Incidence and first trimester risk factors of stillbirth in Indonesia

OBJECTIVES

To determine the incidence and the risk factors of stillbirth from maternal biophysical, ultrasound, and biochemical markers at 11-13 weeks of gestation in the Indonesian population.

METHODS

This was a retrospective cohort study of pregnant women for first-trimester preeclampsia screening at 11-13 weeks of gestation in some clinics and hospital in Jakarta. Maternal characteristics and history, mean arterial pressure (MAP) measurement, uterine artery pulsatility index (UtA-PI) ultrasound, maternal ophthalmic peak ratio (Oph-PR) Doppler, and placental growth factor (PlGF) serum were collected during the visit. Stillbirth was classified into placental dysfunction-related when it occurred with preeclampsia or birth weight <10th percentile and non-placental dysfunction-related. Bivariate and multivariate logistic regression analyses were employed to determine the risk factors associated with stillbirth.

RESULTS

Of 1,643 eligible participants, 13 (0.79 %) stillbirth cases were reported. More than half of the stillbirths (7) were placental dysfunction-related. After adjusted with maternal age, body mass index (BMI), and parity status, chronic hypertension (aOR (adjusted odds ratio)) 24.41, 95 % CI {confidence interval} 5.93-100.43), previous pregnancy with preeclampsia (aOR 15.79, 95 % CI 4.42-56.41), MAP >101.85 (aOR 26.67, 95 % CI 8.26-86.06), UtA-PI >1.90 (aOR 10.68, 95 % CI 2.34-48.58, and PlGF <28.77 pg/mL (aOR 18.60, 95 % CI 5.59-61.92) were associated with stillbirth.

CONCLUSIONS

The incidence of stillbirth in the population is comparable to studies conducted in developed countries. Most routine variables assessed at the 11-13 weeks combined screening for preeclampsia are associated with the risk of stillbirth.

Ophthalmic artery Doppler for pre-eclampsia prediction at the first trimester: a Bayesian survival-time model

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.

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

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.