Prediction of an effective cervical ripenning in the induction of labour using vaginal dinoprostone.
Sci Rep
; 13(1): 6855, 2023 04 26.
Article
em En
| MEDLINE
| ID: mdl-37100837
To develop a predictive model for successful cervical ripening in women that undergo induction of labour by means of a vaginal prostaglandin slow-release delivery system (Propess®). Prospective observational study on 204 women that required induction of labour between February 2019 and May 2020 at "La Mancha Centro" hospital in Alcázar de San Juan, Spain. The main variable studied was effective cervical ripening (Bishop score > 6). Using multivariate analysis and binary logistic regression, we created three initial predictive models (model A: Bishop Score + Ultrasound cervical length + clinical variables (estimated fetal weight, premature rupture of membranes and body mass index)); model B: Ultrasound cervical lenght + clinical variables; and model C: Bishop score + clinical variables) to predict effective cervical ripening. All three predictive models obtained (A, B and C) presented good predictive capabilities, with an area under the ROC curve ≥ 0.76. Predictive model C, composed of the variables: gestational age (OR 1.55, 95% CI 1.18-2.03, p = 0.002), premature rupture of membranes (OR 3.21 95% CI 1.34-7.70, p = 0.09) body mass index (OR 0.93, 95% CI 0.87-0.98, p = 0.012), estimated fetal weight (OR 0.99, 95% CI 0.99-1.00, p = 0.068) and Bishop score (OR 1.49 95% CI 1.18-1.81, p = 0.001), is presented as the model of choice with an area under the ROC curve of 0.76 (95% CI 0.70-0.83, p < 0.001). A predictive model composed of the variables: gestational age, premature rupture of membranes, body mass index, estimated fetal weight and Bishop score upon admission presents good capabilities in predicting successful cervical ripening following administration of prostaglandins. This tool could be useful in making clinical decisions with regard to induction of labour.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Dinoprostona
/
Parto Obstétrico
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Female
/
Humans
/
Pregnancy
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article