Systemic lupus erythematosus (SLE) is a chronic, autoimmune disease associated with major obstetrical complications such as gestational loss, preterm delivery, fetal growth restriction (FGR) and preeclampsia. Published literature is not consensual regarding the main risk factors for each of these outcomes. Our goal with this study was to determine the most important predictors for each of the main adverse pregnancy outcomes in this population. We conducted a retrospective cohort study of unifetal pregnancies of women with the diagnosis of SLE followed in our unit between January 1994 and December 2016. We excluded elective terminations of pregnancy and cases lost to follow-up and we analyzed 157 pregnancies (128 women). Multiple logistic regression models for the outcomes gestational loss, preterm delivery, fetal growth restriction, and preeclampsia were built. Twosided p-values of < 0.05 were used to determine statistical significance, and two-sided confidence intervals of 95% are reported. In our cohort, the main risk factors for gestational loss were maternal age and the presence of antiphospholipid antibodies. Lupic nephritis was predictive of a preterm delivery and preeclampsia. Renal involvement and lupus flares during pregnancy were risk factors for FGR. Overall, the main risk factor for an adverse pregnancy outcome were lupus flares during pregnancy. Despite optimal pregnancy monitoring, women with SLE are still at risk for adverse pregnancy outcomes. Risk stratification for each of these outcomes is crucial for an effective counselling and tailored monitoring.

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Electrohysterography (EHG) is a promising technique for pregnancy monitoring and preterm risk evaluation. It allows for uterine contraction monitoring as early as the 20th gestational week, and it is a non-invasive technique based on recording the electric signal of the uterine muscle activity from electrodes located in the abdominal surface. In this work, EHG-based contraction detection methodologies are applied using signal envelope features. Automatic contraction detection is an important step for the development of unsupervised pregnancy monitoring systems based on EHG. The exploratory methodologies include wavelet energy, Teager energy, root mean square (RMS), squared RMS, and Hilbert envelope. In this work, two main features were evaluated: contraction detection and its related delineation accuracy. The squared RMS produced the best contraction (97.15 ± 4.66%) and delineation (89.43 ± 8.10%) accuracy and the lowest false positive rate (0.63%). Despite the wavelet energy method having a contraction accuracy (92.28%) below the first-rated method, its standard deviation was the second best (6.66%). The average false positive rate ranged between 0.63% and 4.74%—a remarkably low value.