PAPP-A levels as an early marker of idiopathic preterm birth: a pilot study

Evaluation of antenatal care quality for preterm birth prevention using an auditable scoring system: A retrospective, descriptive, longitudinal study in Sydney, Australia

INTRODUCTION

Preterm birth continues to be one of the most significant contributors to perinatal death. This study aims to evaluate the quality of antenatal care provided to women delivering preterm.

METHODS

This was a retrospective, descriptive, longitudinal review of all women who had antenatal care within a single Australian tertiary hospital and delivered spontaneously between 24 and 37 weeks of gestation, using an auditable scoring system assessing potential interventions for prevention of preterm birth. The review was limited to singleton pregnancies without fetal abnormalities delivering between January 2013 and April 2015. The audit tool was developed by reference to established 'best practice' guidance for prediction and prevention of preterm birth based on Royal Australian and New Zealand College of Obstetricians and Gynaecologists guidelines and published literature. Different pathways were assessed for women deemed either low- or high-risk at the outset of antenatal care.

RESULTS

A series of 161 pregnancies that delivered preterm (between 24 and 37 weeks' gestation) were reviewed. The quality of antenatal care was scored 'good' in 42.9% and 50% of high-risk and low-risk women, respectively. Care was scored 'adequate', with room for improvement in 51.4% and 45.2% of the two corresponding groups. The main deficiencies in care were recorded evidence of assessment of cervical length (absent in 35% of cases) and failure to screen for bacterial vaginosis in high-risk women.

CONCLUSIONS

Auditing antenatal care for prevention of preterm birth allows identification of suboptimal practice allowing service improvement and potential intervention for preterm birth prevention.

Prediction of preterm birth in nulliparous women using logistic regression and machine learning

OBJECTIVE

To predict preterm birth in nulliparous women using logistic regression and machine learning.

DESIGN

Population-based retrospective cohort.

PARTICIPANTS

Nulliparous women (N = 112,963) with a singleton gestation who gave birth between 20-42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014.

METHODS

We used data during the first and second trimesters to build logistic regression and machine learning models in a "training" sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent "validation" sample.

RESULTS

During the first trimester, logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23-5.42; Type II: AOR: 2.68; 95% CI: 2.05-3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80-2.30). During the first trimester, the maximum AUC was 60% (95% CI: 58-62%) with artificial neural networks in the validation sample. During the second trimester, 17 variables were significantly associated with preterm birth, among which complications during pregnancy had the highest AOR (13.03; 95% CI: 12.21-13.90). During the second trimester, the AUC increased to 65% (95% CI: 63-66%) with artificial neural networks in the validation sample. Including complications during the pregnancy yielded an AUC of 80% (95% CI: 79-81%) with artificial neural networks. All models yielded 94-97% negative predictive values for spontaneous PTB during the first and second trimesters.

CONCLUSION

Although artificial neural networks provided slightly higher AUC than logistic regression, prediction of preterm birth in the first trimester remained elusive. However, including data from the second trimester improved prediction to a moderate level by both logistic regression and machine learning approaches.

The Multi-Faced Role of PAPP-A in Post-Partum Breast Cancer: IGF-Signaling is Only the Beginning

Insulin-like growth factor (IGF) signaling and control of local bioavailability of free IGF by the IGF binding proteins (IGFBP) are important regulators of both mammary development and breast cancer. A recent genome-wide association study (GWAS) identified small nucleotide polymorphisms that reduce the expression of IGFBP-5 as a risk factor of developing breast cancer. This observation suggests that genetic alterations leading to a decreased level of IGFBP-5 may also contribute to breast cancer. In the current review, we focus on Pregnancy-Associated Plasma Protein A (PAPP-A), a protease involved in the degradation of IGFBP-5. PAPP-A is overexpressed in the majority of breast cancers but its role in cancer has only begun to be explored. More specifically, this review aims at highlighting the role of post-partum involution in the oncogenic function of PAPP-A. Notably, we summarize recent studies indicating that PAPP-A plays a role not only in the degradation of IGFBP-5 but also in the deposition of collagen and activation of the collagen receptor discoidin 2 (DDR2) during post-partum involution. Finally, considering the immunosuppressive microenvironment of post-partum involution, we also discuss the unexpected finding made in Ewing Sarcoma that PAPP-A plays a role in immune evasion. While the immunosuppressive role of PAPP-A in breast cancer remains to be determined, collectively these studies highlight the multifaced role of PAPP-A in cancer that extends well beyond its effect on IGF-signaling.

Early rapid growth, early birth: accelerated fetal growth and spontaneous late preterm birth

The past two decades in the United States have seen a 24% rise in spontaneous late preterm delivery (34-36 weeks) of unknown etiology. This study tested the hypothesis that fetal growth was identical prior to spontaneous preterm (n = 221, median gestational age at birth 35.6 weeks) and term (n = 3706) birth among pregnancies followed longitudinally in Santiago, Chile. The hypothesis was not supported: Preterm-delivered fetuses were significantly larger than their term-delivered peers by mid-second trimester in estimated fetal weight, head, limb, and abdominal dimensions, and they followed different growth trajectories. Piecewise regression assessed time-specific differences in growth rates at 4-week intervals from 16 weeks. Estimated fetal weight and abdominal circumference growth rates slowed at 20 weeks among the preterm-delivered, only to match and/or exceed their term-delivered peers at 24-28 weeks. After an abrupt growth rate decline at 28 weeks, fetuses delivered preterm did so at greater population-specific sex and age-adjusted birth weight percentiles than their peers from uncomplicated pregnancies (P < 0.01). Growth rates predicted birth timing: one standard score of estimated fetal weight increased the odds ratio for late preterm birth from 2.8 prior to 23 weeks, to 3.6 (95% confidence interval, 1.82-7.11, P < 0.05) between 23 and 27 weeks. After 27 weeks, increasing size was protective (OR: 0.56, 95% confidence interval, 0.38-0.82, P = 0.003). These data document, for the first time, a distinctive fetal growth pattern across gestation preceding spontaneous late preterm birth, identify the importance of mid-gestation for alterations in fetal growth, and add perspective on human fetal biological variability.