Foetal weight prediction models at a given gestational age in the absence of ultrasound facilities: application in Indonesia

Development and validation of prediction models for fetal growth restriction and birthweight: an individual participant data meta-analysis

Background

Fetal growth restriction is associated with perinatal morbidity and mortality. Early identification of women having at-risk fetuses can reduce perinatal adverse outcomes.

Objectives

To assess the predictive performance of existing models predicting fetal growth restriction and birthweight, and if needed, to develop and validate new multivariable models using individual participant data.

Design

Individual participant data meta-analyses of cohorts in International Prediction of Pregnancy Complications network, decision curve analysis and health economics analysis.

Participants

Pregnant women at booking. External validation of existing models (9 cohorts, 441,415 pregnancies); International Prediction of Pregnancy Complications model development and validation (4 cohorts, 237,228 pregnancies).

Predictors

Maternal clinical characteristics, biochemical and ultrasound markers.

Primary outcomes

fetal growth restriction defined as birthweight <10th centile adjusted for gestational age and with stillbirth, neonatal death or delivery before 32 weeks' gestation birthweight.

Analysis

First, we externally validated existing models using individual participant data meta-analysis. If needed, we developed and validated new International Prediction of Pregnancy Complications models using random-intercept regression models with backward elimination for variable selection and undertook internal-external cross-validation. We estimated the study-specific performance (c-statistic, calibration slope, calibration-in-the-large) for each model and pooled using random-effects meta-analysis. Heterogeneity was quantified using τ2 and 95% prediction intervals. We assessed the clinical utility of the fetal growth restriction model using decision curve analysis, and health economics analysis based on National Institute for Health and Care Excellence 2008 model.

Results

Of the 119 published models, one birthweight model (Poon) could be validated. None reported fetal growth restriction using our definition. Across all cohorts, the Poon model had good summary calibration slope of 0.93 (95% confidence interval 0.90 to 0.96) with slight overfitting, and underpredicted birthweight by 90.4 g on average (95% confidence interval 37.9 g to 142.9 g). The newly developed International Prediction of Pregnancy Complications-fetal growth restriction model included maternal age, height, parity, smoking status, ethnicity, and any history of hypertension, pre-eclampsia, previous stillbirth or small for gestational age baby and gestational age at delivery. This allowed predictions conditional on a range of assumed gestational ages at delivery. The pooled apparent c-statistic and calibration were 0.96 (95% confidence interval 0.51 to 1.0), and 0.95 (95% confidence interval 0.67 to 1.23), respectively. The model showed positive net benefit for predicted probability thresholds between 1% and 90%. In addition to the predictors in the International Prediction of Pregnancy Complications-fetal growth restriction model, the International Prediction of Pregnancy Complications-birthweight model included maternal weight, history of diabetes and mode of conception. Average calibration slope across cohorts in the internal-external cross-validation was 1.00 (95% confidence interval 0.78 to 1.23) with no evidence of overfitting. Birthweight was underestimated by 9.7 g on average (95% confidence interval -154.3 g to 173.8 g).

Limitations

We could not externally validate most of the published models due to variations in the definitions of outcomes. Internal-external cross-validation of our International Prediction of Pregnancy Complications-fetal growth restriction model was limited by the paucity of events in the included cohorts. The economic evaluation using the published National Institute for Health and Care Excellence 2008 model may not reflect current practice, and full economic evaluation was not possible due to paucity of data.

Future work

International Prediction of Pregnancy Complications models' performance needs to be assessed in routine practice, and their impact on decision-making and clinical outcomes needs evaluation.

Conclusion

The International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight models accurately predict fetal growth restriction and birthweight for various assumed gestational ages at delivery. These can be used to stratify the risk status at booking, plan monitoring and management.

Study registration

This study is registered as PROSPERO CRD42019135045.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/148/07) and is published in full in Health Technology Assessment; Vol. 28, No. 14. See the NIHR Funding and Awards website for further award information.

Estimation of Gestational Age Using Neonatal Anatomical Anthropometric Parameters in Dessie Referral Hospital, Northeast Ethiopia

Introduction

Estimation of gestational age is a key for the identification of a given low birth weight neonate is either preterm or growth retarded.

Objective

To estimate gestational age from neonatal anatomical anthropometric parameters in Dessie Referral Hospital, Ethiopia.

Methods

Institutional-based cross-sectional study design was employed in Dessie Referral Hospital from October 2019 to April 2020, with 424 consecutively live-born of 28–42 weeks of gestation. After considering the inclusion criteria, neonatal anthropometric parameters were measured within 3 days of birth. Foot length, hand length, mid-upper arm circumference, head circumference, crown-heel length, intermammary distance, umbilical nipple distance, and birth weight were measured and summarized using descriptive statistics, and the power of association was evaluated using correlation analysis. Regression equations of gestational age (GA) in completed weeks with anthropometric parameters were formulated using simple and multiple linear regression analysis.

Results

Except for hand length, all other neonatal anthropometric measurements were positively correlated with GA in completed weeks at p< 0.05. Anthropometric parameters individually, mid-upper arm circumference (MUAC) and BW (birth weight) were correlated well with GA at correlation coefficient (r) of 0.406 and 0.334, respectively. Regression formula was formulated as GA (weeks) = 26.12+ [1.11×MUAC (cm)] and GA (Weeks) = 33.19 + [1.53×BW (kg)]. Multiple regression contributed correlation with GA and used for prediction of GA as GA (weeks) = 28.12 – [0.393×HL (cm)] + [1.07×BW (kg)] + [0.87×MUAC (cm)] (r₌ 0.458).

Conclusion

The overall relative better correlation for prediction of GA, alone and in combination, is found by combined parameters (HL, MUAC, and BW). The relatively better individual anthropometric parameter for GA assessment is MUAC. Hence, using this neonatal parameter as a prediction of gestational age, the death of neonate due to preterm can be minimized.

The development of an alternative growth chart for estimated fetal weight in the absence of ultrasound: Application in Indonesia

A fetal growth chart is a vital tool for assessing fetal risk during pregnancy. Since fetal weight cannot be directly measured, its reliable estimation at different stages of pregnancy has become an essential issue in obstetrics and gynecology and one of the critical elements in developing a fetal growth chart for estimated fetal weight. In Indonesia, however, a reliable model and data for fetal weight estimation remain challenging, and this causes the absence of a standard fetal growth chart in antenatal care practices. This study has reviewed and evaluated the efficacy of the prediction models used to develop the most prominent growth charts for estimated fetal weight. The study also has discussed the potential challenges when such surveillance tools are utilized in low resource settings. The study, then, has proposed an alternative model based only on maternal fundal height to estimate fetal weight. Finally, the study has developed an alternative growth chart and assessed its capability in detecting abnormal patterns of fetal growth during pregnancy. Prospective data from twenty selected primary health centers in South Kalimantan, Indonesia, were used for the proposed model validation, the comparison task, and the alternative growth chart development using both descriptive and inferential statistics. Results show that limited access to individual fetal biometric characteristics and low-quality data on personal maternal and neonatal characteristics make the existing fetal growth charts less applicable in the local setting. The proposed model based only on maternal fundal height has a comparable ability in predicting fetal weight with less error than the existing models. The results have shown that the developed chart based on the proposed model can effectively detect signs of abnormality, between 20 and 41 weeks, among low birth weight babies in the absence of ultrasound. Consequently, the developed chart would improve the quality of fetal risk assessment during pregnancy and reduce the risk of adverse neonatal outcomes.

Improving the Information Availability and Accessibility of Antenatal Measurements to Ensure Safe Delivery: A Research-Based Policy Recommendation to Reduce Neonatal Mortality in Indonesia

Purpose

Assessing the risks and preventable causes of maternal and neonatal mortality requires the availability of good-quality antenatal information. In Indonesia, however, access to reliable information on pregnancy-related results remains challenging. This research has proposed a research-based policy recommendation to improve availability and accessibility to vital information on antenatal examinations.

Patients and Methods

Descriptive statistics were used to characterize midwives’ capabilities in routinely gathering and recording antenatal information during pregnancy. The investigation was carried out among 19 midwives in South Kalimantan, Indonesia, from April 2016 to October 2017. Antenatal data on 4946 women (retrospective study) and 381 women (prospective study) have been accessed through a scientific and technical training program.

Results

To date, lack of timely access to antenatal information has hampered the process of reducing neonatal mortality in Indonesia. The post-training statistical analysis showed that the training has significantly improved midwives’ scientific knowledge and technical abilities in providing more reliable data on antenatal measurements.

Conclusion

Consistent scientific and technical training among midwives is required to update their knowledge and skills, particularly those relating to documenting the results of antenatal examinations at different stages of pregnancy and using that information to assess potential risks and identify necessary interventions. This should also be followed by routine monitoring on the quality of collected antenatal data. This can be one of the enabling actions to achieve the 2030 Sustainable Development Goals target in reducing neonatal mortality in Indonesia.

The Impact of Scientific and Technical Training on Improving Databases' Adequacy for Fetal Growth Chart Development in Limited-Resource Settings: A Case Study in the Province of South Kalimantan, Indonesia

OBJECTIVES

To assess the impact of scientific and technical training on midwives' abilities in collecting and recording the key performance indicators for fetal growth chart development in limited-resource settings.

METHODS

A descriptive design was used to describe midwives' abilities in timely collecting and recording the minimum data required to estimate fetal weight and develop fetal growth chart. The study was conducted among 19 urban and rural midwives in South Kalimantan, Indonesia, between April 2016 and October 2017. The training provided access to antenatal care information on 4,946 women (retrospective cohort study) and 381 women (prospective cohort study).

RESULTS

The average amount of recorded antenatal care data on the key performance indicators of fetal growth assessment has been significantly improved (from 33.4% to 89.1%, p-value < 0.0005) through scientific and technical training.

CONCLUSIONS

Scientific knowledge and technical abilities have enabled midwives to timely record routine data of the key performance indicators for fetal growth surveillance. Access to this information is vital during different stages of pregnancy. The information can be utilised as evidence-based guidelines to assess fetal risks through fetal weight estimation and to develop fetal growth chart that is currently not available in Indonesian primary healthcare systems.