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

Autor: Hernández-Martínez A, Pascual-Pedreño AI, Baño-Garnés AB, Melero-Jiménez MR, Tenías-Burillo JM, Molina-Alarcón M
Rok vydání: 2016
Předmět:
Zdroj: ARCHIVES OF GYNECOLOGY AND OBSTETRICS
r-FISABIO. Repositorio Institucional de Producción Científica
instname
r-FISABIO: Repositorio Institucional de Producción Científica
Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO)
ISSN: 0932-0067
Popis: 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 Alcazar 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
Databáze: OpenAIRE
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