Characteristics of Subsequent (Index) Pregnancy in Women Experiencing Recurrent Pregnancy Loss

Methods: A retrospective cohort study including patients treated at the RPL clinic, with two or more consecutive pregnancy losses, followed by an index pregnancy. Multiple logistic regression models were used to control for confounders.

Results: A total of 675 patients were included in the study, of whom 177 had pregnancy loss. Maternal age at RPL work-up and at the beginning of the index pregnancy was significantly older at the pregnancy loss group (31.2±5.9 vs. 28.6±5.5 and 32.4±6.1 vs. 30.1±5.6. p<0.001).
No significant differences rates between the two groups regarding the ethnicity, maternal smoking, maternal chronic illness, clexane treatment, progesterone treatment and treatment was observed. Infertility Treatment was significantly more common in the pregnancy loss group (9.6% vs. 6.4%, p=0.002).

In the workup process, abnormal maternal karyotype as well as lupus anti-coagulant was more commonly observed in the pregnancy loss group. (14.2% vs. 7%, p=0.023 and 21.3% vs. 14.2%, p=0.049).
By using multiple logistic regression model (Table 1), controlling for confounders including maternal age, clexane treatment, number of previous pregnancy losses, fertility treatment, additional problems (abnormal male or female karyotype, elevated prolactin, abnormal fasting glucose, abnormal TSH, uterine anatomy abnormality, abnormal clotting test and APS), and ethnicity - maternal age at the index pregnancy and additional problems were found as independent risk factor for pregnancy loss in the index pregnancy.
Among the patient who had live-birth - 14.3% had cesarean section, 12.7% delivered at preterm and 1.2% had placenta abruption. The incidence of GDM was 10.4%, and the incidence of PET and IUGR was 4.6% each.

Conclusions: By using multiple logistic regression, controlling for confounders, maternal age at the index pregnancy and additional problems were found as independent risk factor for pregnancy loss in the index pregnancy.

Table 1: Multiple Logistic Regression