Early predictors of small-for-gestational-age neonates using non-invasive, low-cost, and readily available hematological markers

Correlation between Maternal Systemic Inflammatory Indicators before Delivery and Congenital Pneumonia in Newborns: A Case-Control Study

BACKGROUND

Congenital pneumonia is a common respiratory disease in newborns, often influenced by obstetric factors. Clinical diagnosis can be delayed, prompting interest in using systemic inflammatory indicators to predict various diseases.

OBJECTIVE

Our aim was to evaluate the predictive value of maternal systemic inflammatory indicators before delivery for congenital pneumonia in newborns.

METHODS

This retrospective study included full-term, singleton infants born at the Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University between January 2017 and December 2022. Infants admitted to the neonatal department within 48 h of birth were divided into two groups: 46 with clinical congenital pneumonia (the observation group) and 65 without congenital pneumonia (the control group). Maternal peripheral blood, complete blood cell count, and general condition within 72 h before delivery, as well as neonatal admission data were recorded. Systemic inflammatory indicators were assessed. Receiver operating characteristic curves were used to evaluate the predictive value of the maternal systemic inflammatory indicators before delivery for congenital pneumonia. A prediction model for neonatal pneumonia was constructed by combining systemic inflammatory indicators before delivery with logistic regression. The association between this prediction model and the prognosis of neonatal congenital pneumonia was examined.

RESULTS

Maternal systemic inflammatory indicators before delivery as predictive markers for congenital pneumonia and the regression model jointly constructed by NLR, MLR, SII, SIRI, and PIV before delivery predicted the occurrence of congenital pneumonia better. Maternal systemic inflammatory indicators correlated with the severity of congenital pneumonia in neonates.

CONCLUSIONS

Maternal systemic inflammatory indicators before delivery have predictive value for congenital pneumonia in neonates, aiding early identification and treatment.

Can the neutrophil-to-lymphocyte ratio be used as an early marker of small fetuses for gestational age? A prospective study

Background

Small-for-gestational-age (SGA) fetuses are at increased risk of mortality and morbidity, and less than 30% will be detected by any ultrasound scan within 4 weeks before delivery. Our aim was to evaluate the relationship between neutrophil/lymphocyte ratio (NLR) in the first trimester of pregnancy and SGA fetuses.

Method

We performed a prospective study between June 2021 and August 2022, to evaluate the relationship between the neutrophil to lymphocyte ratio in maternal blood in the first trimester of pregnancy, with the birth of an SGA fetus. One hundred ninety-four participants with singleton pregnancies between 11 + 1 and 13 + 6 weeks of gestation were recruited. Pregnancies affected with diagnosed fetal chromosomal abnormalities, or chronic pathologies were excluded. SGA was defined as birthweight less than the 10th centile (N = 42) and severe SGA as birthweight less than the 3rd centile for gestation (N = 10) according to a locally derived descriptive charts. The NLR value measured in the first trimester was compared between these two groups and controls.

Results

We found no statistically significant difference in NLR, (3.5 +/-1.2 vs. 3.4+/-1.2, p-value of 0.78) when comparing the SGA less than the 10th centile group to the control group. NLR was also not different between severe SGA and controls (3.6+/-1.4 vs. 3.4+/-1.2 p-value of 0.78).

Conclusion

We found no association between first-trimester NLR ratio and SGA.

Association between white blood cell count and adverse pregnancy outcomes: a retrospective cohort study from a tertiary hospital in China

OBJECTIVES

This study aimed to clarify the relationship between white blood cell (WBC) and adverse pregnancy outcomes.

DESIGN

A total of 25 270 pregnant women underwent peripheral blood white blood cell count tests in the first, second and third trimesters. Adverse pregnancy outcomes were gestational hypertension, pre-eclampsia, gestational diabetes mellitus, preterm birth, low birth weight, caesarean delivery, macrosomia and fetal distress. Due to acute infectious disease or other diseases, 1127 were excluded.

SETTING

Minhang Hospital, China.

PARTICIPANTS

A total of 24 143 pregnant women were included in this study.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was the adverse pregnancy outcomes.

RESULTS

For the 24 143 participants, we calculated adjusted ORs for adverse pregnancy outcomes associated with an increased WBC count. For gestational hypertension, the ORs were 1.18 (95% CI, 1.05 to 1.24) in the first trimester and 1.10 (1.06 to 1.13) in the second trimester; for pre-eclampsia, ORs were 1.14 (95% CI, 1.47 to 1.64) in the first trimester and 1.10 (1.05 to 1.16) in the second trimester; for gestational diabetes mellitus, ORs were 1.06 (95% CI, 1.00 to 1.13) in the first trimester and 1.10 (1.04 to 1.16) in the second trimester; for preterm birth, ORs were 1.12 (95% CI, 1.06 to 1.18) in the first trimester, 1.10 (1.06 to 1.13) in the second trimester and 1.12 (1.09 to 1.15) in the third trimester; for low birth weight, ORs were 1.09 (95% CI, 1.02 to 1.17) in the first trimester, 1.03 (0.99 to 1.08) in the second trimester and 1.12 (1.08 to 1.16) in the third trimester. Significant associations were not observed obviously for caesarean delivery, macrosomia and fetal distress.

CONCLUSIONS

Our results indicate strong, continuous associations of maternal WBC count with increased risks of adverse pregnancy outcomes.

Data-Driven Modeling of Pregnancy-Related Complications

A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.