BackgroundPlatelet parameters during pregnancy were associated with the risk of preeclampsia (PE), but the predictive value of these parameters for PE remained unclear. Our aim was to clarify the individual and incremental predictive value of platelet parameters, including platelet count (PC), mean platelet volume (MPV), plateletcrit (PCT), and platelet distribution width (PDW) for PE.MethodsThis study was based on the Born in Guangzhou Cohort Study in China. Data on platelet parameters were extracted from medical records of routine prenatal examinations. Receiver operating characteristic (ROC) curve was performed to analyze the predictive ability of platelet parameters for PE. Maternal characteristic factors proposed by NICE and ACOG were used to develop the base model. Detection rate (DR), integrated discrimination improvement (IDI) and continuous net reclassification improvement (NRI) were calculated compared with the base model to assess the incremental predictive value of platelet parameters.ResultsA total of 30,401 pregnancies were included in this study, of which 376 (1.24%) were diagnosed with PE. Higher levels of PC and PCT were observed at 12–19 gestational weeks in women who developed PE later. However, no platelet parameters before 20 weeks of gestation reliably distinguished between PE complicated pregnancy and non-PE complicated pregnancy, with all values of the areas under the ROC curves (AUC) below 0.70. The addition of platelet parameters at 16–19 gestational weeks to the base model increased the DR for preterm PE from 22.9 to 31.4% at a fixed false positive rate of 5%, improved the AUC from 0.775 to 0.849 (p= 0.015), and yielded a NRI of 0.793 (p< 0.001), and an IDI of 0.0069 (p= 0.035). Less but significant improvement in prediction performance was also observed for term PE and total PE when all the four platelet parameters were added to the base model.ConclusionsAlthough no single platelet parameter at the early stage of pregnancy identified PE with high accuracy, the addition of platelet parameters to known independent risk factors could improve the prediction of PE.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12884-023-05661-y.