PDF herunterladen
Am J Perinatol 2022; 39(07): 786-796
DOI: 10.1055/s-0040-1718704
Original Article

A Model to Predict Vaginal Delivery and Maternal and Neonatal Morbidity in Low-Risk Nulliparous Patients at Term

Maged M. Costantine
1   Departments of Obstetrics and Gynecology of University of Texas Medical Branch, Galveston, Texas
,
Grecio Sandoval
2   The George Washington University Biostatistics Center, Washington, Dist. of Columbia
,
William A. Grobman
3   Department of Obstetrics and Gynecology, Northwestern University, Chicago, Illinois
,
Jennifer L. Bailit
4   Department of Obstetrics and Gynecology, MetroHealth Medical Center-Case Western Reserve University, Cleveland, Ohio
,
Uma M. Reddy
5   Department of Obstetrics and Gynecology, The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland
,
Ronald J. Wapner
6   Department of Obstetrics and Gynecology, Columbia University, New York, New York
,
Michael W. Varner
7   Department of Obstetrics and Gynecology, University of Utah Health Sciences Center, Salt Lake City, Utah
,
John M. Thorp Jr.
8   Department of Obstetrics and Gynecology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
,
Steve N. Caritis
9   Department of Obstetrics and Gynecology, University of Pittsburgh, Pittsburgh, Pennsylvania
,
Mona Prasad
10   Department of Obstetrics and Gynecology, The Ohio State University, Columbus, Ohio
,
Alan T.N. Tita
11   Department of Obstetrics and Gynecology, University of Alabama at Birmingham, Birmingham, Alabama
,
Yoram Sorokin
12   Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan
,
Dwight J. Rouse
13   Department of Obstetrics and Gynecology, Brown University, Providence, Rhode Island
,
Sean C. Blackwell
14   Department of Obstetrics and Gynecology, The University of Texas Health Science Center at Houston, McGovern Medical School-Children's Memorial Hermann Hospital, Houston, Texas
,
Jorge E. Tolosa
15   Department of Obstetrics and Gynecology, Oregon Health and Science University, Portland, Oregon
,
for the Eunice Kennedy Shriver National Institute of Child Health Human Development Maternal-Fetal Medicine Units Network, Bethesda, MD › Institutsangaben Funding The project described was supported by grants from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), grant nos.: HD21410, HD27869, HD27915, HD27917, HD34116, HD34208, HD36801, HD40500, HD40512, HD40544, HD40545, HD40560, HD40485, HD53097, HD53118; the National Center for Research Resources grant nos.: UL1 RR024989, 5UL1 RR025764. Comments and views of the authors do not necessarily represent views of the National Institute of Health.
› Weitere Informationen
    Lizenzen und Reprints

Abstract

Objective This study aimed to develop and validate a model to predict the probability of vaginal delivery (VD) in low-risk term nulliparous patients, and to determine whether it can predict the risk of severe maternal and neonatal morbidity.

Methods Secondary analysis of an obstetric cohort of patients and their neonates born in 25 hospitals across the United States (n = 115,502). Trained and certified research personnel abstracted the maternal and neonatal records. Nulliparous patients with singleton, nonanomalous vertex fetuses, admitted with an intent for VD ≥ 37 weeks were included in this analysis. Patients in active labor (cervical exam > 5 cm), those with prior cesarean and other comorbidities were excluded. Eligible patients were randomly divided into a training and test sets. Based on the training set, and using factors available at the time of admission for delivery, we developed and validated a logistic regression model to predict the probability of VD, and then estimated the prevalences of severe morbidity according to the predicted probability of VD.

Results A total of 19,611 patients were included. Based on the training set (n = 9,739), a logistic regression model was developed that included maternal age, body mass index (BMI), cervical dilatation, and gestational age on admission. The model was internally validated on the test set (n = 9,872 patients) and yielded a receiver operating characteristic-area under the curve (ROC-AUC) of 0.71 (95% confidence interval [CI]: 0.70–0.72). Based on a subset of 18,803 patients with calculated predicted probabilities, we demonstrated that the prevalences of severe morbidity decreased as the predicted probability of VD increased (p < 0.01).

Conclusion In a large cohort of low-risk nulliparous patients in early labor or undergoing induction of labor, at term with singleton gestations, we developed and validated a model to calculate the probability of VD, and maternal and neonatal morbidity. If externally validated, this calculator may be clinically useful in helping to direct level of care, staffing, and adjustment for case-mix among various systems.

Key Points

  • A model to predict the probability of vaginal delivery in low-risk nulliparous patients at term.

  • The model also predicts the risk of severe maternal and neonatal morbidity.

  • The prevalences of severe morbidity decrease as the probability of vaginal delivery increases.

Keywords

vaginal delivery - calculator - maternal and neonatal morbidity

Note

This study was presented at the 35th and 36th Annual Meeting of the Society for Maternal Fetal Medicine.


* See [Supplementary Material] (available in the online version) for a list of other members of the NICHD MFMU Network.


Supplementary Material



Publikationsverlauf

Eingereicht: 17. August 2020

Angenommen: 07. September 2020

Artikel online veröffentlicht:
19. Oktober 2020

© 2020. Thieme. All rights reserved.

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

 

  • References

  • 1 Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Drake P. Births: Final Data for 2017. Natl Vital Stat Rep 2018; 67 (08) 1-50
    PubMedGoogle Scholar
  • 2 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
  • 3 American College of Obstetricians and Gynecologists, Society for Maternal-Fetal Medicine. Obstetric care consensus no. 1: safe prevention of the primary cesarean delivery. Obstet Gynecol 2014; 123 (03) 693-711
    CrossrefPubMedGoogle Scholar
  • 4 Spong CY, Berghella V, Wenstrom KD, Mercer BM, Saade GR. Preventing the first cesarean delivery: summary of a joint Eunice Kennedy Shriver National Institute of Child Health and Human Development, Society for Maternal-Fetal Medicine, and American College of Obstetricians and Gynecologists Workshop. Obstet Gynecol 2012; 120 (05) 1181-1193
    CrossrefPubMedGoogle Scholar
  • 5 Appropriate technology for birth. Lancet 1985; 2 (8452): 436-437
    PubMedGoogle Scholar
  • 6 Ye J, Betrán AP, Guerrero Vela M, Souza JP, Zhang J. Searching for the optimal rate of medically necessary cesarean delivery. Birth 2014; 41 (03) 237-244
    CrossrefPubMedGoogle Scholar
  • 7 Molina G, Weiser TG, Lipsitz SR. et al. Relationship between cesarean delivery rate and maternal and neonatal mortality. JAMA 2015; 314 (21) 2263-2270
    CrossrefPubMedGoogle Scholar
  • 8 Grobman WA, Bailit JL, Rice MM. Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network. et al. Can differences in obstetric outcomes be explained by differences in the care provided? The MFMU Network APEX study. Am J Obstet Gynecol 2014; 211 (02) 147.e1-147.e16
    CrossrefPubMedGoogle Scholar
  • 9 Bailit JL, Grobman WA, Rice MM. Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal–Fetal Medicine Units Network. et al. Risk-adjusted models for adverse obstetric outcomes and variation in risk-adjusted outcomes across hospitals. Am J Obstet Gynecol 2013; 209 (05) 446.e1-446.e30
    CrossrefPubMedGoogle Scholar
  • 10 Vyas DA, Eisenstein LG, Jones DS. Hidden in Plain Sight - Reconsidering the Use of Race Correction in Clinical Algorithms. N Engl J Med 2020; 383 (09) 874-882
    CrossrefPubMedGoogle Scholar
  • 11 Hosmer DWLS, Sturdivant RX. Applied Logistic Regression, Vol. 398. 3rd ed. John Wiley and Sons; Hoboken, NJ: 2013
    CrossrefGoogle Scholar
  • 12 D'Agostino Sr. RB, Vasan RS, Pencina MJ. et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 2008; 117 (06) 743-753
    CrossrefPubMedGoogle Scholar
  • 13 Wu BU, Johannes RS, Sun X, Tabak Y, Conwell DL, Banks PA. The early prediction of mortality in acute pancreatitis: a large population-based study. Gut 2008; 57 (12) 1698-1703
    CrossrefPubMedGoogle Scholar
  • 14 Kleinrouweler CE, Cheong-See FM, Collins GS. et al. Prognostic models in obstetrics: available, but far from applicable. Am J Obstet Gynecol 2016; 214 (01) 79-90.e36
    CrossrefPubMedGoogle Scholar
  • 15 Grobman WA, Lai Y, Landon MB. National Institute of Child Health and Human Development (NICHD) Maternal-Fetal Medicine Units Network (MFMU). et al. Development of a nomogram for prediction of vaginal birth after cesarean delivery. Obstet Gynecol 2007; 109 (04) 806-812
    CrossrefPubMedGoogle Scholar
  • 16 Tyson JE, Parikh NA, Langer J, Green C, Higgins RD. National Institute of Child Health and Human Development Neonatal Research Network. Intensive care for extreme prematurity--moving beyond gestational age. N Engl J Med 2008; 358 (16) 1672-1681
    CrossrefPubMedGoogle Scholar
  • 17 Eggebø TM, Wilhelm-Benartzi C, Hassan WA, Usman S, Salvesen KA, Lees CC. A model to predict vaginal delivery in nulliparous women based on maternal characteristics and intrapartum ultrasound. Am J Obstet Gynecol 2015; 213 (03) 362.e1-362.e6
    CrossrefPubMedGoogle Scholar
  • 18 Kawakita T, Reddy UM, Huang CC, Auguste TC, Bauer D, Overcash RT. Predicting vaginal delivery in nulliparous women undergoing induction of labor at term. Am J Perinatol 2018; 35 (07) 660-668
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 19 Levine LD, Downes KL, Parry S, Elovitz MA, Sammel MD, Srinivas SK. A validated calculator to estimate risk of cesarean after an induction of labor with an unfavorable cervix. Am J Obstet Gynecol 2018; 218 (02) 254.e1-254.e7
    CrossrefPubMedGoogle Scholar
  • 20 Tolcher MC, Holbert MR, Weaver AL. et al. Predicting cesarean delivery after induction of labor among nulliparous women at term. Obstet Gynecol 2015; 126 (05) 1059-1068
    CrossrefPubMedGoogle Scholar
  • 21 Ennen CS, Bofill JA, Magann EF, Bass JD, Chauhan SP, Morrison JC. Risk factors for cesarean delivery in preterm, term and post-term patients undergoing induction of labor with an unfavorable cervix. Gynecol Obstet Invest 2009; 67 (02) 113-117
    CrossrefPubMedGoogle Scholar
  • 22 Laughon SK, Zhang J, Troendle J, Sun L, Reddy UM. Using a simplified Bishop score to predict vaginal delivery. Obstet Gynecol 2011; 117 (04) 805-811
    CrossrefPubMedGoogle Scholar
  • 23 Rossi RM, Requarth E, Warshak CR, Dufendach KR, Hall ES, DeFranco EA. Risk calculator to predict cesarean delivery among women undergoing induction of labor. Obstet Gynecol 2020; 135 (03) 559-568
    CrossrefPubMedGoogle Scholar
  • 24 ACOG committee opinion no. 649: racial and ethnic disparities in obstetrics and gynecology. Obstet Gynecol 2015; 126 (06) e130-e134
    CrossrefPubMedGoogle Scholar
  • 25 Grobman WA, Lai Y, Landon MB. Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network. et al. Can a prediction model for vaginal birth after cesarean also predict the probability of morbidity related to a trial of labor?. Am J Obstet Gynecol 2009; 200 (01) 56.e1-56.e6
    CrossrefPubMedGoogle Scholar