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Intrapartum prediction of birth weight with a simplified algorithmic approach derived from maternal characteristics

  • Piengbulan Yapan , Chirameth Promchirachote , Chutima Yaiyiam , Suraiya Rahman , Julaporn Pooliam and Tuangsit Wataganara ORCID logo EMAIL logo



To derive and validate a population-specific multivariate approach for birth weight (BW) prediction based on quantitative intrapartum assessment of maternal characteristics by means of an algorithmic method in low-risk women.


The derivation part (n = 200) prospectively explored 10 variables to create the best-fit algorithms (70% correct estimates within ±10% of actual BW) for prediction of BW at term; vertex presentation with engagement. The algorithm was then cross validated with samples of unrelated cases (n = 280) to compare the accuracy with the routine abdominal palpation method.


The best-fit algorithms were parity-specific. The derived simplified algorithms were (1) BW (g) = 100 [(0.42 × symphysis-fundal height (SFH; cm)) + gestational age at delivery (GA; weeks) − 25] in nulliparous, and (2) BW (g) = 100 [(0.42 × SFH (cm)) + GA − 23] in multiparous. Cross validation showed an overall 69.3% accuracy within ±10% of actual BW, which exceeded routine abdominal palpation (60.4%) (P = 0.019). The algorithmic BW prediction was significantly more accurate than routine abdominal palpation in women with the following characteristics: BW 2500–4000 g, multiparous, pre-pregnancy weight <50 kg, current weight <60 kg, height <155 cm, body mass index (BMI) <18.5 kg/m2, cervical dilatation 3–5 cm, station <0, intact membranes, SFH 30–39 cm, maternal abdominal circumference (mAC) <90 cm, mid-upper arm circumference (MUAC) <25 cm and female gender of the neonates (P < 0.05).


An overall accuracy of term BW prediction by our simplified algorithms exceeded that of routine abdominal palpation.

Corresponding author: Tuangsit Wataganara, MD, Associate Professor, Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Prannok Road, Bangkok Noi, Bangkok 10700, Thailand, Tel.: 011 662 419 7000, Fax: 011 662 418 2662

Appendix 1:

Multivariate algorithmic approaches using maternal characteristics to predict birth weight.

MethodsPrediction within ±10% of actual birth weight, %
Abdominal palpation55–75 [8], [9]
Ultrasonographya57.5 [10], 61.5 [6], 65.3% [11], 84.1 [9]
Multivariate algorithms
1.EFW (g)=SFH×AG53.5 [6]
2.EFW (g)=−3198−(122.3×presentation)+[94.9×GA (weeks)]+[93.5×SFH (cm)]−(97.4×fetal gender)+(87.1×parity)61.6 [9]
3.EFW (g)=100 [(SFH (cm)]−5] plus 100 if head engaged68.1 [8]
4.EFW (g)=1485.61+[SFH (cm)×23.37]+[11.62 (cm)×mAC]+(BMI×−6.81)+[parity (0=nulliparous, 1=multiparous)×72.25]72.7 [9]
  1. aDifference in the inclusion criteria and the equation used to estimate fetal weight. AG, abdominal girth; BMI, body mass index; EFW, estimated fetal weight; GA, gestational age; mAC, maternal abdominal circumference; SFH, symphysis-fundal height.


The authors want to thank Suparat Jaingam for her administrative assistance.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: Siriraj Research Development Fund (managed by Routine to Research: R2R).

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.


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Received: 2018-10-21
Accepted: 2019-05-28
Published Online: 2019-07-02
Published in Print: 2019-08-27

© 2019 Walter de Gruyter GmbH, Berlin/Boston

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