Maternal Blood Angiogenic Factors and the Prediction of Critical Adverse Perinatal Outcomes Among Small-for-Gestational-Age Pregnancies

Abstract

Objective Our objective was to determine whether maternal blood angiogenic factors in suspected-small-for-gestational-age (sSGA) fetuses can predict critical adverse perinatal outcomes (CAPO) and improve risk assessment. Methods Women with singleton pregnancies diagnosed with sSGA, between 24 and 356/7 weeks' gestation, were included. Clinical and sonographic comprehensive evaluations were performed at enrolment. Plasma angiogenic factors, soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PlGF), were obtained at diagnosis. In parallel, three attending maternal-fetal-medicine specialists predicted the risk (1–5 scale) of these pregnancies to develop CAPO, based on the clinical presentation. CAPOs were defined as prolonged neonatal intensive care unit hospitalization, fetal or neonatal death, and major neonatal morbidity. Statistical analysis included sensitivity, specificity, positive and negative predictive values, and receiver-operating characteristic (ROC) curve analyses. Results Of the 79 cases included, 32 were complicated by CAPO (40.5%). In SGA fetuses with CAPO, the sFlt-1/PlGF ratio was higher (p < 0.001) and PlGF was lower (p < 0.001) as compared with uncomplicated pregnancies. The areas under the ROC curves for specialists were 0.913, 0.824, and 0.811 and for PlGF and sFlt-1/PlGF ratio 0.926 and 0.900, respectively. CAPO was more common in pregnancies with absent end-diastolic flow or reversed end-diastolic flow (AEDF or REDF) in the umbilical artery upon enrolment (91.6%). Yet, 65.6% of cases involving CAPO occurred in patients without AEDF or REDF, and 66.6% of these cases were not identified by one or more of the experts. The sFlt-1/PlGF ratio identified 92.9% of the experts' errors in this group and 100% of the errors in cases with AEDF or REDF. Conclusions Among sSGA pregnancies prior to 36 weeks' gestation, angiogenic factors testing can identify most cases later complicated with CAPO. Our data demonstrate for the first time that these markers can reduce clinician judgment errors. Incorporation of these measures into decision-making algorithms could potentially improve management, outcomes, and even health care costs. Key Points