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

Roger B. Newman; Danielle R. Stevens; Kelly J. Hunt; William A. Grobman; John Owen; Anthony Sciscione; Ronald J. Wapner; Daniel Skupski; Edward K. Chien; Deborah A. Wing; Angela C. Ranzini; Manuel Porto; Katherine L. Grantz

doi:10.1055/a-1787-6991

American Journal of Perinatology, Table of Contents

Am J Perinatol 2024; 41(07): 891-901
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

¹Department of Obstetrics and Gynecology, Medical University of South Carolina, Charleston, South Carolina

,

Danielle R. Stevens

²Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina

,

Kelly J. Hunt

²Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina

,

William A. Grobman

³Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois

,

John Owen

⁴Department of Obstetrics and Gynecology, University of Alabama at Birmingham, Birmingham, Alabama

,

Anthony Sciscione

⁵Department of Obstetrics and Gynecology, Christiana Health Care Center, Wilmington, Delaware

,

Ronald J. Wapner

⁶Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, New York

,

Daniel Skupski

⁷Department of Obstetrics and Gynecology, New York Presbyterian Queens, Flushing, New York

,

Edward K. Chien

⁸Department of Obstetrics and Gynecology, Case Western Reserve University, Metro Health Medical Center, Cleveland, Ohio

,

Deborah A. Wing

⁹Department of Obstetrics and Gynecology, University of California, Irvine; Orange, California

¹⁰Department of Obstetrics and Gynecology, Fountain Valley Regional Hospital and Medical Center, Fountain Valley, California

,

Angela C. Ranzini

⁸Department of Obstetrics and Gynecology, Case Western Reserve University, Metro Health Medical Center, Cleveland, Ohio

¹¹Department of Obstetrics and Gynecology, Saint Peter's University Hospital, New Brunswick, New Jersey

,

Manuel Porto

⁹Department of Obstetrics and Gynecology, University of California, Irvine; Orange, California

,

Katherine L. Grantz

¹²Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland

› Author Affiliations

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.

Keywords

fetal biometrics - fetal growth ultrasound - prediction of shoulder dystocia - asymmetric fetal growth

Full Text

References

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Supplementary Material

Supplementary Material