Gestational Age Assessment with Anthropometric Parameters in Newborns

Heart Rate and Pulse Oximetry Dynamics in the First Week after Birth in Neonatal Intensive Care Unit Patients and the Risk of Cerebral Palsy

OBJECTIVE

Infants in the neonatal intensive care unit (NICU) are at high risk of adverse neuromotor outcomes. Atypical patterns of heart rate (HR) and pulse oximetry (SpO2) may serve as biomarkers for risk assessment for cerebral palsy (CP). The purpose of this study was to determine whether atypical HR and SpO2 patterns in NICU patients add to clinical variables predicting later diagnosis of CP.

STUDY DESIGN

This was a retrospective study including patients admitted to a level IV NICU from 2009 to 2017 with archived cardiorespiratory data in the first 7 days from birth to follow-up at >2 years of age. The mean, standard deviation (SD), skewness, kurtosis and cross-correlation of HR and SpO2 were calculated. Three predictive models were developed using least absolute shrinkage and selection operator regression (clinical, cardiorespiratory and combined model), and their performance for predicting CP was evaluated.

RESULTS

Seventy infants with CP and 1,733 controls met inclusion criteria for a 3.8% population prevalence. Area under the receiver operating characteristic curve for CP prediction was 0.7524 for the clinical model, 0.7419 for the vital sign model, and 0.7725 for the combined model. Variables included in the combined model were lower maternal age, outborn delivery, lower 5-minute Apgar's score, lower SD of HR, and more negative skewness of HR.

CONCLUSION

In this study including NICU patients of all gestational ages, HR but not SpO2 patterns added to clinical variables to predict the eventual diagnosis of CP. Identification of risk of CP within the first few days of life could result in improved therapy resource allocation and risk stratification in clinical trials of new therapeutics.

KEY POINTS

· SD and skewness of HR have some added predictive value of later diagnosis of CP.. · SpO2 measures do not add to CP prediction.. · Combining clinical variables with early HR measures may improve the prediction of later CP..

Estimating gestational age using the anthropometric measurements of newborns in North Shewa Zone public hospitals, Oromia, Ethiopia

Background

The accurate estimation of gestational age is crucial in identifying prematurity and other health problems in newborns and in providing appropriate perinatal care. Although there are numerous methods for measuring gestational age, they are not always applicable. During these situations, it becomes challenging to ascertain whether a baby has been born prematurely or not. Therefore, this study aims to estimate gestational age by utilizing newborn anthropometric parameters.

Purpose

The objective of this study is to estimate the gestational age of newborns in public hospitals located in the North Shewa Zone of the Oromia Region in Ethiopia, by using anthropometric parameters.

Methods

A cross-sectional study was conducted at a facility from February 2022 to April 2022, using an interview-based questionnaire and anthropometric measurements. The anthropometric parameters that were measured include foot length (FL), mid-upper arm circumference (MUAC), and chest and head circumference (CHC). The study's sample size had a total of 420 participants. The data were cleaned, edited, manually checked for completeness, and entered into Epi-data version 3.1. Subsequently, the data were transferred into SPSS for analysis. The data were analyzed using descriptive analysis, simple linear regression, and multiple linear regressions. Finally, the data were presented using statements and tables.

Results

There is a significant and positive correlation between anthropometric parameters, including head circumference (r: 0.483), MUAC (r: 0.481), foot length (r: 0.457), and chest circumference (r: 0.482) with gestational age. All anthropometric parameters demonstrated positive and significant estimates of gestational age. The combination of the four measurements yielded the strongest estimate of gestational age. Gestational age can be calculated by the formula: Gestational age (Weeks) = 9.78 + 0.209*CHC + 0.607*MUAC + 0.727*FL + 0.322*HC.

Conclusion

Gestational age can be measured using head circumference, mid-upper arm circumference, foot length, and chest circumference. Utilizing the four anthropometric parameters in combination exhibits greater efficacy in estimating gestational age than using them individually. Therefore, it is recommended to use these alternative approaches when standard methods are not applicable.

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.

Measuring newborn foot length to estimate gestational age in a high risk Northwest Ethiopian population

Introduction

Preterm birth is defined as all births before 37 completed weeks of gestation. Globally, the prevalence rate of preterm birth ranges from 47.5 to 137 per 1000 live births. In Ethiopia, the prevalence of preterm birth is 10.1%. Several anthropometric parameters, particularly, head circumference and foot length(FL) have been used as a proxy measure for gestational age(GA).

Objective

To assess the use of newborn foot length as a screening tool to identify preterm newborns and correlation factors at the University of Gondar Comprehensive Specialized Hospital (UOG CSH), Northwest Ethiopia.

Methods

Institutional based cross-sectional study design was conducted on 205 newborns admitted to a neonatal intensive care unit, UOG CSH. Systematic sampling technique was employed. Optimal cutoff newborn foot length and area under the curve (AUC) was calculated by the receiver operating characteristic curve analysis to assess the power of foot length measurement to diagnosis prematurity.

Results

The mean foot length was 7.41±0.67 cm with a range of 5.4–8.6 cm. Gestational age had a significant strong positive correlation with foot length(r = 0.865). The regression equation derived was GA = 4.5*FL + 3.61. Foot length had strong power (AUC = 0.99) to differentiate preterm from term newborns. A threshold newborn foot length of ≤7.35 cm had a sensitivity and specificity of 98.5% and 96.3%, respectively to predict prematurity.

Conclusion

Foot length had a high sensitivity and specificity in identifying preterm newborns, making it a reliable tool to identify preterm birth in a rural setting.