Association and predictive performance of platelet-to-lymphocyte ratio and maternal serum ferritin level for preterm birth: A prospective cohort study

Abstract

Although maternal inflammation is increasingly recognized in the pathogenesis of preterm birth, evidence regarding the combined predictive value of platelet-to-lymphocyte ratio (PLR) and maternal serum ferritin levels remains scarce. The aim of this study was to evaluate the association and predictive performance of PLR and maternal serum ferritin levels, two readily available inflammatory biomarkers, with the incidence of preterm birth. A prospective cohort study was conducted. Pregnant women with singleton pregnancies between 28 and 36 weeks 6 days of gestation were consecutively recruited and followed until delivery. PLR was calculated from absolute platelet and lymphocyte counts obtained from complete blood count analysis, while maternal serum ferritin levels were measured using electrochemiluminescence immunoassay. The present study found that higher PLR (odds ratio (OR): 1.013; 95%CI: 1.002–1.025; p=0.025) and maternal serum ferritin levels (OR: 1.016; 95%CI: 1.002–1.031; p=0.029) were significantly associated with the incidence of preterm birth. In multivariable analysis, PLR ≥134.4 (adjusted OR: 3.701; 95%CI: 1.186–11.548; p=0.024) and maternal serum ferritin levels ≥29.3 ng/mL (adjusted OR: 4.513; 95%CI: 1.351–15.075; p=0.014) remained independently associated with preterm birth. Receiver operating characteristic analysis demonstrated very modest discriminatory performance for both PLR (area under the curve (AUC): 0.632, p=0.076) and maternal serum ferritin levels (AUC: 0.645, p=0.052), with optimal cut-off values of 134.4 and 29.3 ng/mL, respectively. These findings support the contribution of maternal inflammatory processes to the development of preterm birth and suggest that PLR and maternal serum ferritin levels may provide complementary information for risk assessment. However, the modest predictive performance observed indicates that these biomarkers are unlikely to be sufficient as standalone screening tools and should be interpreted alongside established clinical predictors.