Definition of Failed Induction of Labor and Its Predictive Factors: Two Unsolved Issues of an Everyday Clinical Situation.

OBJECTIVE The objectives of this review were to identify the predictive factors of induction of labor (IOL) failure or success as well as to highlight the current heterogeneity regarding the definition and diagnosis of failed IOL. MATERIALS AND METHODS Only studies in which the main or secondary outcome was failed IOL, defined as not entering the active phase of labor after 24 h of prostaglandin administration ± 12 h of oxytocin infusion, were included in the review. The data collected were: study design, definition of failed IOL, induction method, IOL indications, failed IOL rate, cesarean section because of failed IOL and predictors of failed IOL. RESULTS The database search detected 507 publications. The main reason for exclusion was that the primary or secondary outcomes were not the predetermined definition of failed IOL (not achieving active phase of labor). Finally, 7 studies were eligible. The main predictive factors identified in the review were cervical status, evaluated by the Bishop score or cervical length. DISCUSSION Failed IOL should be defined as the inability to achieve the active phase of labor, considering that the definition of IOL is to enter the active phase of labor. A universal definition of failed IOL is an essential requisite to analyze and obtain solid results and conclusions on this issue. An important finding of this review is that only 7 of all the studies reviewed assessed achieving the active phase of labor as a primary or secondary IOL outcome. Another conclusion is that cervical status remains the most important predictor of IOL outcome, although the value of the parameters explored up to now is limited. To find or develop predictive tools to identify those women exposed to IOL who may not reach the active phase of labor is crucial to minimize the risks and costs associated with IOL failure while opening a great opportunity for investigation. Therefore, other predictive tools should be studied in order to improve IOL outcome in terms of health and economic burden.