Download PDF
Am J Perinatol
DOI: 10.1055/s-0044-1788274
Original Article

Fetal and Maternal Factors Predictive of Primary Cesarean Delivery at Term in a Low-Risk Population: NICHD Fetal Growth Studies—Singletons

Julio Mateus 
1   Division of Maternal-Fetal Medicine, Atrium Health, Charlotte, North Carolina
,
Danielle R. Stevens
2   Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland
,
Katherine L. Grantz
2   Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland
,
Cuilin Zhang
3   Global Center for Asian Women's Health and the Bia-Echo Asia Centre for Reproductive Longevity and Equality (ACRLE), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
4   Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
,
Jagteshwar Grewal
2   Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland
,
William A. Grobman
5   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, The Ohio State University College of Medicine, Columbus, Ohio
,
John Owen
6   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Alabama at Birmingham, Center for Women's Reproductive Health, Birmingham, Alabama
,
Anthony C. Sciscione
7   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Christiana Hospital, Newark, Delaware
,
Ronald J. Wapner
8   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, New York
,
Daniel Skupski
9   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, New York Presbyterian Queens, Flushing, New York and Weill Cornell Medicine, New York, New York
,
Edward Chien
10   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Cleveland Clinic Health System, Cleveland, Ohio
,
Deborah A. Wing
11   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of California, Irvine, and Long Beach Memorial Medical Center/Miller Children's Hospital Irvine, California
,
Angela C. Ranzini
12   Department of Obstetrics and Gynecology. The MetroHealth System, Cleveland, Ohio; Case Western Reserve University, MetroHealth Medical Center, Cleveland, Ohio
,
Michael P. Nageotte
13   Maternal-Fetal medicine Specialist, Miller Children's and Women's Hospital, Long Beach, California
,
Roger B. Newman
14   Division of Maternal-Fetal Medicine, Medical University of South Carolina, Charleston, South Carolina
› Author Affiliations Funding This research was supported, in part, by the Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health; and, in part, by Federal Funds for the NICHD Fetal Growth Studies including ARRA funding (contract numbers: HHSN275200800013C; HHSN275200800002I; HHSN27500006; HHSN275200800003IC; HHSN275200800014C; HHSN275200800012C; HHSN275200800028C; and HHSN275201000009C). K.L.G. and J.G. have contributed to this work as part of their official duties as employees of the United States Federal Government.

Clinical Trials Registration This study is registered with ClinicalTrials.gov (identifier: NCT00912132).
› Further Information
Also available at
    Permissions and Reprints

Abstract

Objective This study aimed to examine associations of fetal biometric and amniotic fluid measures with intrapartum primary cesarean delivery (PCD) and develop prediction models for PCD based on ultrasound parameters and maternal factors.

Study Design Secondary analysis of the National Institute of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies—singleton cohort (2009–2013) including patients with uncomplicated pregnancies and intent to deliver vaginally at ≥370/7 weeks. The estimated fetal weight, individual biometric parameters, fetal asymmetry measurements, and amniotic fluid single deepest vertical pocket assessed at the final scan (mean 37.5 ± 1.9 weeks) were categorized as <10th, 10th to 90th (reference), and >90th percentiles. Logistic regression analyses examined the association between the ultrasound measures and PCD. Fetal and maternal SuperLearner prediction algorithms were constructed for the full and nulliparous cohorts.

Results Of the 1,668 patients analyzed, 249 (14.9%) had PCD. The fetal head circumference, occipital–frontal diameter, and transverse abdominal diameter >90th percentile (adjusted odds ratio [aOR] = 2.50, 95% confidence interval [95% CI]: 1.39, 4.51; aOR = 1.86, 95% CI: 1.02, 3.40; and aOR = 2.13, 95% CI: 1.16, 3.89, respectively) were associated with PCD. The fetal model demonstrated poor ability to predict PCD in the full cohort and in nulliparous patients (area under the receiver-operating characteristic curve [AUC] = 0.56, 95% CI: 0.52, 0.61; and AUC = 0.54, 95% CI: 0.49, 0.60, respectively). Conversely, the maternal model had better predictive capability overall (AUC = 0.79, 95% CI: 0.75, 0.82) and in the nulliparous subgroup (AUC = 0.72, 95% CI: 0.67, 0.77). Models combining maternal/fetal factors performed similarly to the maternal model (AUC = 0.78, 95% CI: 0.75, 0.82 in full cohort, and AUC = 0.71, 95% CI: 0.66, 0.76 in nulliparas).

Conclusion Although a few fetal biometric parameters were associated with PCD, the fetal prediction model had low performance. In contrast, the maternal model had a fair-to-good ability to predict PCD.

Key Points

  • Fetal HC >90th percentile was associated with cesarean delivery.

  • Fetal parameters did not effectively predict PCD.

  • Maternal factors were more predictive of PCD.

  • Maternal/fetal and maternal models performed similarly.

  • Prediction models had lower performance in nulliparas.

Keywords

estimated fetal weight - fetal biometric parameters - maternal factors - primary cesarean delivery - prediction models

Supplementary Material



Publication History

Received: 14 May 2024

Accepted: 17 June 2024

Article published online:
29 July 2024

© 2024. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 

  • References

  • 1 Clark SL, Belfort MA, Dildy GA, Herbst MA, Meyers JA, Hankins GD. Maternal death in the 21st century: causes, prevention, and relationship to cesarean delivery. Am J Obstet Gynecol 2008; 199 (01) 36.e1-36.e5 , discussion 91–92, e7–e11
    CrossrefPubMedGoogle Scholar
  • 2 Huennekens K, Oot A, Lantos E, Yee LM, Feinglass J. Using electronic health record and administrative data to analyze maternal and neonatal delivery complications. Jt Comm J Qual Patient Saf 2020; 46 (11) 623-630
    PubMedGoogle Scholar
  • 3 Chauhan SP, Beydoun H, Hammad IA. et al. Indications for caesarean sections at ≥34 weeks among nulliparous women and differential composite maternal and neonatal morbidity. BJOG 2014; 121 (11) 1395-1402
    CrossrefPubMedGoogle Scholar
  • 4 Boyle A, Reddy UM, Landy HJ, Huang CC, Driggers RW, Laughon SK. Primary cesarean delivery in the United States. Obstet Gynecol 2013; 122 (01) 33-40
    CrossrefPubMedGoogle Scholar
  • 5 Silver RM, Landon MB, Rouse DJ. et al; National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network. Maternal morbidity associated with multiple repeat cesarean deliveries. Obstet Gynecol 2006; 107 (06) 1226-1232
    CrossrefPubMedGoogle Scholar
  • 6 Clark EA, Silver RM. Long-term maternal morbidity associated with repeat cesarean delivery. Am J Obstet Gynecol 2011; 205 (6 suppl); S2-S10
    CrossrefPubMedGoogle Scholar
  • 7 Simoes E, Kunz S, Bosing-Schwenkglenks M, Schmahl FW. Association between method of delivery, puerperal complication rate and postpartum hysterectomy. Arch Gynecol Obstet 2005; 272 (01) 43-47
    CrossrefPubMedGoogle Scholar
  • 8 Solheim KN, Esakoff TF, Little SE, Cheng YW, Sparks TN, Caughley AB. The effect of placenta previa, placenta accreta, and maternal mortality. J Matern Fetal Neonatal Med 2011; 24 (11) 1341-1346
    CrossrefPubMedGoogle Scholar
  • 9 Betrán AP, Ye J, Moller AB, Zhang J, Gülmezoglu AM, Torloni MR. The increasing trend in caesarean section rates: global, regional, and national estimates: 1990–2014. PLoS ONE 2016; 11 (02) e0148343
    CrossrefPubMedGoogle Scholar
  • 10 Caughey AB, Cahill AG, Guise JM, Rouse DJ. American College of Obstetricians and Gynecologists (College), Society for Maternal-Fetal Medicine. Safe prevention of the primary cesarean delivery. Am J Obstet Gynecol 2014; 210 (03) 179-193
    CrossrefPubMedGoogle Scholar
  • 11 Osterman MJK, Hamilton BE, Martin JA, Driscoll AK, Valenzuela CP. Births: final data for 2021. Natl Vital Stat Rep 2023; 72 (01) 1-53
    PubMedGoogle Scholar
  • 12 Ecker JL, Chen KT, Cohen AP, Riley LE, Lieberman ES. Increased risk of cesarean delivery with advancing maternal age: indications and associated factors in nulliparous women. Am J Obstet Gynecol 2001; 185 (04) 883-887
    CrossrefPubMedGoogle Scholar
  • 13 Kominiarek MA, Vanveldhuisen P, Hibbard J. et al; Consortium on Safe Labor. The maternal body mass index: a strong association with delivery route. Am J Obstet Gynecol 2010; 203 (03) 264.e1-264.e7
    CrossrefPubMedGoogle Scholar
  • 14 Dude AM, Davis B, Delaney K, Yee LM. Sonographic estimated fetal weight and cesarean delivery among nulliparous women with obesity. AJP Rep 2019; (02) e127-e132
    PubMedGoogle Scholar
  • 15 Buck Louis GM, Grewal J, Albert PS. et al. Racial/ethnic standards for fetal growth: the NICHD Fetal Growth Studies. Am J Obstet Gynecol 2015; 213 (04) 449.e1-449.e41
    Google Scholar
  • 16 Grewal J, Grantz KL, Zhang C. et al. Cohort profile: NICHD Fetal Growth Studies-singletons and twins. Int J Epidemiol 2018; 47 (01) 25-25l
    CrossrefPubMedGoogle Scholar
  • 17 Zhang C, Hediger ML, Albert PS. et al. Association of maternal obesity with longitudinal ultrasonographic measures of fetal growth: findings from the NICHD Fetal Growth Studies. JAMA Pediatr 2018; 172 (01) 24-31
    CrossrefPubMedGoogle Scholar
  • 18 Hadlock FP, Harrist RB, Sharman RS, Deter RL, Park SK. Estimation of fetal weight with the use of head, body, and femur measurements–a prospective study. Am J Obstet Gynecol 1985; 151 (03) 333-337
    CrossrefPubMedGoogle Scholar
  • 19 Burkhardt T, Schmidt M, Kurmanavicius J, Zimmermann R, Schäffer L. Evaluation of fetal anthropometric measures to predict the risk for shoulder dystocia. Ultrasound Obstet Gynecol 2014; 43 (01) 77-82
    CrossrefPubMedGoogle Scholar
  • 20 Hediger ML, Fuchs KM, Grantz KL. et al. Ultrasound quality assurance for singletons in the National Institute of Child Health and Human Development Fetal Growth Studies. J Ultrasound Med 2016; 35 (08) 1725-1733
    CrossrefPubMedGoogle Scholar
  • 21 Mahajan S, Caraballo C, Lu Y. et al. Trends in differences in health status and health care access and affordability by race and ethnicity in the United States, 1999-2018. JAMA 2021; 326 (07) 637-648
    CrossrefPubMedGoogle Scholar
  • 22 Dominguez TP, Dunkel-Schetter C, Glynn LM, Hobel C, Sandman CA. Racial differences in birth outcomes: the role of general, pregnancy, and racism stress. Health Psychol 2008; 27 (02) 194-203
    CrossrefPubMedGoogle Scholar
  • 23 Larrabee Sonderlund A, Schoenthaler A, Thilsing T. The association between maternal experiences of interpersonal discrimination and adverse birth outcomes: a systematic review of the evidence. Int J Environ Res Public Health 2021; 18 (04) 1465
    CrossrefPubMedGoogle Scholar
  • 24 Maldonado G, Greenland S. Simulation study of confounder-selection strategies. Am J Epidemiol 1993; 138 (11) 923-936
    CrossrefPubMedGoogle Scholar
  • 25 Polley E, LeDell E, Kennedy C, Lendle S, van der Lann M. SuperLearner: Super Learner Prediction. R packages. Published July 18, 2023. Accessed January 24, 2024 at: https://CRAN.R-project.org/package=SuperLearner
    PubMedGoogle Scholar
  • 26 Van MJ, Rose S. Targeted Learning: Causal Inference for Observational and Experimental Data. SpringerLink Online Service. New York: Springer; 2011
    Google Scholar
  • 27 Polley E, van der Laan M. Super Learner In Prediction. UC Berkeley Division of Biostatistics Working Paper Series. Published online May 3, 2010. Accessed May 31, 2023 at: http://biostats.bepress.com/ucbbiostat/paper266
    PubMedGoogle Scholar
  • 28 Hosmer DW, Lemeshow S, Netlibrary I. Applied Logistic Regression. Wiley; 2000
    CrossrefGoogle Scholar
  • 29 Friedman J, Hastie T, Tibshirani R. regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010; 33 (01) 1-22
    CrossrefPubMedGoogle Scholar
  • 30 Friedman JH. Multivariate adaptive regression splines. Ann Stat 1991; 19 (01) 1-67
    PubMedGoogle Scholar
  • 31 Breiman L. Random forests. Mach Learn 2001; 45: 5-32
    CrossrefPubMedGoogle Scholar
  • 32 Gelman A, Jakulin A, Pittau MG, Su YS. A weakly informative default prior distribution for logistic and other regression models. Ann Appl Stat 2008; 2 (04) 1360-1383
    CrossrefPubMedGoogle Scholar
  • 33 Hastie T, Tibshirani R. Generalized additive models for medical research. Stat Methods Med Res 1995; 4 (03) 187-196
    CrossrefPubMedGoogle Scholar
  • 34 Ripley BD. Pattern Recognition and Neural Networks. Cambridge University Press; 2007
    Google Scholar
  • 35 Brownlee J. Data Preparation for Machine Learning. Machine Learning Mastery; 2020
    Google Scholar
  • 36 Wilcox AJ, Cortese M, McConnaughey DR, Moster D, Basso O. The limits of small-for-gestational-age as a high-risk category. Eur J Epidemiol 2021; 36 (10) 985-991
    CrossrefPubMedGoogle Scholar
  • 37 Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 2015; 350: g7594
    CrossrefPubMedGoogle Scholar
  • 38 Carter JV, Pan J, Rai SN, Galandiuk S. ROC-ing along: evaluation and interpretation of receiver operating characteristic curves. Surgery 2016; 159 (06) 1638-1645
    CrossrefPubMedGoogle Scholar
  • 39 Schuit E, Kwee A, Westerhuis ME. et al. A clinical prediction model to assess the risk of operative delivery. BJOG 2012; 119 (08) 915-923
    CrossrefPubMedGoogle Scholar
  • 40 Little SE, Edlow AG, Thomas AM, Smith NA. Estimated fetal weight by ultrasound: a modifiable risk factor for cesarean delivery?. Am J Obstet Gynecol 2012; 207 (04) 309.e1-309.e6
    CrossrefPubMedGoogle Scholar
  • 41 Sovio U, Smith GCS. Blinded ultrasound fetal biometry at 36 weeks and risk of emergency cesarean delivery in a prospective cohort study of low-risk nulliparous women. Ultrasound Obstet Gynecol 2018; 52 (01) 78-86
    CrossrefPubMedGoogle Scholar
  • 42 Henrichs J, Verfaille V, Jellema P, Viester L, Pajkrt E, Wilschut J, van der Horst HE, Franx A, de Jonge A. IRIS study group. Effectiveness of routine third trimester ultrasonography to reduce adverse perinatal outcomes in low-risk pregnancy (the IRIS study): nationwide, pragmatic, multicentre, stepped wedge cluster randomised trial. BMJ 2019; 367: l5517
    CrossrefPubMedGoogle Scholar
  • 43 Al-Hafez L, Chauhan SP, Riegel M, Balogun OA, Hammad IA, Berghella V. Routine third-trimester ultrasound in low-risk pregnancies and perinatal death: a systematic review and meta-analysis. Am J Obstet Gynecol MFM 2020; 2 (04) 100242
    CrossrefPubMedGoogle Scholar
  • 44 Committee on Practice Bulletins—Obstetrics and the American Institute of Ultrasound in Medicine. Practice Bulletin No. 175: Ultrasound in pregnancy. Obstet Gynecol 2016; 128 (06) e241-e256
    CrossrefPubMedGoogle Scholar
  • 45 Zhang J, Landy HJ, Ware Branch D. et al; Consortium on Safe Labor. Contemporary patterns of spontaneous labor with normal neonatal outcomes. Obstet Gynecol 2010; 116 (06) 1281-1287
    CrossrefPubMedGoogle Scholar
  • 46 American College of Obstetrics and Gynecology Committee on Practice Bulletins-Obstetrics. ACOG practice bulletin number 49, December 2003: Dystocia and augmentation of labor. Obstet Gynecol 2003; 102 (06) 1445-1454
    CrossrefPubMedGoogle Scholar