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

Grisaru‐Granovsky, Sorina; Halevy, Tamar; Planer, Dan; Elstein, Deborah; Eidelman, Arthur I.; Samueloff, Arnon

doi:10.1038/sj.jp.7211800

Cited by 8 publications

(4 citation statements)

References 32 publications

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“…Further, binary categorization collapses all types of PTB in one group despite different rates of neonatal mortality and morbidity for each category of PTB [61] and despite potentially different predictors of extremely PTB compared to PTB overall. Although low pregnancy-associated plasma protein A concentraion is associated with trisomies which themselves are associated with preterm birth, the majority of such cases are in euploid pregnancies [62][63][64][65][66]. Finally, we were unable to examine ultrasonographic measurement of the uterine cervix, which is a strong predictor of PTB [67] as it is not available in the BORN database.…”

Section: Limitationsmentioning

confidence: 99%

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

2021

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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.

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Section: Limitationsmentioning

confidence: 99%

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

2021

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“…Grisaru-Granovsky ve ark. (32) yaptığı çalışmada, 51 preterm olgusu değerlendirilmiş ve tokolitik tedavi verilmiştir. Yirmi altı gebe 1 hafta içinde, geri kalan 25'i daha sonra doğum yapmıştır.…”

Section: Discussionunclassified

Role of Pregnancy Associated Plasma Protein (PAPP-A) and Free Human Chorionic Gonadotropin (B-HCG) in Predicting Adverse Pregnancy Outcomes

Yıldız¹,

Şahin²

2015

IKSST

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“…Pregnancy-associated plasma protein A is measured in the maternal serum as a part of the first-trimester screening. A study by Grisaru-Granovsky et al suggested that a decrease in PAPP-A levels (≤30,000 mU/l) was noted in women with PTB (40). Other studies also suggest that PAPP-A is associated with PTL and its measurement can further help in predicting PTD (41,42).…”

Section: D) Pregnancy-associated Plasma Protein a (Papp-a)mentioning

confidence: 97%

Biochemical Markers As Predictor Of Preterm Labor- Their Clinical Relevance And The Current Status

Singh¹,

Dey²,

Singh

et al. 2020

Gynecol Obstet Reprod Med

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Preterm birth is associated with significant perinatal morbidity and mortality. Spontaneous preterm birth accounts for upto 75% of all preterm births. A number of maternal and fetal characteristics have been associated with preterm birth. With better understanding of the pathophysiology of preterm birth, various biochemical markers have been studied extensively to predict the preterm birth efficiently so as to intervene appropriately and timely in the cases that would benefit from treatment. This paper provides a summary of the current literature on the use of biochemical markers in predicting spontaneous preterm birth in symptomatic and high risk-asymptomatic women. Evidence from the literature suggests cervico vaginal fetal fibronectin,interleukin-6, phosphorylated Insulin-like growth factor binding protein-1(phIGFBP1), placental alpha macroglobulin-1 (PAMG-1) and serum

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

Cited by 8 publications

References 32 publications

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

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

Role of Pregnancy Associated Plasma Protein (PAPP-A) and Free Human Chorionic Gonadotropin (B-HCG) in Predicting Adverse Pregnancy Outcomes

Biochemical Markers As Predictor Of Preterm Labor- Their Clinical Relevance And The Current Status

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