Machine learning-based prediction of preeclampsia using first-trimester inflammatory markers and red blood cell indices.

BACKGROUND

Preeclampsia (PE) affects 2–4% of pregnancies, and early detection and intervention can reduce its incidence. Dysregulation of the maternal immune response and red blood cells (RBCs) are key to its development, although early alterations remain unclear.

METHODS

This study analyzed data from 17,955 pregnant women across two centers to explore the relationships among inflammatory markers, RBC indices, and PE via multivariate logistic regression and restricted cubic splines (RCSs). Machine learning integrated inflammatory markers, RBC indices, and maternal risk factors to predict PE risk at 14 weeks, as validated by receiver operating characteristic (ROC) curve analysis.

RESULTS

After adjusting for confounders, the lymphocyte (LYMPH) count (OR = 1.27, 95% CI: 1.05–1.53, P = 0.013), monocyte (MONO) count (OR = 2.57, 95% CI: 1.31–5.03, P = 0.006), systemic inflammatory response index (SIRI) (OR = 1.11, 95% CI: 1.01–1.21, P = 0.032), and systemic immune inflammatory index (SII) (OR = 1.01, 95% CI: 1.01–1.01, P = 0.002) were identified as significant risk factors for PE. Nonlinear associations between white blood cell (WBC) count, neutrophil (NEUT) count, platelet (PLT) count, RBC count, and hemoglobin (HGB) and PE were observed via RCS (nonlinear P < 0.05). Further analysis revealed threshold effects for WBC (P = 0.034), with an inflection point at 8.44. Below 8.44, no significant association was found (OR = 0.92, P = 0.307), but above 8.44, each unit increase was linked to a 0.14-fold rise in PE risk (OR = 1.14, P < 0.001). Similar threshold effects were found for the PLT, RBC, and HGB (P < 0.001). A prediction model based on inflammatory markers, RBC indices, and maternal risk factors achieved high performance (ROC = 0.82).

CONCLUSIONS

LYMPH, MONO, SIRI, and SII were linearly associated with PE, whereas WBC, NEUT, PLT, RBC, and HGB showed nonlinear associations with threshold effects. Early prediction using these indicators is a cost-effective strategy for PE prevention.