Predictors Of Birth Weight Research Articles

Prediction and feature selection of low birth weight using machine learning algorithms

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual’s health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017–2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML model.MethodsTo pick out the key features, the Boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector Machine), NB (Naïve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model’s performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC value.ResultsResult shows, Boruta algorithm identifies eleven significant features including respondent’s age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating Boruta algorithm’s significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model’s respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy = 85.86%, F1 score = 0.9243, AUC = 0.549) outperformed the other methods. Both the classical (LR) and machine learning (ML) models’ performance has improved dramatically when important characteristics are extracted using the wrapper method. The LR method identified five significant features with a specificity, sensitivity, accuracy and F1 score of 0.87, 0.33, 87.12% and 0.9309. The region, whether the infant is a twin, and cesarean delivery were the three key features discovered by the DT and RF models, which were implemented using the wrapper technique. All three models had the identical F1 score of 0.9318. However, “child is twin” was recognized as a significant feature by the SVM, NB, and AB models, with an F1 score of 0.9315. Ultimately, with an F1 score of 0.9315, the XGBoost model recognized “child is twin” and “age at first sex” as relevant features. Random Forest again beat the other approaches in this instance.ConclusionsThe study reveals Wrapper method as the optimal feature selection technique. The ML method outperforms traditional methods, with Random Forest (RF) being the most effective predictive model for Low-Birth-Weight prediction. The study suggests that policymakers in Bangladesh can mitigate low birth weight newborns by considering identified risk factors.

Neonatal birthweight prediction using two- and three-dimensional estimated fetal weight among borderline small fetuses.

This study aimed (1) to determine the degree of correlation between 2D and 3D estimated fetal weight (EFW) and neonatal birth weight (BW) among borderline small fetuses and (2) to compare the accuracy and precision of 2D and 3D EFW in BW prediction. A retrospective cohort study evaluated fetuses who had an ultrasound performed between January 2017 and September 2021 at a tertiary maternal center. All singleton pregnancies with 3D EFW within 4 weeks of delivery were included. Fetuses with known structural or genetic abnormalities were excluded. Pearson's correlation coefficients were determined for both 2D and 3D EFW to BW then compared using Williams' test and Fisher r to z transformation, where applicable. Mean percent difference and standard deviation were used to assess the accuracy and precision, respectively, of 2D and 3D EFWs in BW prediction. Two hundred forty-eight pregnancies were included. Ultrasound studies were performed with a median interval of 2 weeks (IQR 1, 3) between ultrasound and delivery. Both 2D and 3D estimated fetal weights showed a significant correlation with birth weight (r = 0.74 and r = 0.73, respectively), indicating similar accuracy between the two techniques. Two-dimensional and three-dimensional EFWs performed similarly in the prediction of BW in borderline small fetuses.

Accurate birth weight prediction from fetal biometry using the Gompertz model

ObjectivesMonitoring of fetal growth and estimation of birth weight is of clinical importance. During pregnancy, ultrasound fetal biometry values including femur length, head circumference, abdominal circumference, biparietal diameter are measured and used to place fetuses on “growth charts”. There is no simple growth-model-based, predictive formula in use for fetal biometry. Estimation of fetal weight at birth currently depends on ultrasound data taken a short time before birth. Study designOur cohort (“Seethapathy cohort”) consists of ultrasound biometry measurements and other data for 774 pregnant women in Chennai, India, 2015–2017. We use the Gompertz model, a standard model for constrained growth, with just three intuitive parameters, to model the growth of fetal biometry, and a machine learning (ML) model trained on these parameters to predict birth weight (BW). ResultsThe Gompertz model convincingly fits the growth of fetal biometry values. Two Gompertz parameters—t0 (inflection time) and c (rate of decrease of growth rate)—seem universal to all fetuses, while the third, A, is an overall scale specific to each fetus, capturing individual variation. On the Seethapathy cohort we can infer A for each fetus from ultrasound data available by the 24 or 35 weeks. Our ML model predicts birth weight with < 8 % error, outperforming published methods that have access to late-term ultrasound data. The same model gives an 8.4 % error in BW prediction on an independent validation cohort of 365 women. ConclusionsThe Gompertz model fits fetal biometry growth and enables birth weight estimation without need of late-term ultrasounds. Aside from its clinical predictive value, we suggest its use for future growth standards, with almost all variation described by a single scale parameter A.

Evaluation of sonographic and clinical measures in early versus late third trimester for birth weight prediction.

To evaluate the optimal timing for fetal weight estimation during the third trimester. This retrospective cohort study involved fetal weight estimations from both early (28+0-36+6 weeks) and late (37+0 weeks and beyond) third trimester. These estimations were converted to predicted birth weights using the gestation-adjusted projection formula. Birth weight predictions were compared with actual birth weights, to identify the most effective timing for weight prediction. The study included 3549 cases, revealing mean percentage errors (MPE) of -3.69% for early sonographic assessments, -2.5% for late sonographic assessments, and -1.9% for late clinical assessments. A significant difference was found between early and late sonographic estimations (P < 0.001), whereas late sonographic and clinical assessments did not differ significantly (P = 0.771). Weight predictions for fetuses below the 10th and above the 90th centiles were less accurate than for those within the 10th-90th centiles (P < 0.001). In women with obesity, late clinical estimations were less precise (MPE of -5.85) compared with non-obese women (MPE of -1.66, P < 0.001). For women with diabetes, early sonographic estimations were more accurate (MPE of -1.31) compared with non-diabetic patients (MPE of -3.94, P < 0.001) though this difference did not persist later in pregnancy. Sonographic and clinical weight predictions in the late third trimester were more accurate than earlier third-trimester sonographic assessments, hence continuous follow up and assessments closer to term are important. In women with diabetes, no adjustments in weight prediction methods are necessary. Accurately predicting birth weights for abnormally small or large fetuses remains challenging, indicating the need for improved screening and diagnostic strategies.

Predicting the fetal weight by ultrasonography for isolated polyhydramnios: Comparison of 14 formulas.

To recalculate the estimated fetal weight (EFW) based on ultrasound measurements in patients complicated with isolated polyhydramnios, using 14 current formulas to observe which formula better predicts the EFW. This study examined pregnant women who gave birth in the hospital between January 2015 and January 2020. Maximum vertical pocket (MVP) was classified as, mild, moderate, and severe polyhydramnios, and the patients' measurements were reanalyzed using 14 formulas. The estimation of birth weight (EBW) alongside observed birth weight (OBW) facilitated the computation of statistical indices, namely the mean absolute percentage error (MAPE) expressed as [(EBW - OBW)/OBW × 100], the mean percentage error (MPE) denoted as (EBW - OBW)/(OBW × 100), and their corresponding 95% confidence intervals. A total of 564 polyhydramnios patients were included in the study. When looking at the MAPE, the lowest rate (7.65) was found in the Hadlock 2 formula. Hadlock 1, Hadlock 3, and Shinozuka formulas demonstrated MAPE values most closely aligned with Hadlock 2. Weiner I and Thurnau were the formulas with the highest MAPE values. When the cut-off values for MAPE were taken as 10%, 4/14 of the formulas (Weiner I-II, Vintzleos and Thurnau) gave results above 10%. Among 14 formulas, 3 (21.4%) had positive (sonographic overestimation) (Hadlock 3, Shinozuka, and Vintzleos) and the other 11 (78.6%) had negative MPE (sonographic underestimation). The Hadlock 2 formula had the lowest MAPE in predicting birth weight in patients with polyhydramnios, closely followed by the Hadlock 1, Hadlock 3, and Shinozuka formulas.

Development and validation of a prognostic model to predict birth weight: individual participant data meta-analysis

ObjectiveTo predict birth weight at various potential gestational ages of delivery based on data routinely available at the first antenatal visit.DesignIndividual participant data meta-analysis.Data sourcesIndividual participant data of four cohorts...

Fetal size monitoring in women with gestational diabetes and normal glucose tolerance.

To monitor fetal size and identify predictors for birthweight in women with gestational diabetes (GDM) and normal glucose tolerance (NGT). Cohort study of 1843 women universally screened for GDM, with routine ultrasounds each trimester. Women with GDM and NGT were categorized in subgroups by birthweight centile. Of the total cohort, 231 (12.5%) women were diagnosed with GDM. Fetal size, incidence of large-for-gestational age (LGA: 12.3% of GDM vs. 12.9% of NGT, p = 0.822) and small-for-gestational age (SGA) neonates (4.8% of GDM vs. 5.1% of NGT, p = 0.886) were similar between GDM and NGT. GDM women with LGA neonates were more insulin resistant at baseline and had more often estimated fetal weight (EFW) ≥ P90 on the 28-33 weeks ultrasound (p = 0.033) than those with AGA (appropriate-for-gestational age) neonates. Compared to NGT women with AGA neonates, those with LGA neonates were more often obese and multiparous, had higher fasting glycemia, a worse lipid profile, and higher insulin resistance between 24 -28 weeks, with more often excessive gestational weight gain. On the 28-33 weeks ultrasound, abdominal circumference ≥ P95 had a high positive predictive value for LGA neonates in GDM (100%), whereas, in both GDM and NGT, EFW ≥ P90 and ≤ P10 had a high negative predictive value for LGA and SGA neonates (> 88%), respectively. There were no differences in fetal size throughout pregnancy nor in LGA incidence between GDM and NGT women. EFW centile at 28-33 weeks correlated well with birthweight. This indicates that GDM treatment is effective and targeted ultrasound follow-up is useful. TRIAL REGISTRATION CLINICALTRIALS.GOV: NCT02036619. Registration date: January 15, 2014. https://clinicaltrials.gov/ct2/show/NCT02036619 .

Causal effects of genetic birth weight and gestational age on adult esophageal diseases: Mendelian randomization study.

Few studies have investigated the association between gestational age, birth weight, and esophageal cancer risk; however, causality remains debated. We aimed to establish causal links between genetic gestational age and birth weight traits and gastroesophageal reflux disease (GERD), Barrett's esophagus (BE), and esophageal adenocarcinoma (EA). Additionally, we explored if known risk factors mediate these links. To analyze of the relationship between gestational age, birth weight and GERD, BE, and EA. Genetic data on gestational age and birth weight (n = 84689 and 143677) from the Early Growth Genetics Consortium and outcomes for GERD (n = 467253), BE (n = 56429), and EA (n = 21271) from genome-wide association study served as instrumental variables. Mendelian randomization (MR) and mediation analyses were conducted using MR-Egger, weighted median, and inverse variance weighted methods. Robustness was ensured through heterogeneity, pleiotropy tests, and sensitivity analyses. Birth weight was negatively correlated with GERD and BE risk [odds ratio (OR) = 0.78; 95% confidence interval (CI): 0.69-0.8] and (OR = 0.75; 95%CI: 0.60-0.9), respectively, with no significant association with EA. No causal link was found between gestational age and outcomes. Birth weight was positively correlated with five risk factors: Educational attainment (OR = 1.15; 95%CI: 1.01-1.31), body mass index (OR = 1.06; 95%CI: 1.02-1.1), height (OR = 1.12; 95%CI: 1.06-1.19), weight (OR = 1.13; 95%CI: 1.10-1.1), and alcoholic drinks per week (OR = 1.03; 95%CI: 1.00-1.06). Mediation analysis showed educational attainment and height mediated the birth weight-BE link by 13.99% and 5.46%. Our study supports the protective role of genetically predicted birth weight against GERD, BE, and EA, independent of gestational age and partially mediated by educational attainment and height.

Peripartum Prediction of Fetal Weight in Gravidas With Obesity.

Estimated fetal weight (EFW) is an important metric at delivery as neonates with abnormal birthweight and their mothers are at higher risk of birth complications. Data regarding optimal EFW assessment in gravidas with obesity is inconsistent, and with the increasing incidence of obesity, clarification of this question is crucial. We aimed to compare accuracy of ultrasound (US)-derived EFW and clinical assessments of EFW in predicting neonatal birthweight among gravidas with obesity. This prospective cohort study enrolled gravidas with obesity and a singleton pregnancy admitted for delivery at term. EFW was determined using either US biometry or clinical assessment (Leopold's maneuvers, Johnson's formula, and Insler's formula) at time of admission. Our primary outcome was accurate EFW, defined as EFW within 500 g of birthweight. Secondary outcomes included ability to predict small-for-gestational age (SGA) and large-for-gestational age (LGA) birthweights. These outcomes were compared between all EFW methods. A total of 250 gravidas with a median body mass index of 36.4 kg/m2 were enrolled. Admission US outperformed Leopold's maneuvers in obtaining accurate EFW (81.6% versus 74.5%, P = .03). When comparing all methods, Johnson's and Insler's formulae performed the worst, accurately predicting EFW in only 27.4% and 14.3% of cases, respectively. Likewise, US-derived EFW outperformed Leopold's maneuvers and fundal height in the prediction of SGA and LGA neonates. US is more accurate than clinical assessment of EFW in gravidas with obesity both for estimation of actual birthweight and prediction of abnormal birthweight. Universal late third-trimester or peripartum US for EFW should be considered in gravidas with obesity.

No causal associations of genetically predicted birth weight and life course BMI with thyroid function and diseases.

Observational studies have suggested associations of birth weight, childhood BMI, and adulthood BMI with thyroid function or diseases. However, the causal relationships remain unclear due to residual confounding inherent in conventional epidemiological studies. We performed a two-sample Mendelian randomization (MR) study to investigate causal relationships of genetically predicted birth weight, childhood BMI, and adulthood BMI with a range of clinically relevant thyroid outcomes. Additionally, we conducted a reverse MR analysis on adulthood BMI. Data on exposures and outcomes were obtained from large-scale genome-wide association study meta-analyses predominantly composed of individuals of European ancestry. The MR analysis revealed no evidence of causal associations of birth weight or BMI at different life stages with thyrotropin (TSH) levels, hypothyroidism, hyperthyroidism, autoimmune thyroid disorders, or thyroid cancer. Contrarily, thyroid cancer demonstrated a significant causal relationship with increased adulthood BMI (β = 0.010, 95% CI: 0.006-0.015; p = 5.21 × 10-6). Our comprehensive MR did not find causal links of birth weight, childhood BMI, or adulthood BMI with thyroid diseases but provided evidence that thyroid cancer may play a role in weight gain. Our research findings offer valuable insights into the intricate relationship between body weight and thyroid health throughout an individual's life.

Accuracy of OBGYN Resident Physician Clinical Estimation of Fetal Weight in Term Pregnancies Before and After a Teaching Curriculum

Purpose: Accurate prediction of fetal birth weight can improve antepartum and intrapartum shared decision-making with patients. Therefore, this study primarily sought to determine if OBGYN residents are more accurate at clinically estimating fetal weight via Leopold maneuvers following a teaching curriculum including a hands-on simulation model. Secondary aims were to determine if accuracy changes depending on training level, maternal or fetal characteristics. Methods: A paired t-test was used to evaluate the primary aim of the teaching curriculum’s effect on accuracy of correct fetal weight estimation. Nineteen residents served as their own controls to assess differences in accuracy by performing 10-12 estimates prior to and after the teaching curriculum. The curriculum consisted of a short verbal workshop with images of how to perform Leopold maneuvers, along with five models that were created to represent a gravid uterus with a fetus measured in the 5th, 25th, 50th, 75th, 95th percentiles. Residents were also tested on models with unknown weights to confirm accuracy prior to completing the teaching curriculum. The inclusion criteria during the fetal weight estimates pre- and post-curriculum was as follows: mother had to deliver during hospital admission; gestational age was between 37 weeks and 41 weeks; singleton pregnancy; mother had not undergone rupture of membranes; mother was English speaking. Accuracy was defined as estimated fetal weight within 10% of actual birth weight. Generalized Logistic Mixed Models were used to assess accuracy when adjusting for PGY level, maternal and fetal characteristics. Maternal characteristics including age, BMI, race, pregestational diabetes, gestational diabetes, polyhydramnios, and oligohydramnios were abstracted from maternal medical charts via retrospective chart review. Infant characteristics including fetal macrosomia, fetal growth restriction, and gestational age were also recorded. Analyses were conducted using SAS 9.4; all p-values were two-sided and p-values &lt;0.05 were deemed statistically significant. Results: Nineteen OBGYN residents performed a total of 443 Leopold maneuvers to estimate fetal weight. Overall, resident estimation accuracy within 10% of actual birth weight improved from 65.5% to 67.4% (p=0.49) after the teaching curriculum. Senior residents showed a greater improvement after the teaching curriculum than junior residents, however it was not statistically significant. This could be due to the low sample size. Increasing gestational age was associated with higher odds of accurate estimation of fetal weight (OR 1.34, p=0.0295). There were no other differences in accuracy based on other maternal or fetal factors. Conclusion: The teaching of Leopold maneuvers is often not formalized in residency programs. This study did not show the expected improvement in accuracy of fetal weight estimation. Considerations that may impact clinical estimations include improving the simulation model, revising the teaching curriculum, and/or doing the teaching curriculum more frequently. Incorporating this hands-on simulation annually within the residency program educational curriculum to standardize the learning experience may be beneficial.

Ultrasound Evaluation of Placental Thickness: Insights From an Observational Study and Implications for Fetal Growth Assessment.

Introduction A precise gestational age (GA) assessment is critical to monitoring fetal growth and planning delivery. Any disorder that affects the placenta will affect the fetus. Hence, the placenta serves as an indicator of fetal development. So, placental thickness (PT) measurement can be utilized as a parameter in the precise estimation of gestational age and prediction of the fetal outcome. Ultrasound (USG) remains the preferred method for detecting placental abnormalities due to its benefits. This study aimed to evaluate placental thickness by USG in various GA subgroups and to see the correlation of PT with GA and fetal outcome. Methods Cross-sectional observational study with short follow-up. A total of 296 antenatal women between 14 weeks and 40 weeks underwent USG to measure placental thickness and were followed up until delivery. The collected data was compiled systematically and analyzed using IBM SPSS Statistics for Windows, Version 25 (released 2017; IBM Corp., Armonk, New York, United States). The level of significance was taken as p<0.05. Results The mean placental thickness progressed from 1.8 cm to 3.5 cm as the gestational age advanced from 14 weeks to 35 weeks and six days. After that, it decreased until delivery (r-value = 0.531 (<0.8), p-value <0.001). PT was positively correlated only with birth weight (p-value 0.013) amongst all fetal outcome parameters. Conclusion GA can be determined using PT with the help of regression techniques. PT can be used as a replacement when a particular parameter of the composite growth formula is fallacious. The PT increase rate is a more reliable indicator than the actual PT to predict birth weight.

Comparing Fetal Ultrasound Biometric Measurements to Neonatal Anthropometry at the Extremes of Birth Weight.

Error in birthweight prediction by sonographic estimated fetal weight (EFW) has clinical implications, such as avoidable cesarean or misclassification of fetal risk in labor. We aimed to evaluate optimal timing of ultrasound and which fetal measurements contribute to error in fetal ultrasound estimations of birth size at the extremes of birthweight. We compared differences in head circumference (HC), abdominal circumference (AC), femur length, and EFW between ultrasound and corresponding birth measurements within 14 (n = 1,290) and 7 (n = 617) days of birth for small- (SGA, <10th percentile), appropriate- (AGA, 10th-90th), and large-for-gestational age (LGA, >90th) newborns. Average differences between EFW and birthweight for SGA neonates were: -40.2 g (confidence interval [CI]: -82.1, 1.6) at 14 days versus 13.6 g (CI: -52.4, 79.7) at 7 days; for AGA, -122.4 g (-139.6, -105.1) at 14 days versus -27.2 g (-50.4, -4.0) at 7 days; and for LGA, -242.8 g (-306.5, -179.1) at 14 days versus -72.1 g (-152.0, 7.9) at 7 days. Differences between fetal and neonatal HC were larger at 14 versus 7 days, and similar to patterns for EFW and birthweight, differences were the largest for LGA at both intervals. In contrast, differences between fetal and neonatal AC were larger at 7 versus 14 days, suggesting larger error in AC estimation closer to birth. Using a standardized ultrasound protocol, SGA neonates had ultrasound measurements closer to actual birth measurements compared with AGA or LGA neonates. LGA neonates had the largest differences between fetal and neonatal size, with measurements 14 days from delivery showing 3- to 4-fold greater differences from birthweight. Differences in EFW and birthweight may not be explained by a single fetal measurement; whether estimation may be improved by incorporation of other knowable factors should be evaluated in future research. · Ultrasound measurements may be inadequate to predict neonatal size at birth.. · Birthweight estimation error is higher for neonates >90th percentile.. · There is higher error in AC closer to birth..

Prediction of Expected Fetal Weight From Fetal Limb Soft Tissue Measurements [ID 2683393

INTRODUCTION: This study was to investigate a combination of new formulas that predict birth weight in full-term pregnancy by measuring the thickness of the humerus soft tissue and femur soft tissue in fetuses using three-dimensional (3D) ultrasound. METHODS: There were a total of 110 mothers and newborns who were hospitalized with selective cesarean section, induced delivery, or natural delivery between April 2019 and August 2021. We retrospectively investigated fetal biometric parameters (BPD, HC, AC, FL, partial arm volume [Avol], and partial thigh volume [Tvol]), as measured by 4D View 9.0 and GE Healthcare Ultrasound while the mother was admitted to the delivery ward before delivery. The study was approved by the ethics board of the National Medical Center (NMC-2022-03-026). RESULTS: Among the several combinations that showed the expected weight, the weight predicted by AC/BPD/Tvol gave the strongest correlation (0.780) with the actual weight of the newborn, with an error rate of 6.4%. In boys, among the several combinations indicating the predicted body weight, AC/Avol/BPD had the highest correlation coefficient with the actual body weight (0.791), with an error rate of 5.0%. In girls, among several combinations, AC/Tvol/BPD had the highest correlation coefficient with actual body weight (0.878), with an error rate of 5.3%. CONCLUSION: The fetal weight predicted using Tvol and Avol measured using 3D ultrasound as parameters had a lower error rate for birth weight than the predicted weight measured by conventional 2D ultrasound. The parameters highly correlated with birth weight; however, differences were seen between the sexes.

Bridging the notch: quantification of the end diastolic notch to better predict fetal growth restriction.

We aimed to evaluate several quantitative methods to describe the diastolic notch (DN) and compare their performance in the prediction of fetal growth restriction. Patients who underwent a placental scan at 16-26 weeks of gestation and delivered between Jan 2016 and Dec 2020 were included. The uterine artery pulsatility index was measured for all of the patients. In patients with a DN, it was quantified using the notch index and notch depth index. Odds ratios for small for gestational age neonates (defined as birth weight <10th and <5th percentile) were calculated. Predictive values of uterine artery pulsatility, notch, and notch depth index for fetal growth restriction were calculated. Overall, 514 patients were included, with 69 (13.4%) of them delivering a small for gestational age neonate (birth weight<10th percentile). Of these, 20 (20.9%) had a mean uterine artery pulsatility index >95th percentile, 13 (18.8%) had a unilateral notch, and 11 (15.9%) had a bilateral notch. 16 patients (23.2%) had both a high uterine artery pulsatility index (>95th percentile) and a diastolic notch. Comparison of the performance between uterine artery pulsatility, notch, and notch depth index using receiver operating characteristic curves to predict fetal growth restriction <10th percentile found area under the curve values of 0.659, 0.679, and 0.704, respectively, with overlapping confidence intervals. Quantifying the diastolic notch at 16-26 weeks of gestation did not provide any added benefit in terms of prediction of neonatal birth weight below the 10th or 5th percentile for gestational age, compared with uterine artery pulsatility index.

Reduced T2*-weighted placental MRI predicts foetal growth restriction in women with chronic rheumatic disease—a Danish explorative study

ObjectivesWomen with chronic rheumatic disease (CRD) are at greater risk of foetal growth restriction than their healthy peers. T2*-weighted magnetic resonance imaging of placenta (T2*P-MRI) is superior to conventional ultrasonography in predicting birth weight and works as a proxy metabolic mirror of the placental function. We aimed to compare T2*P-MRI in pregnant women with CRD and healthy controls. In addition, we aimed to investigate the correlation between T2*P-MRI and birth weight.MethodsUsing a General Electric (GE) 1.5 Tesla, we consecutively performed T2*-weighted placental MRI in 10 women with CRD and 18 healthy controls at gestational week (GW)24 and GW32. We prospectively collected clinical parameters during pregnancy including birth outcome and placental weight.ResultsWomen with CRD had significantly lower T2*P-MRI values at GW24 than healthy controls (median T2*(IQR) 92.1 ms (81.6; 122.4) versus 118.6 ms (105.1; 129.1), p = 0.03). T2*P-MRI values at GW24 showed a significant correlation with birth weight, as the T2*P-MRI value was reduced in all four pregnancies complicated by SGA at birth. Three out of four pregnancies complicated by SGA at birth remained undetected by routine antenatal ultrasound.ConclusionThis study demonstrates reduced T2*P-MRI values and a high proportion of SGA at birth in CRD pregnancies compared to controls, suggesting an increased risk of placental dysfunction in CRD pregnancies. T2*P-MRI may have the potential to focus clinical vigilance by identifying pregnancies at risk of SGA as early as GW24.Key Points• Placenta-related causes of foetal growth restriction in women with rheumatic disease remain to be investigated.• T2*P-MRI values at gestational week 24 predicted foetuses small for gestational age at birth.• T2*P-MRI may indicate pregnant women with chronic rheumatic disease (CRD) in need of treatment optimization.

A study of correlation between foot length and gestational maturity and anthropometric measurements in neonates

Background: This pinnacle’s the importance for alternative measurements which predicts birth weight and gestational age (GA). Foot length is a simple measure and does not require expertise. The aim of this study was to study correlation of foot length and GA among preterm, term and post-term neonates. Methods: This study was a prospective observational study done in 155 babies. Anthropometric measurements were taken within the first 24 hours of life. GA estimation was done using modified Ballard score. Foot length, head circumference and chest circumference were measured and noted. Weight of the baby was recorded using electronic weighing scale. Results: Foot length statistically correlated (p&lt;0.05) with GA assessment using NBS, weight, length, head circumference and chest circumference. The highest correlation of foot length in term SGA and term AGA babies for foot length was with head circumference (r=0.74 and 0.64 respectively). In pre-terms, foot length correlated well with head circumference and birth weight (r=0.92, 0.84 and 0.92 respectively). There were no babies in preterm LGA group and post term SGA and LGA group. Conclusions: Foot length also statistically correlated with other parameters like birth weight, length, head circumference and chest circumference.

A simulation study to assess the potential benefits of MRI-based fetal weight estimation as a second-line test for suspected macrosomia

ObjectiveTo simulate the outcomes of Boulvain’s trial by using magnetic resonance imaging (MRI) for estimated fetal weight (EFW) as a second-line confirmatory imaging. Study designData derived from the Boulvain’s trial and the study PREMACRO (PREdict MACROsomia) were used to simulate a 1000-patient trial. Boulvain’s trial compared induction of labor (IOL) to expectant management in suspected macrosomia, whereas PREMACRO study compared the performance of ultrasound-EFW (US-EFW) and MRI-EFW in the prediction of birthweight. The primary outcome was the incidence of significant shoulder dystocia (SD). Cesarean delivery (CD), hyperbilirubinemia (HB), and IOL at < 39 weeks of gestation (WG) were selected as secondary outcomes. A subgroup analysis of the Boulvain’s trial was performed to estimate the incidence of the primary and secondary outcomes in the true positive and false positive groups for the two study arms. Sensitivity, specificity, positive and negative predictive values (PPV, NPV) for the prediction of macrosomia by MRI-EFW at 36 WG were calculated, and a decision tree was constructed for each outcome. ResultsThe PPV of US-EFW for the prediction of macrosomia in the PREMACRO trial was 56.3 %. MRI-EFW was superior to US-EFW as a predictive tool resulting in lower rates of induction for false-positive cases. Repeating Boulvain’s trial using MRI-EFW as a second-line test would result in similar rates of SD (relative risk [RR]:0.36), CD (RR:0.84), and neonatal HB (RR:2.6), as in the original trial. Increasing the sensitivity and specificity of MRI-EFW resulted in a similar relative risk for SD as in Boulvain’s trial, but with reduced rates of IOL < 39 WG, and improved the RR of CD in favor of IOL. We found an inverse relationship between IOL rate and incidence of SD for both US-EFW and MRI-EFW, although overall rates of IOL, CD, and neonatal HB would be lower with MRI-derived estimates of fetal weight. ConclusionThe superior accuracy of MRI-EFW over US-EFW for the diagnosis of macrosomia could result in lower rates of IOL without compromising the relative advantages of the intervention but fails to demonstrate a significant benefit to justify a replication of the original trial using MRI-EFW as a second-line test.

A comparative study of clinical methods and ultrasound methods for prediction of fetal birth weight at term gestation

A comparative study of clinical methods and ultrasound methods for prediction of fetal birth weight at term gestation

Predictors of birth weight in pregnant women with malaria: a prospective cohort facility-based study in Webuye-Kenya

In sub-Saharan Africa, malaria, which remains a major public health burden, has a prevalence of 9 to 28% and malaria in pregnancy is associated with severe adverse outcomes for the mother and her baby. Here, we sought to determine the predictors of birth weight in a cohort of 140 women with malaria in pregnancy, who were recruited at the Webuye County hospital in Western Kenya. All study participants underwent malaria diagnosis through microscopic examination of blood smear samples and were grouped into the malaria-positive and malaria-negative groups. Both groups were followed up beginning at the first antenatal visit (March 2022) until delivery (December 2022) and various data, including demographic, parity, gravidity, socioeconomic, maternal and fetal outcomes were collected. Data analyses were done using SPSS version 27. Chi-square and Fisher’s Exact tests were used for bivariate and relative risk analyses at a p-value of ≤0.05 (95%) confidence level. Most of the participants were aged 18–25 years, were primigravidas and married, had secondary school-level education, earned 20–30 thousand Kenya shillings, resided in rural areas, and were in the second trimester. There were 6 (4.6%) cases of low birth weight, 3 (4.5%) in the malaria-negative group and 3 (4.7%) in the malaria-positive group. During pregnancy, 41 (31.5%) were anaemic, 5 (3.8%) were HIV-positive, 5 (3.8%) had preeclampsia, and 2 (1.5%) had gestational diabetes. Our analyses show that confounding factors like anaemia, HIV, pre-eclampsia and gestational diabetes did not influence birthweight (p ≥ 0.923). The malaria-positive and malaria-negative groups did not differ significantly with regard to the low birth weight (relative risk: 0.999, 95% confidence interval: 0.926–1.077). Marital status, gestational age, and area of residence were associated with malaria p ≤ 0.001, ≤ 0.001 and 0.028 respectively. In both groups, 124 of the 140 deliveries had normal birth weights and of these 63 (95.4%, n = 70) were in the malaria-negative group, whereas 61 (95.3%, n = 70) belonged to the malaria-positive group.