Clinical evaluation of a first trimester pregnancy algorithm predicting the risk of small for gestational age neonates. (24th January 2019) - Graham, Kathryn; Park, Felicity; McLennan, Andrew; Pelosi, Marilena; Williams, Paul; Poon, Liona C.; Hyett, Jon
Abstract : Background: The Fetal Medicine Foundation developed a multiple logistic regression algorithm for risk prediction of delivering a small for gestational age neonate. Aim: To validate this algorithm in an Australian population. Methods: At the combined first trimester screen participants' medical histories, demographic data, mean arterial pressure, uterine artery pulsatility index and pregnancy‐associated plasma protein‐A were assessed. After delivery, risk of delivering a small for gestational age neonate at <37 or ≥37 weeks gestation was retrospectively calculated using the Fetal Medicine Foundation algorithm. Results: Three thousand and eight women underwent prediction of risk for delivering a small for gestational age neonate. The algorithm detected 15.0% (95% CI: 3.2–37.9) of small for gestational age neonates delivered <37 weeks gestation at a fixed 10% false positive rate (or 35.0% (95% CI: 15.4–59.2) at a fixed 20% false positive rate). It detected 23.4% (95% CI: 16.1–30.7) of small for gestational age neonates delivered ≥37 weeks gestation at a fixed 10% false positive rate (or 39.1% (95% CI: 30.7–47.5) at a fixed 20% false positive rate). The algorithm performed significantly better than individual parameters ( P < 0.05). The area under the receiver operating characteristic curve was 0.68 (95% CI: 0.56–0.80) and 0.70 (95% CI: 0.65–0.74) for small for gestational age neonates at <37 and ≥37 weeks gestation, respectively. Conclusions: The Fetal Medicine Foundation algorithm for first trimester prediction of small for gestational age neonates does not perform as well in an Australian population as in the original United Kingdom cohort. However, it performs significantly better than any individual test parameter in both preterm and term neonates. Incorporation of further variables may help improve screening efficacy.