The role of maternal blood elabela levels in the prediction of placenta previa and accreta

Placenta previa and Placenta Accreta Spectrum are life-threatening obstetric conditions that are challenging to diagnose accurately. Currently, there is no biochemical parameter available for their diagnosis. The aim of our study is to investigate the potential of Elabela as a laboratory marker that could predict placenta previa and placenta accreta, both of which can lead to severe, life-threatening complications for the mother. In this study, which was conducted prospectively in two tertiary centers between 2020 and 2022, Elabela levels were examined in patient groups with placental insertion and invasion anomalies. SPSS program was used for comparative statistical analysis between groups. Of the 67 analyzed patients, 32 were in the control group, 12 were in the previa group, and 23 were in the accreta group. There was no statistically significant difference between the groups regarding age, BMI, number of curettages, presence of previous cesarean section, and smoking status. The Elabela level was measured at 135.6 ± 72.1 in the control group, 988.3 ± 925.5 in the previa group, and 376 ± 364.6 in the accreta group, with a statistically significant difference between the groups. The cut-off value of Elabela levels in the previa group was determined to be 304, with a sensitivity of 83.30 % and a specificity of 83.60 % (AUC = 0.909). In the accreta group, the cut-off value was 195.5, with a sensitivity of 60.90 % and a specificity of 61.40 % (AUC = 0.658). By showing that the prediction of placenta previa and placenta acreata can be made with a biochemical parameter in our study, young researchers will focus more on this subject and thus make many contributions to science. •Placental invasion anomalies are important conditions that can affect the life of the mother.•Predicting placental invasion anomalies is very important to prevent possible morbidity and mortality.•Measurement of maternal Elabela level in the antenatal period may be useful for the prediction of PAS.