Preeclampsia (PE) is a serious pregnancy-related condition that causes severe maternal and fetal morbidity and mortality. Within the recent years, there has been an increasing focus in predicting PE at the end of the first trimester of pregnancy. In this review, literature published between 2011 and 2015 was evaluated. In a total of six biomarker algorithms, for first and early second trimester, the prediction of preeclampsia is discussed. In addition, one randomized clinical trial was included. Several algorithms were based on placental biomarkers such as pregnancy-associated plasma protein A (PAPP-A), placental growth factor (PLGF), and soluble FMS-like tyrosine kinase 1 (s-FLT-1). The algorithms containing these biomarkers showed a high prediction rate (PR) for early onset PE, ranging from 44 to 92 % at 5 % false positive rate (FPR). New biomarkers suggest an alternative model based on free HbF and the heme scavenger alpha-1-microglobulin (A1M) with a prediction rate of 69 % at an FPR of 5 %. Interestingly, this model performs well without uterine artery Doppler pulsatility index (UtAD-PI), which is an advantage particularly if the screening method were to be implemented in developing countries. The randomized clinical trial showed a clear reduction in early onset PE as well as reducing preterm PE if identified high-risk pregnancies were treated with low-dose aspirin. In conclusion, PE prediction is now possible through several prediction algorithms and prophylaxis is beneficial in high-risk cases.