Relationship of umbilical coiling index and cord twist direction with adverse perinatal outcomes

Background: The main objectives of this study were to examine - (1) relationship of pregnancy-related factors (maternal age, gestational diabetes mellitus, pregnancy-induced hypertension, oligohydramnios, small for gestational age (GA), and fetal gender) and postnatally measured umbilical coiling index (UCI); (2) association of UCI and cord twist directions with the following adverse perinatal outcomes, meconium staining of amniotic fluid, non-reassuring FHR on CTG, low Apgar score ( 34 weeks. The cases were categorized in hypocoiled (UCI 90th percentile) and normocoiled groups. To study relationship of pregnancy-related factors and UCI multivariate logistic regression was used; whereas bivariate analysis was used to study impact of UCI on various adverse perinatal outcomes. UCI was measured by a single observer.Results: In total, 100 subjects were enrolled. The mean UCI was 0.268 (SD = 0.13; 10th percentile = 0.139; 90th percentile =0.410) coils/cm. Pregnancy-related factors had non-significant relationship with UCI. For adverse perinatal outcomes, only the non-reassuring/abnormal FHR patterns were significantly associated with hypercoiled groups (OR = 4.5; CI= 1.15-17.58). Both the cord directions had almost equal distribution without any significant difference in outcomes; however, anticlockwise twisted cords were found to have significantly high UCI.Conclusions: No significant relationship was observed with pregnancy-related factors and UCI. However, it was observed that hypercoiled cords had significant association with non-reassuring/abnormal FHR patterns on CTG

Fetal weight estimation by ultrasound: development of Indian population-based models

Purpose Existing ultrasound-based fetal weight estimation models have been shown to have high errors when used in the Indian population. Therefore, the primary objective of this study was to develop Indian population-based models for fetal weight estimation, and the secondary objective was to compare their performance against established models. Methods Retrospectively collected data from 173 cases were used in this study. The inclusion criteria were a live singleton pregnancy and an interval from the ultrasound scan to delivery of ≤7 days. Multiple stepwise regression (MSR) and lasso regression methods were used to derive fetal weight estimation models using a randomly selected training group (n=137) with cross-products of abdominal circumference (AC), biparietal diameter (BPD), head circumference (HC), and femur length (FL) as independent variables. In the validation group (n=36), the bootstrap method was used to compare the performance of the new models against 12 existing models. Results The equations for the best-fit models obtained using the MSR and lasso methods were as follows: log10(EFW)=2.7843700+0.0004197(HC×AC)+0.0008545(AC×FL) and log10(EFW)=2.38 70211110+0.0074323216(HC)+0.0186555940(AC)+0.0013463735(BPD×FL)+0.0004519715 (HC×FL), respectively. In the training group, both models had very low systematic errors of 0.01% (±7.74%) and -0.03% (±7.70%), respectively. In the validation group, the performance of these models was found to be significantly better than that of the existing models. Conclusion The models presented in this study were found to be superior to existing models of ultrasound-based fetal weight estimation in the Indian population. We recommend a thorough evaluation of these models in independent studies

A Systematic Evaluation of Ultrasound-based Fetal Weight Estimation Models on Indian Population

Background: The purpose of this study was to systematically evaluate ultrasound-based fetal weight estimation models on Indian population to find out their performance across different weight bands and ability to correctly categorize low birth weight (LBW) and high birth weight (HBW) fetuses. Methods: We used retrospectively collected data of 154 cases for the study. Inclusion criteria were a live singleton pregnancy, gestational age ≥34 weeks and ultrasound scan to delivery duration ≤7 days. Cases with fetal growth restriction or malformation were excluded. The cases were divided into standard weight bands of 500 g each based on newborns' actual birth weights (ABW). For each weight band, performance of 12 different models based on abdominal circumference (AC), biparietal diameter (BPD), head circumference (HC) and femur length (FL) was evaluated by mean percentage error (MPE) and its standard deviation (random error). Sensitivity and positive predict value (PPV) of models to categorize LBW (ABW ≤ 2500 g) and HBW (ABW >3500 g) neonates were also evaluated. Results: We observed a significant variation in MPE of the 12 models with no single model being consistently superior across all the weight bands. For the cases with birth weight ≤3000 g, the Woo (AC-BPD) model was found to be more appropriate, whereas for the cases with birth weight >3000 g the Woo (AC-BPD-FL) model was found more appropriate. In general, models had a tendency to overestimate fetal weight in LBW neonates and underestimate it in HBW neonates. Overall, the models showed poor sensitivity and PPV to categorize LBW and HBW neonates. The highest sensitivity (57.1%) for LBW identification was observed with the Woo (AC-BPD) model; the highest PPV (50%) for HBW neonate identification was observed with the Hadlock (AC-HC), Warsof (AC-BPD) and Combs (AC-HC-FL) model. Conclusion: We found that the existing fetal weight estimation models have high systematic and random errors on Indian population, with a general tendency of overestimation of fetal weight in the LBW category and underestimation in the HBW category. We also observed that these models have a limited ability to predict babies at a risk of either low or high birth weight. It is recommended that the clinicians should consider all these factors, while interpreting estimated weight given by the existing models

Ultrasonography-based Fetal Weight Estimation: Finding an Appropriate Model for an Indian Population

Background: Very limited information is available regarding the accuracy and applicability of various ultrasonography parameters [abdominal circumference (AC), biparietal diameter (BPD), femur length (FL), and head circumference (HC)]-based fetal weight estimation models for Indian population. The objective of this study was to systematically evaluate commonly used fetal weight estimation models to determine their appropriateness for an Indian population. Methods: Retrospective data of 300 pregnant women was collected from a tertiary care center in Bengaluru, India. The inclusion criteria were a live singleton pregnancy, gestational age > 34 weeks, and last ultrasound scan to delivery duration < 7 days. Cases with suspected fetal growth restriction or malformation were excluded. For each case, fetal weight was estimated using 34 different models. The models specifically designed for low birth weight, small for gestation age, or macrosomic babies were excluded. The models were ranked based on their mean percentage error (MPE) and its standard deviation (random error). A model with the least MPE and random error ranking was considered as the best model. Results: In total, 149 cases were found suitable for the study. Out of 34, only 12 models had MPE within ± 10% and only seven models had random error < 10%. Most of the Western population-based models had a tendency to overestimate the fetal weight. Based on MPE and random error ranking, the Woo's (AC-BPD) model was found to be the best, followed by Jordaan (AC), Combs (AC-HC-FL), Hadlock (AC-HC), and Hadlock-3 (AC-HC-FL) models. It was observed that the models based on just AC and AC-BPD combinations had statistically significant lesser MPE than the models based on all other combinations (p < 0.05). Conclusion: It was observed that the existing models have higher errors on Indian population than on their native populations. This points toward limitations in direct application of these models on Indian population without due consideration. Therefore, it is recommended that clinicians should exert caution in interpretation of fetal weight estimations based on these models. Moreover, this study highlights a need of models based on native Indian population