Abstract
Induction of Labour (IOL) is an important practice that is carried out commonly in modern day obstetrics. In medium to large healthcare facilities in Sri Lanka, it is estimated that approximately 35.5% of all deliveries involve IOL. This research attempts to identify the factors that affect IOL and to assess the association between IOL and the pregnancy outcome. In this study, we considered 18309 women who were admitted to 14 healthcare facilities for delivery in 3 randomly selected provinces in Sri Lanka (Western, Southern and Eastern provinces), during July to October 2011. Multinomial Logistic Regression model (MLR) and Fuzzy Expert System (FES) were used to identify the factors that lead to IOL. The MLR model predicts IOL with a classification rate of 65.5% and the FES predicts IOL with an accuracy of 55.10%. Maternal age, number of previous caesarian sections, number of previous births, estimated gestational age, Pre-Eclampsia, number of previous pregnancies, Placenta Preavia, Abruption Placenta, total number of neonates delivered, birth weight and Maternal Severity Index (MSI) were identified as factors associated with IOL. Neonatal status after seven days of life can also be predicted using the developed FES. FES is predictive of IOL and birth outcome, where if the FES score is between 0.8570 and 0.8854, the patient will belong to the induced group and the baby would be alive after seven days of birth. This study concludes that, MLR and FES models can be used to predict IOL outcomes. These findings can be informative to healthcare providers when counselling women for labour induction and develop evidence-based protocols on IOL.
Highlights
Induction of labour (IOL) is a common procedure in obstetrics (Laws, Li, & Sullivan, 2010), (“National Institute for Health and Clinical Excellence: Guidance,” 2008), and is defined as the initiation of labour by artificial means prior to its spontaneous onset at a viable gestational age, with the aim of achieving vaginal delivery in a pregnant woman with intact membranes
Multinomial Logistic Regression (MLR) model consists of variables maternal age, number of previous caesarian sections, number of previous births, estimate gestational age, Pre-Eclampsia, number of previous pregnancies, Placenta Preavia and Abruption Placenta; which are useful predictors for distinguishing between categories of onset of labour
The MLR model predict IOL with a classification rate of 65.5% and the Fuzzy Expert System (FES) predict IOL with an accuracy of 55.10%
Summary
Induction of labour (IOL) is a common procedure in obstetrics (Laws, Li, & Sullivan, 2010), (“National Institute for Health and Clinical Excellence: Guidance,” 2008), and is defined as the initiation of labour by artificial means prior to its spontaneous onset at a viable gestational age, with the aim of achieving vaginal delivery in a pregnant woman with intact membranes. It is estimated that in medium to large healthcare facilities in Sri Lanka, approximately 35.5% of all deliveries involve IOL (World Health Organization, 2011). IOL is thought to be a factor in reducing maternal mortality by preventing maternal complications and improving pregnancy outcome (Hiluf & Assefa, 2015). The findings of this study can inform healthcare providers when counselling women for labour induction; develop evidence-based protocols on IOL within the local-context, and improve future quality of care provided for woman who need labour induction in hospitals
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