Individual patient data meta-analysis : Cervical stitch (cerclage) for preventing pregnancy loss in women.

Abstract

BackgroundCervical cerclage is a surgical procedure involving suturing the cervix with a purse type stitch to keep it closed during pregnancy. This procedure has been used widely in the management of pregnancies considered at high risk of preterm delivery. Several observational studies into the efficacy of cervical cerclage have claimed high rates of successful pregnancy outcome in women with a poor obstetric history attributed to cervical incompetence. However, a recent aggregate data Cochrane review found no such conclusive evidence from seven included randomised studies. Current data suggests that cervical cerclage is likely to benefit women considered to be 'at very high risk' of a second trimester miscarriage due to a cervical factor, however identifying such women remains elusive and many women may be treated unnecessarily. Undertaking an individual patient data (IPD) meta-analysis of the studies will allow us to investigate whether treatment is more effective in particular subgroups. Such an analysis will also provide a more powerful analysis of the predictors of preterm delivery and pregnancy loss, including ultrasound measurement of cervical length, and will allow a more complete analysis of 'time to event' outcomes.Methods/DesignThe analysis will include data from randomised trials comparing the intervention of elective cerclage versus no cerclage or bedrest to prevent miscarriage or pre-term labour. A specific list of data will be requested for each trial, including demographic and obstetric history data. The primary outcomes of interest will be neonatal mortality/morbidity. Attention will also be given to secondary outcomes such as time from randomisation to delivery, preterm delivery before 32 weeks and maternal morbidity. An intention to treat analysis will be performed, with attention paid to assessing clinical and statistical heterogeneity. Multilevel models with patients and trials as the two levels will be explored to investigate treatment effect on various outcomes. Patient-level covariates will be incorporated into the models in an attempt to account for statistical heterogeneity as well as to investigate interactions with treatment effect.DiscussionPredictive models generated from our analysis should lead to more effective counselling of women at risk and a more cost effective use of cerclage.