Alternatives of Risk Prediction Models for Preeclampsia in a Low Middle-Income Setting

Raden Aditya Kusuma; Detty Siti Nurdiati; Siswanto Agus Wilopo

doi:10.3889/oamjms.2022.9030

Authors

Raden Aditya Kusuma Department of Obstetrics and Gynecology, Harapan Kita National Women and Children Hospital, Jakarta, Indonesia
Detty Siti Nurdiati Department of Obstetrics and Gynecology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Dr. Sardjito Hospital, Yogyakarta, Indonesia
Siswanto Agus Wilopo Department of Biostatistics, Epidemiology and Population Health, Universitas Gadjah Mada, Yogyakarta, Indonesia

DOI:

https://doi.org/10.3889/oamjms.2022.9030

Keywords:

Pre-eclampsia, Prediction model, First trimester

Abstract

Objectives: To develop prediction models for the first-trimester prediction of PE (PE) using the established biomarkers including maternal characteristics and history, mean arterial pressure (MAP), uterine artery Doppler pulsatility index (UtA-PI ), and Placental Growth Factor (PlGF)) in combination with Ophthalmic artery Doppler peak ratio (PR).

Methods: This was a prospective observational study in women attending a first-trimester screening at 11-14 weeks’ gestation. Maternal characteristics and history, measurement of MAP, ultrasound examination for UtA-PI measurement, maternal ophthalmic PR Doppler measurement, and serum PlGF collection were performed during the visit. Logistic regression analysis was used to determine if the maternal factor had a significant contribution in predicting PE. The Receiving Operator Curve (ROC) analysis was used to determine the area under the curve (AUC), positive predictive value (PPV), negative prefictive value (NPV) and positive screening cut-off in predicting the occurrence of PE at any gestational age.

Results: Of the 946 eligible participants, 71 (7,49%) subjects were affected by PE. Based on the ROC curves, optimal high-risk cutoff value for prediction of preeclampsia at any gestational age for model 2 (primary care model) in this Indonesia study population were 63% with the sensitivity and specificity of 71.8% and 71.2%, respectively. Both sensitivity and specificity for model 3 (complete model) were 70.4% and 74.9%, respectively for the cutoff value 58%. The area under the curve of model 2, model 3 was 0.7651 (95% CI: 0.7023-0.8279)) and 0.7911 (95% CI: 0.7312-0.8511), respectively, for predicting PE. In addition, PPV and NPV for model 2 were 16.8% and 96.9%, respectively. PPV and NPV for model 3 were 18.55 and 96.9%, respectively.

Conclusion: The prediction models of preeclampsia vary depending upon healthcare resource. Complete model is clinically superior to primary care model but it is not statistically significant. Prognostic models should be easy to use, informative and low cost with great potential to improve maternal and neonatal health in Low Middle Income Country settings.

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