sFlt-1, Coagulation Function, and Platelets as Predictors of Preeclampsia.

Objective: To investigate the predictive value of soluble FMS-like tyrosine kinase-1 (sFlt-1), coagulation function, and platelet (PLT) parameters for preeclampsia (PE).

Methods: A prospective study was conducted on women registered and delivered at XXXX Hospital from October 2020 to December 2021. All eligible pregnant women were recruited at the time of initial registration in the first trimester. And we then obtained serum samples uniformly at 24-28 weeks and stored these samples in a freezer at -80°C and labeled them to create a biobank. Later, when PE was diagnosed, we followed the markers to find their blood samples and complete the tests. Participants were divided into healthy pregnant (HP) and PE groups. Participants were divided into healthy pregnant (HP) and PE groups. Approximately 5 mL of venous blood was collected from each participant at 24-28 weeks gestation. Serum sFlt-1 was measured by enzyme-linked immunosorbent assay (ELISA). Additionally, D-dimer (D-D), activated partial thromboplastin time (APTT), thrombin time (TT), prothrombin time (PT), antithrombin III (ATIII), fibrinogen (FIB), PLT, platelet distribution width (PDW), and mean platelet volume (MPV) were recorded. SPSS 27.0 software was used to analyze the correlation of these parameters with PE. Receiver operating characteristic (ROC) curve analysis determined the optimal cutoff value for each parameter.

Results: Serum sFlt-1, APTT, TT, ATIII, PLT, MPV, and PDW levels were significantly different between the PE and HP groups (P < 0.05). Among single factor indicators for predicting PE, sFlt-1 exhibited the highest value. With an optimal cutoff value of 4.409 ng/ml, sFlt-1 demonstrated a sensitivity and specificity of 85.4% and 87.5%, respectively. The combination of sFlt-1, APTT, TT, PDW, and MPV yielded the highest predictive value, with an area under the ROC curve (AUC) of 0.946, sensitivity of 86.8%, and specificity of 87.5%.

Conclusions: This study demonstrates that a combination of sFlt-1, APTT, TT, PDW, and MPV is a valuable tool for predicting PE.