Raden Aditya Kusuma1,2, Detty Siti Nurdiati3, Adly Nanda Al Fattah4,5, Didi Danukusumo6, Sarini Abdullah7, Ivan Sini8,9. 1. Department of Obstetrics and Gynecology, Harapan Kita National Women and Children Hospital, Letjen S. Parman Street, Number Kav 87, Palmerah, West Jakarta, 11420, Jakarta, Indonesia. samtida98@gmail.com. 2. Indonesian Prenatal Institute, Jakarta, Indonesia. samtida98@gmail.com. 3. Department of Obstetrics and Gynecology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Dr. Sardjito Hospital, Yogyakarta, Indonesia. 4. Indonesian Prenatal Institute, Jakarta, Indonesia. 5. Kosambi Maternal and Children Center, Jakarta, Indonesia. 6. Department of Obstetrics and Gynecology, Harapan Kita National Women and Children Hospital, Letjen S. Parman Street, Number Kav 87, Palmerah, West Jakarta, 11420, Jakarta, Indonesia. 7. Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Jakarta, Indonesia. 8. Morula IVF Jakarta Clinic, Jakarta, Indonesia. 9. IRSI Research and Training Centre, Jakarta, Indonesia.
Abstract
OBJECTIVE: To develop a Bayesian survival-time model for the prediction of pre-eclampsia (PE) at the first trimester using a combination of established biomarkers including maternal characteristics and history, mean arterial pressure (MAP), uterine artery Doppler pulsatility index (UtA-PI), and Placental Growth Factor (PlGF)) with an ophthalmic artery Doppler peak ratio (PR) analysis. METHODS: The receiving operator curve (ROC) analysis was used to determine the area under the curve (AUC), detection rate (DR), and positive screening cut-off value of the model in predicting the occurrence of early-onset PE (< 34 weeks' gestation) and preterm PE (< 37 weeks' gestation). RESULTS: Of the 946 eligible participants, 71 (7.49%) subjects were affected by PE. The incidences of early-onset and preterm PE were 1% and 2.2%, respectively. At a 10% false-positive rate, using the high-risk cut-off 1:49, with AUC 0.981 and 95%CI 0.965-0.998, this model had an 100% of DR in predicting early-onset PE. The DR of this model in predicting preterm PE is 71% when using 1:13 as the cut-off, with AUC 0.919 and 95%CI 0.875-0.963. CONCLUSION: Combination ophthalmic artery Doppler PR with the previously established biomarkers could improve the accuracy of early and preterm PE prediction at the first trimester screening.
OBJECTIVE: To develop a Bayesian survival-time model for the prediction of pre-eclampsia (PE) at the first trimester using a combination of established biomarkers including maternal characteristics and history, mean arterial pressure (MAP), uterine artery Doppler pulsatility index (UtA-PI), and Placental Growth Factor (PlGF)) with an ophthalmic artery Doppler peak ratio (PR) analysis. METHODS: The receiving operator curve (ROC) analysis was used to determine the area under the curve (AUC), detection rate (DR), and positive screening cut-off value of the model in predicting the occurrence of early-onset PE (< 34 weeks' gestation) and preterm PE (< 37 weeks' gestation). RESULTS: Of the 946 eligible participants, 71 (7.49%) subjects were affected by PE. The incidences of early-onset and preterm PE were 1% and 2.2%, respectively. At a 10% false-positive rate, using the high-risk cut-off 1:49, with AUC 0.981 and 95%CI 0.965-0.998, this model had an 100% of DR in predicting early-onset PE. The DR of this model in predicting preterm PE is 71% when using 1:13 as the cut-off, with AUC 0.919 and 95%CI 0.875-0.963. CONCLUSION: Combination ophthalmic artery Doppler PR with the previously established biomarkers could improve the accuracy of early and preterm PE prediction at the first trimester screening.