Simple SummaryThe prediction of pre-eclampsia (PE) is a crucial task both medically and socioeconomically. Recently, several biomarkers have been developed with clinically promising results. However, the currently identified markers face the challenges of their applicability in the clinical settings due to factors such as cost and measurement platform. According to our study, incorporating serum creatinine (SCr) levels that can be easily derived from a real-world hospital database along with prior knowledge of hyperfiltration, which is a kidney-specific physiological adaptation in pregnancy, improved PE prediction significantly. The model developed in this study is practical and can be easily applied in primary care settings without requiring significant hospital database upgrades.AbstractPre-eclampsia (PE) is a pregnancy-related disease, causing significant threats to both mothers and babies. Numerous studies have identified the association between PE and renal dysfunction. However, in clinical practice, kidney problems in pregnant women are often overlooked due to physiologic adaptations during pregnancy, including renal hyperfiltration. Recent studies have reported serum creatinine (SCr) level distribution based on gestational age (GA) and demonstrated that deviations from the expected patterns can predict adverse pregnancy outcomes, including PE. This study aimed to establish a PE prediction model using expert knowledge and by considering renal physiologic adaptation during pregnancy. This retrospective study included pregnant women who delivered at the Wonju Severance Christian Hospital. Input variables, such as age, gestational weeks, chronic diseases, and SCr levels, were used to establish the PE prediction model. By integrating SCr, GA, GA-specific SCr distribution, and quartile groups of GA-specific SCr (GAQ) were made. To provide generalized performance, a random sampling method was used. As a result, GAQ improved the predictive performance for any cases of PE and triple cases, including PE, preterm birth, and fetal growth restriction. We propose a prediction model for PE consolidating readily available clinical blood test information and pregnancy-related renal physiologic adaptations.