Prediction of cesarean delivery in class III obese nulliparous women: An externally validated model using machine learning.
Cesarean delivery
Machine learning
Obesity
Personalized medicine
Predictive model
Predictor selection
Random forests
Journal
Journal of gynecology obstetrics and human reproduction
ISSN: 2468-7847
Titre abrégé: J Gynecol Obstet Hum Reprod
Pays: France
ID NLM: 101701588
Informations de publication
Date de publication:
Sep 2023
Sep 2023
Historique:
received:
11
05
2023
revised:
11
06
2023
accepted:
12
06
2023
medline:
11
9
2023
pubmed:
16
6
2023
entrez:
15
6
2023
Statut:
ppublish
Résumé
class III obese women, are at a higher risk of cesarean section during labor, and cesarean section is responsible for increased maternal and neonatal morbidity in this population. the objective of this project was to develop a method with which to quantify cesarean section risk before labor. this is a multicentric retrospective cohort study conducted on 410 nulliparous class III obese pregnant women who attempted vaginal delivery in two French university hospitals. We developed two predictive algorithms (a logistic regression and a random forest models) and assessed performance levels and compared them. the logistic regression model found that only initial weight and labor induction were significant in the prediction of unplanned cesarean section. The probability forest was able to predict cesarean section probability using only two pre-labor characteristics: initial weight and labor induction. Its performances were higher and were calculated for a cut-point of 49.5% risk and the results were (with 95% confidence intervals): area under the curve 0.70 (0.62,0.78), accuracy 0.66 (0.58, 0.73), specificity 0.87 (0.77, 0.93), and sensitivity 0.44 (0.32, 0.55). this is an innovative and effective approach to predicting unplanned CS risk in this population and could play a role in the choice of a trial of labor versus planned cesarean section. Further studies are needed, especially a prospective clinical trial. French state funds "Plan Investissements d'Avenir" and Agence Nationale de la Recherche.
Sections du résumé
BACKGROUND
BACKGROUND
class III obese women, are at a higher risk of cesarean section during labor, and cesarean section is responsible for increased maternal and neonatal morbidity in this population.
OBJECTIVE
OBJECTIVE
the objective of this project was to develop a method with which to quantify cesarean section risk before labor.
METHODS
METHODS
this is a multicentric retrospective cohort study conducted on 410 nulliparous class III obese pregnant women who attempted vaginal delivery in two French university hospitals. We developed two predictive algorithms (a logistic regression and a random forest models) and assessed performance levels and compared them.
RESULTS
RESULTS
the logistic regression model found that only initial weight and labor induction were significant in the prediction of unplanned cesarean section. The probability forest was able to predict cesarean section probability using only two pre-labor characteristics: initial weight and labor induction. Its performances were higher and were calculated for a cut-point of 49.5% risk and the results were (with 95% confidence intervals): area under the curve 0.70 (0.62,0.78), accuracy 0.66 (0.58, 0.73), specificity 0.87 (0.77, 0.93), and sensitivity 0.44 (0.32, 0.55).
CONCLUSIONS
CONCLUSIONS
this is an innovative and effective approach to predicting unplanned CS risk in this population and could play a role in the choice of a trial of labor versus planned cesarean section. Further studies are needed, especially a prospective clinical trial.
FUNDING
BACKGROUND
French state funds "Plan Investissements d'Avenir" and Agence Nationale de la Recherche.
Identifiants
pubmed: 37321400
pii: S2468-7847(23)00091-0
doi: 10.1016/j.jogoh.2023.102624
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
102624Informations de copyright
Copyright © 2023 Elsevier Masson SAS. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors have no competing interests to declare.