RPL: Interval to Live Birth and Adverse Perinatal Outcomes

Overview

This will be a retrospective observational cohort study utilizing the data from the British Columbia Perinatal Data Registry (BCPDR). The BCPDR is a provincially inclusive database that aggregates obstetrics and neonatal variables from all attended births in British Columbia. The primary objective of this study is to evaluate and contrast the average time interval from the first to second birth for patients with recurrent pregnancy loss compared to healthy controls. Secondarily, the investigators will calculate the cumulative live birth rate in the cohort of women with recurrent pregnancy loss who were \</= 35 at age of first birth and delivered between the years 2000-2010. Finally, the investigators will compare the incidence of adverse perinatal outcomes for those with recurrent pregnancy loss and those without. The results of this study will be valuable for clinicians and patients as it will provide information for prognosis counselling. This will also help those desiring more than one child with long term family planning.

Recurrent pregnancy loss (RPL) is defined as 2 or more pregnancy losses by the European Society of Human Reproduction and Embryology and affects 2-5% of the population. RPL can be further classified into primary and secondary pregnancy losses, with secondary RPL diagnosed when one has achieved a previous live birth. RPL is a frustrating and distressing condition as nearly 50% of patients have no medical explanation for the cause of pregnancy loss. For these patients, research regarding the prognosis and time to live birth is essential. This will be a retrospective observational cohort study utilizing the data from the British Columbia Perinatal Data Registry (BCPDR). The BCPDR is a provincially inclusive database that aggregates obstetrics and neonatal variables from all attended births in British Columbia. Since April 2000, there are up to 700, 000 births included in this database. The study population will be divided into 4 groups: those without RPL (control group), all patients with RPL, primary RPL, and secondary RPL. Within the RPL group, primary RPL is defined by those with \>/=2 pregnancy loss before 20 weeks gestational age before the first birth and the remaining patients will make up the secondary RPL group. The following demographic variables will be requested: age at first delivery, age at second delivery, the average number of pregnancy loss before first birth, number of pregnancy loss before second birth, BMI at first delivery, BMI at second delivery, principle residence, use of in vitro fertilization to conceive, obstetrical status (number of pregnancies, term deliveries, preterm deliveries, miscarriages, and living children) at first and second birth, school years completed, smoking, at-risk alcohol use, preexisting hypertension, preexisting diabetes, psychiatric illnesses, the number of infant(s) born in first and second delivery, live birth/stillbirth. Primary outcome variables include the date (year, month, and day) of delivery for the first and second live births. For our second objective, the investigators will analyze the cumulative live birth rate of a subgroup who is \</= 35 at age of first birth and first delivery recorded between the year 2000-2010. The investigators will assume that this cohort will be more likely to try for a second pregnancy rather than an older group. Furthermore, there will be 10 to 20 years of follow-up data from the year 2010. A Kaplan-meier curve will be constructed showing the cumulative live birth rate for patients with RPL, primary RPL, secondary RPL, and control group, controlling for age, BMI, city of residence, use of IVF, number of miscarriages, consecutive RPL, and school years completed. Lastly, the medical and obstetric characteristics of the pregnancies resulting in live birth after the diagnosis of RPL will be analyzed between the 4 groups (primary RPL, secondary RPL, all RPL, control). The associations between the previous diagnosis of RPL and adverse perinatal outcomes will be determined using logistic regression analyses.