OBJECTIVES: This study develops a prediction model for preterm birth with preeclampsia, addressing challenges such as different gestational age at first presentation, time-dependency, and births without preeclampsia. STUDY DESIGN: Data were obtained from a retrospective real-world pregnancy cohort. MAIN OUTCOME MEASURES: A two-stage landmark prediction model was constructed. First, longitudinal trajectories of biomarkers (mean arterial pressure, mean pulsatility index of uterine arteries, soluble fms-like tyrosine kinase-1 to placental growth factor ratio) were modeled at predefined landmark time points using a linear mixed-effects model. Second, based on Cox proportional hazards models the probability of preterm preeclampsia was predicted. RESULTS: Of 2,930 pregnancies, 311 resulted in a preterm delivery with current diagnosis of preeclampsia. Unadjusted estimates yielded a probability of preterm delivery with preeclampsia of 25% (95% confidence interval 21-28%). Across all landmark time points, the model demonstrated good sensitivity (76-89%) and specificity (65-77%) for predicting preterm preeclampsia by gestational week 37 and within the next two weeks (sensitivity 61-89% and specificity 65-95%). However, the low positive predictive value (11-23%) indicates a high false-discovery rate. Compared to a simple strategy based solely on the soluble fms-like tyrosine kinase-1 to placental growth factor ratio, the new prediction model showed disadvantages for detection of preterm preeclampsia by gestational week 37 but advantages for preterm preeclampsia within the next two weeks. CONCLUSION: The two-stage landmark prediction model may support early identification of pregnancies at elevated risk for preeclampsia in high-risk cohorts. However, the high false-positive rate might result in an over-surveillance.
Stegherr et al. (Tue,) studied this question.