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Prospective evaluation of point-of-care placental growth factor in Sierra Leone.

Kuhrt, K.; Cole, R.; M'Bayoh, M.; Mabula-Bwalya, C.; Hurrell, A.; Ridout, A.; Fernandez-Turienzo, C.; Seed, P. T.; Chappell, L. C.; Bramham, K.; Shennan, A. H.

2025-03-17 obstetrics and gynecology
10.1101/2025.03.14.25324010 medRxiv
Show abstract

ObjectivesPre-eclampsia is a major cause of maternal death. Placental growth factor (PlGF) testing improves time-to-diagnosis and outcomes. We evaluated two novel, whole blood, point-of-care (POC) PlGF tests (RONIATM and Lepzi(R) Quanti PlGF) in a low-resource setting, for prediction of adverse outcomes. Study DesignA prospective observational cohort study in hypertensive pregnant women, 24-36+6 weeks gestation, at a tertiary maternity hospital in Sierra Leone. MethodsEligible, consented women underwent RONIATM and/or Lepzi(R) Quanti PLGF testing; results were concealed. Optimal rule-out and rule-in thresholds were determined for prediction of predefined maternal (maternal death, eclampsia) and perinatal (stillbirth, termination pre-viability, neonatal death before discharge) composite outcomes. Sensitivity, specificity, negative (NPV) and positive predictive values were determined. ResultsAnalysis was performed on women with complete outcomes: RONIATM n=488 and Lepzi(R) Quanti PlGF n=140. Optimal thresholds were <60pg/mL or <90pg/mL (rule-out) and <20pg/mL or <12pg/mL (rule-in) for RONIATM and Lepzi(R) Quanti PlGF respectively. For tests performed <34 weeks gestation, RONIATM PlGF <60pg/mL had high sensitivity, 94.9% (95%CI 82.7-99.4%) and NPV, 94.6% (95%CI 81.8-99.3%) for maternal outcomes, with sensitivity, 100% (95%CI 95.8-100.0%) and NPV, 100% (95%CI 90.5-100%) for the perinatal composite. Lepzi(R) Quanti PlGF < 90pg/mL had 100% sensitivity and NPV for all predefined maternal and neonatal outcomes. Performance reduced slightly at later gestations. ConclusionsWhole blood POC-PlGF measurement demonstrates accurate rule-out performance of two novel devices for serious outcomes, with potential for individualised risk stratification in low-resource settings.

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