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Predictive model for risk of cesarean section in pregnant women after induction of labor.

Abstract：

PURPOSE:To develop a predictive model for risk of cesarean section in pregnant women after induction of labor. METHODS:A retrospective cohort study was conducted of 861 induced labors during 2009, 2010, and 2011 at Hospital "La Mancha-Centro" in Alcázar de San Juan, Spain. Multivariate analysis was used with binary logistic regression and areas under the ROC curves to determine predictive ability. Two predictive models were created: model A predicts the outcome at the time the woman is admitted to the hospital (before the decision to of the method of induction); and model B predicts the outcome at the time the woman is definitely admitted to the labor room. RESULTS:The predictive factors in the final model were: maternal height, body mass index, nulliparity, Bishop score, gestational age, macrosomia, gender of fetus, and the gynecologist's overall cesarean section rate. The predictive ability of model A was 0.77 [95% confidence interval (CI) 0.73-0.80] and model B was 0.79 (95% CI 0.76-0.83). The predictive ability for pregnant women with previous cesarean section with model A was 0.79 (95% CI 0.64-0.94) and with model B was 0.80 (95% CI 0.64-0.96). For a probability of estimated cesarean section ≥80%, the models A and B presented a positive likelihood ratio (+LR) for cesarean section of 22 and 20, respectively. Also, for a likelihood of estimated cesarean section ≤10%, the models A and B presented a +LR for vaginal delivery of 13 and 6, respectively. CONCLUSION:These predictive models have a good discriminative ability, both overall and for all subgroups studied. This tool can be useful in clinical practice, especially for pregnant women with previous cesarean section and diabetes.

journal_name

Arch Gynecol Obstet

journal_title

Archives of gynecology and obstetrics

authors

Hernández-Martínez A,Pascual-Pedreño AI,Baño-Garnés AB,Melero-Jiménez MR,Tenías-Burillo JM,Molina-Alarcón M

doi

10.1007/s00404-015-3856-1

subject

Has Abstract

pub_date

2016-03-01 00:00:00

pages

529-38

issue

3

eissn

0932-0067

issn

1432-0711

pii

10.1007/s00404-015-3856-1

journal_volume

293

pub_type

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