Quality of Pregnancy Dating and Obstetric Interventions During Labor: Retrospective Database Analysis

Influence of different methods for calculating gestational age at birth on prematurity and small for gestational age proportions: a systematic review with meta-analysis

BACKGROUND

Recognizing premature newborns and small-for-gestational-age (SGA) is essential for providing care and supporting public policies. This systematic review aims to identify the influence of the last menstrual period (LMP) compared to ultrasonography (USG) before 24 weeks of gestation references on prematurity and SGA proportions at birth.

METHODS

Systematic review with meta-analysis followed the recommendations of the PRISMA Statement. PubMed, BVS, LILACS, Scopus-Elsevier, Embase-Elsevier, and Web-of-Science were searched (10-30-2022). The research question was: (P) newborns, (E) USG for estimating GA, (C) LMP for estimating GA, and (O) prematurity and SGA rates for both methods. Independent reviewers screened the articles and extracted the absolute number of preterm and SGA infants, reference standards, design, countries, and bias. Prematurity was birth before 37 weeks of gestation, and SGA was the birth weight below the p10 on the growth curve. The quality of the studies was assessed using the New-Castle-Ottawa Scale. The difference between proportions estimated the size effect in a meta-analysis of prevalence.

RESULTS

Among the 642 articles, 20 were included for data extraction and synthesis. The prematurity proportions ranged from 1.8 to 33.6% by USG and varied from 3.4 to 16.5% by the LMP. The pooled risk difference of prematurity proportions revealed an overestimation of the preterm birth of 2% in favor of LMP, with low certainty: 0.02 (95%CI: 0.01 to 0.03); I² 97%). Subgroup analysis of USG biometry (eight articles) showed homogeneity for a null risk difference between prematurity proportions when crown-rump length was the reference: 0.00 (95%CI: -0.001 to 0.000; I²: 0%); for biparietal diameter, risk difference was 0.00 (95%CI: -0.001 to 0.000; I²: 41%). Only one report showed the SGA proportions of 32% by the USG and 38% by the LMP.

CONCLUSIONS

LMP-based GA, compared to a USG reference, has little or no effect on prematurity proportions considering the high heterogeneity among studies. Few data (one study) remained unclear the influence of such references on SGA proportions. Results reinforced the importance of qualified GA to mitigate the impact on perinatal statistics.

TRIAL REGISTRATION

Registration number PROSPERO: CRD42020184646.

Gestational age assessed by optical skin reflection in low-birth-weight newborns: Applications in classification at birth

Introduction

A new medical device was previously developed to estimate gestational age (GA) at birth by processing a machine learning algorithm on the light scatter signal acquired on the newborn's skin. The study aims to validate GA calculated by the new device (test), comparing the result with the best available GA in newborns with low birth weight (LBW).

Methods

We conducted a multicenter, non-randomized, and single-blinded clinical trial in three urban referral centers for perinatal care in Brazil and Mozambique. LBW newborns with a GA over 24 weeks and weighing between 500 and 2,500 g were recruited in the first 24 h of life. All pregnancies had a GA calculated by obstetric ultrasound before 24 weeks or by reliable last menstrual period (LMP). The primary endpoint was the agreement between the GA calculated by the new device (test) and the best available clinical GA, with 95% confidence limits. In addition, we assessed the accuracy of using the test in the classification of preterm and SGA. Prematurity was childbirth before 37 gestational weeks. The growth standard curve was Intergrowth-21st, with the 10th percentile being the limit for classifying SGA.

Results

Among 305 evaluated newborns, 234 (76.7%) were premature, and 139 (45.6%) were SGA. The intraclass correlation coefficient between GA by the test and reference GA was 0.829 (95% CI: 0.785-0.863). However, the new device (test) underestimated the reference GA by an average of 2.8 days (95% limits of agreement: -40.6 to 31.2 days). Its use in classifying preterm or term newborns revealed an accuracy of 78.4% (95% CI: 73.3-81.6), with high sensitivity (96.2%; 95% CI: 92.8-98.2). The accuracy of classifying SGA newborns using GA calculated by the test was 62.3% (95% CI: 56.6-67.8).

Discussion

The new device (test) was able to assess GA at birth in LBW newborns, with a high agreement with the best available GA as a reference. The GA estimated by the device (test), when used to classify newborns on the first day of life, was useful in identifying premature infants but not when applied to identify SGA infants, considering current algohrithm. Nonetheless, the new device (test) has the potential to provide important information in places where the GA is unknown or inaccurate.

Reliability of last menstrual period recall, an early ultrasound and a Smartphone App in predicting date of delivery and classification of preterm and post-term births

Background

A reliable expected date of delivery (EDD) is important for pregnant women in planning for a safe delivery and critical for management of obstetric emergencies. We compared the accuracy of LMP recall, an early ultrasound (EUS) and a Smartphone App in predicting the EDD in South African pregnant women. We further evaluated the rates of preterm and post-term births based on using the different measures.

Methods

This is a retrospective sub-study of pregnant women enrolled in a randomized controlled trial between October 2017-December 2019. EDD and gestational age (GA) at delivery were calculated from EUS, LMP and Smartphone App. Data were analysed using SPSS version 25. A Bland–Altman plot was constructed to determine the limits of agreement between LMP and EUS.

Results

Three hundred twenty-five pregnant women who delivered at term (≥ 37 weeks by EUS) and without pregnancy complications were included in this analysis. Women had an EUS at a mean GA of 16 weeks and 3 days). The mean difference between LMP dating and EUS is 0.8 days with the limits of agreement 31.4–30.3 days (Concordance Correlation Co-efficient 0.835; 95%CI 0.802, 0.867). The mean(SD) of the marginal time distribution of the two methods differ significantly (p = 0.00187). EDDs were < 14 days of the actual date of delivery (ADD) for 287 (88.3%;95%CI 84.4–91.4), 279 (85.9%;95%CI 81.6–89.2) and 215 (66.2%;95%CI 60.9–71.1) women for EUS, Smartphone App and LMP respectively but overall agreement between EUS and LMP was only 46.5% using a five category scale for EDD-ADD with a kappa of .22. EUS 14–24 weeks and EUS < 14 weeks predicted EDDs < 14 days of ADD in 88.1% and 79.3% of women respectively. The proportion of births classified as preterm (< 37 weeks) was 9.9% (95%CI 7.1–13.6) by LMP and 0.3% (95%CI 0.1–1.7) by Smartphone App. The proportion of post-term (> 42 weeks gestation) births was 11.4% (95%CI 8.4–15.3), 1.9% (95%CI 0.9–3.9) and 3.4% (95%CI 1.9–5.9) by LMP, EUS and Smartphone respectively.

Conclusions

EUS and Smartphone App were the most accurate to estimate the EDD in pregnant women. LMP-based dating resulted in misclassification of a significantly greater number of preterm and post-term deliveries compared to EUS and the Smartphone App.

Premature or Small for Gestational Age Discrimination: International Multicenter Trial Protocol for Classification of the Low-Birth-Weight Newborn Through the Optical Properties of the Skin

Background

A low birth weight is an independent risk factor for adverse infant outcomes and a predictor of chronic disease in adulthood. In these situations, differentiating between prematurity and small for gestational age (SGA) or simultaneous conditions is essential to ensuring adequate care. Such diagnoses, however, depend on reliable pregnancy dating, which can be challenging in developing countries. A new medical optoelectronic device was developed to estimate gestational age (GA) at birth based on newborn skin reflection.

Objective

This study will aim to evaluate the device’s ability to detect prematurity or SGA, or both conditions simultaneously as well as predict short-term pulmonary complications in a cohort of low-birth-weight newborns.

Methods

This study protocol was designed for a multicenter cohort including referral hospitals in Brazil and Mozambique. Newborns weighing 500-2500 g will be eligible for inclusion with the best GA available, considering the limited resources of low-income countries. Comparator-GA is based on reliable last menstrual period dating or ultrasound assessment before 24 weeks’ gestation. Estimated GA at birth (Test-GA) will be calculated by applying a novel optoelectronic device to the newborn’s skin over the sole. The average difference between Test-GA and Comparator-GA will be analyzed, as will the percentage of newborns who are correctly diagnosed as preterm or SGA. In addition, in a nested case–control study, the accuracy of skin reflection in the prediction of prematurity-related respiratory problems will be evaluated. The estimated required sample size is 298 newborns.

Results

Teams of health professionals were trained, and standard operating procedures were developed following the good practice guidelines for the clinical investigation of medical devices for human participants. The first recruitment started in March 2019 in Brazil. Data collection is planned to end in December 2020, and the results should be available in March 2021.

Conclusions

The results of this clinical study have the potential to validate a new device to easily assess postnatal GA, supporting SGA identification when pregnancy dating is unreliable or unknown.

Trial Registration

ReBec: RBR-33rnjf; http://www.ensaiosclinicos.gov.br/rg/RBR-33rnjf/

International Registered Report Identifier (IRRID)

DERR1-10.2196/16477