variance inflation factor (VIF) and the VIF values for the variables included in the final regression analysis were less than five, suggesting that there was no significant multi-collinearity. In the final multivariable analysis, a p-value less than 0.05 and an adjusted prevalence ratio with the corresponding 95% confidence interval was used to identify the predictors of suboptimal birth spacing Table .Table 1SWe used publicly accessible DHS dataset and the survey procedures were approved by the ICF Institutional Review Board (IRB) and the host country IRB during the initial data collection. We have received permission to access the data from ICF International via online request.Of 221,098 reproductive-age women included in the analysis, 149,109 (67%) resided in rural dwellings, 86,073 (40%) perceived distance to the nearest health facility as a big problem, and 96,921 (44%) were between the ages of 25 and 34 years. Nearly half of the participants (49%) were married before the age of 18 and 53,361 (24%) were in a polygamous union. About two-thirds of women (65%) had exposure to mass media, 92,820 (43%) had no formal schooling, and 188,561 (85%) lived in male-headed households. Approximately half (51%) of women had ever used contraceptives and less than one-fourth (23%) of them had unmet family planning needs.Our analysis also revealed that the proportion of suboptimal birth spacing was 46%, 47%, and 62% among women who resided in rural settings, those who did not participate in the decision to use healthcare services, and aged 15–24 years, respectively. Furthermore, suboptimal birth interval was relatively higher among women with no formal education (48%) compared to those who attended higher education (39%) , with South Africa having the lowest prevalence 23.26%) and Chad having the highest (59.28%). In addition, it was revealed that 14 of 35 countries included in the analysis had a prevalence greater than the average for SSA (43.91%) and six of these countries were from the Western African region , the ICC value indicated that about 14% of variation in suboptimal birth spacing practice was explained by differences across the clusters, while the remaining 86% was attributed to individual-level differences. In the final model, the values of explained variance also showed that about 35% of the total variation in shorter birth interval was attributed to the combined effect of individual and community-level factors. In addition, the presence of heterogeneity in the level of suboptimal birth interval across the clusters was indicated by the MOR of 1.90 and 1.58 in the null and full models, respectively. This shows that women living in the cluster with a higher prevalence of suboptimal birth interval had a 90% higher likelihood of experiencing closely spaced births compared to those in the clusters with a lower prevalence of shorter birth spacing. Model IV had the lowest AIC, BIC, and deviance values and was hence selected as the best-fitted model  = 1.10 (1.12–1.15)] and those who did not have media exposure [APR (95% CI) = 1.08 (1.07–1.11)] had a higher prevalence of suboptimal birth interval compared to their counterparts. Women aged 15–24 years [APR (95% CI) = 2.05 (2.01–2.09)] and 25–34 years [APR (95% CI) = 1.31 (1.29–1.33)], and those with no formal education [APR (95%) = 1.04 (1.02–1.06)] and primary level education [APR (95% CI = 1.02 (1.01–1.04)] had a greater risk of having shorter birth interval than their reference groups. Compared to women in the richer households, those from families with middle [APR (95% CI) = 1.03 (1.01–1.05)] and poor [APR (95%) = 1.06 (1.05–1.08)] wealth indexes had a higher prevalence of suboptimal birth interval.Furthermore, our result showed that women who had never used contraception [APR (95% CI) = 1.18 (1.16–1.20)] and unmet family planning need [APR (95% CI) = 1.04 (1.03–1.06)] had an increased risk of suboptimal birth interval compared to those who ever used fertility control methods and those who did not have an unmet contraceptive need, respectively. Additionally, the likelihood of experiencing suboptimal birth interval was significantly greater for women with higher birth order [APR (95% CI) = 1.31 (1.28–1.34)] and those who desire to have six children or more [APR (95% CI) = 1.14 (1.13–1.16)] , with considerable within-country variations from 23.3% in South Africa to 59.3% in Chad. The level of suboptimal birth spacing practice observed in this study is higher than the prevalence reported from the studies conducted in Pakistan (23%) , BangladThe result of a multilevel robust Poisson regression analysis showed that suboptimal birth spacing practice was significantly influenced by different socio-demographic and reproductive characteristics. For instance, compared to women who resided in urban settings, rural women had a higher prevalence of shorter birth spacing, which is consistent with the findings of the previous studies , 24, 26.The current study also showed that shorter birth intervals were more likely to occur among younger mothers. Women between the ages of 25 and 34 years had a 31% higher prevalence of suboptimal birth interval than those aged 35 to 49 years, and the risk was increased by about two-fold for younger women 15–24 years). This finding is in agreement with the result of the previous studies that reported older maternal age as a protective factor for short birth spacing –24 years, 23, 34.Contraceptive utilization was also identified as a significant predictor of birth spacing among reproductive-age women. Compared to women who had a history of using contraceptives, women who had never used contraceptives were 18% more likely to experience a second birth after an interval of less than 33 months. The results of the earlier studies are in accordance with this finding , 25, 34,Consistent with the previous studies , 34, 40,Additionally, our analysis showed that compared to reproductive-age women who desired to have fewer children, women who wanted to have more than five children had a 14% increased risk of experiencing shorter birth intervals. Similarly, previous studies also reported a higher likelihood of shorter birth spacing with an increasing maternal desire to have more children , 24. ThiThe result of this study also revealed that women who did not have exposure to mass media were more likely to have a suboptimal birth spacing practice than those who had exposure to such information sources. This result is consistent with a previous study in Ethiopia that reported a 35% added odds of shorter birth interval among women unexposed to media . The mosThe use of a larger sample size, nationally representative data from 35 countries, and advanced statistical methods are the main strengths of this study. However, it is impossible to explain the causal relationship between the independent and dependent variables due to the cross-sectional nature of the survey’s design. There might also be a recall bias since women were asked about the events that took place five years or more preceding the survey.This study showed that more than four out of ten reproductive-age women in SSA countries gave birth to the subsequent child earlier than the recommended waiting time, with considerable variations in the level of this practice across the countries. The result also revealed that rural residency, younger maternal age, low husband education, non-exposure to mass media, poor household wealth, non-use of contraceptives, unmet family planning need, higher birth order, and ideal number of children were the significant predictors of suboptimal birth spacing. Therefore, interventions designed at enhancing optimal birth spacing should pay particular attention to young and socioeconomically disadvantaged women and those residing in rural regions. Establishing regular reproductive health education programs through mass media and outreach activities is also important to increase awareness of the ideal timing of pregnancies. Additionally, strengthening community health programs and improving accessibility and availabilities of fertility control methods that ultimately impacts optimal reproductive behaviors is crucial to address contraceptive utilization and unmet need.