Am J Perinatol
DOI: 10.1055/a-1787-6991
Original Article

Fetal Growth Biometry as Predictors of Shoulder Dystocia in a Low-Risk Obstetrical Population

Roger B. Newman
1   Department of Obstetrics and Gynecology, Medical University of South Carolina, Charleston, South Carolina
,
Danielle R. Stevens
2   Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
,
Kelly J. Hunt
2   Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
,
William A. Grobman
3   Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
,
John Owen
4   Department of Obstetrics and Gynecology, University of Alabama at Birmingham, Birmingham, Alabama
,
Anthony Sciscione
5   Department of Obstetrics and Gynecology, Christiana Health Care Center, Wilmington, Delaware
,
Ronald J. Wapner
6   Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, New York
,
Daniel Skupski
7   Department of Obstetrics and Gynecology, New York Presbyterian Queens, Flushing, New York
,
Edward K. Chien
8   Department of Obstetrics and Gynecology, Case Western Reserve University, Metro Health Medical Center, Cleveland, Ohio
,
Deborah A. Wing
9   Department of Obstetrics and Gynecology, University of California, Irvine; Orange, California
10   Department of Obstetrics and Gynecology, Fountain Valley Regional Hospital and Medical Center, Fountain Valley, California
,
Angela C. Ranzini
8   Department of Obstetrics and Gynecology, Case Western Reserve University, Metro Health Medical Center, Cleveland, Ohio
11   Department of Obstetrics and Gynecology, Saint Peter's University Hospital, New Brunswick, New Jersey
,
Manuel Porto
9   Department of Obstetrics and Gynecology, University of California, Irvine; Orange, California
,
Katherine L. Grantz
12   Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland
› Author Affiliations
Funding This research was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health and included American Recovery and Reinvestment Act (ARRA) funding (contract numbers: HHSN275200800013C; HHSN275200800002I; HHSN27500006; HHSN275200800003IC; HHSN275200800014C; HHSN275200800012C; HHSN275200800028C; and HHSN275201000009C).

Abstract

Objective This study aimed to evaluate fetal biometrics as predictors of shoulder dystocia (SD) in a low-risk obstetrical population.

Study Design Participants were enrolled as part of a U.S.-based prospective cohort study of fetal growth in low-risk singleton gestations (n = 2,802). Eligible women had liveborn singletons ≥2,500 g delivered vaginally. Sociodemographic, anthropometric, and pregnancy outcome data were abstracted by research staff. The diagnosis of SD was based on the recorded clinical impression of the delivering physician. Simple logistic regression models were used to examine associations between fetal biometrics and SD. Fetal biometric cut points, selected by Youden's J and clinical determination, were identified to optimize predictive capability. A final model for SD prediction was constructed using backward selection. Our dataset was randomly divided into training (60%) and test (40%) datasets for model building and internal validation.

Results A total of 1,691 women (98.7%) had an uncomplicated vaginal delivery, while 23 (1.3%) experienced SD. There were no differences in sociodemographic or maternal anthropometrics between groups. Epidural anesthesia use was significantly more common (100 vs. 82.4%; p = 0.03) among women who experienced SD compared with those who did not. Amniotic fluid maximal vertical pocket was also significantly greater among SD cases (5.8 ± 1.7 vs. 5.1 ± 1.5 cm; odds ratio = 1.32 [95% confidence interval: 1.03,1.69]). Several fetal biometric measures were significantly associated with SD when dichotomized based on clinically selected cut-off points. A final prediction model was internally valid with an area under the curve of 0.90 (95% confidence interval: 0.81, 0.99). At a model probability of 1%, sensitivity (71.4%), specificity (77.5%), positive (3.5%), and negative predictive values (99.6%) did not indicate the ability of the model to predict SD in a clinically meaningful way.

Conclusion Other than epidural anesthesia use, neither sociodemographic nor maternal anthropometrics were significantly associated with SD in this low-risk population. Both individually and in combination, fetal biometrics had limited ability to predict SD and lack clinical usefulness.

Key Points

  • SD unpredictable in low-risk women.

  • Fetal biometry does not reliably predict SD.

  • Epidural use associated with increased SD risk.

  • SD prediction models clinically inefficient.

Note

The study is registered with the ClinicalTrials.gov, identifier: NCT00912132.


Supplementary Material



Publication History

Received: 24 February 2021

Accepted: 17 February 2022

Accepted Manuscript online:
03 March 2022

Article published online:
30 June 2022

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