Evidence map›Paper›PMID 34818932›Full record

ArticleWomen's health (London, England)

Maternal and fetal characteristics to predict c-section delivery: A scoring system for pregnant women.

Rima Irwinda, Rabbania Hiksas, Angga Wiratama Lokeswara, Noroyono Wibowo

Abstract read
In one paragraph

Article in Women's health (London, England). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Rima IrwindaMaternal-Fetal Medicine Division, Department of Obstetrics and Gynaecology, Faculty of Medicine Universitas Indonesia and Cipto Mangunkusumo Hospital, Jakarta, Indonesia.ORCID 0000-0002-6260-6273
Rabbania HiksasFaculty of Medicine Universitas Indonesia and Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
Angga Wiratama LokeswaraFaculty of Medicine Universitas Indonesia and Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
Noroyono WibowoMaternal-Fetal Medicine Division, Department of Obstetrics and Gynaecology, Faculty of Medicine Universitas Indonesia and Cipto Mangunkusumo Hospital, Jakarta, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCesarean section is one of the most common obstetrical interventions that has been performed at an increasing rate globally, due to both medical and non-medical reasons. This study aims to develop a prediction tool for pregnant women potentially needing c-section, such that necessary preparations from the mothers, families, and health providers can be made.

methodsA total of 603 pregnant women were recruited in the first phase of c-section prediction tool development. The association between the maternal and fetal factors on the risk of c-section were analyzed, followed by a stepwise multivariate regression analysis. In the next phase, 61 pregnant women were enrolled for external validation. Discrimination was assessed using area under the curve. The calibration plot was then made and assessed using the Hosmer-Lemeshow test.

resultsThere were 251 (41.6%) cases of vaginal delivery and 352 (58.4%) of c-section assessed. Multivariate analysis showed that gestational age < 37 wg (OR: 1.66, 95% CI: 1.10-2.51), pre-pregnancy body mass index (underweight) (OR: 0.40, 95% CI: 0.22-0.76), no history of vaginal delivery (OR: 2.66, 95% CI: 1.76-4.02), history of uterine surgery (OR: 8.34, 95% CI: 4.54-15.30), obstetrical complications (OR: 5.61, 95% CI: 3.53-8.90), birthweight ⩾ 3500 g (OR: 4.28, 95% CI: 2.16-8.47), and non-cephalic presentation (OR: 2.74, 95% CI: 1.53-4.89) were independently associated with c-section delivery. Those parameters were included in a 7-item scoring tool, with consecutive predictive scores of 1,-1,2,3,3,2,2,1. The area under the curve result was 0.813 (95% CI: 0.779-0.847), indicating a good predictive ability. The external validation showed AUC: 0.806, 95% CI: 0.694-0.917, Hosmer-Lemeshow test p = 0.666 and calibration plot coefficient of r = 0.939.

conclusionA total of 7 maternal-fetal factors were found to be strongly associated with c-section delivery, including gestational age < 37, maternal underweight body mass index, previous uterine surgery, obstetrical complications, birthweight ⩾ 3500, history of vaginal delivery, and non-cephalic presentation. Using these factors, a prediction tool was developed and validated with good quality.

Indexed as

Cesarean SectionPregnant PeopleDelivery, ObstetricFemaleGestational AgeHumansPregnancyPrenatal Carecesarean sectionmaternal-fetal characteristicsmode of deliverypredictionscoring system

Identifiers

PMID34818932
PMCPMC8785277

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.