Development of a Comprehensive Antenatal Risk Assessment Tool to Predict Adverse Maternal and Perinatal Outcomes in Rural Areas: An Exploratory Study

Abstract

Objective: To develop a comprehensive antenatal risk assessment tool to predict adverse maternal and early perinatal outcomes in a rural setting. Materials and methods: Cross-sectional study among women admitted for delivery in a rural maternity hospital, south India. Risk factors from Rotterdam Reproductive Risk Reduction (R4U) scorecard and social factors relevant to Indian rural context were included in questionnaire. Maternal and perinatal outcomes were obtained from in-patient records. Logistic regression of risk factors associated with adverse outcomes and weighted scores assigned using beta-coefficients. Cut-off score to predict adverse outcome was derived using Receiver Operator Characteristic Curve (ROC Curve) and Likelihood ratios. Results: Adjusted odds for adverse outcome highest for small for gestational age by ultrasound scan [OR=7.4 (1.4-36.5)], tobacco chewing [OR=5.6 (1.8-28.5)] and hypertensive disorders of pregnancy [OR=3.5 (1.9-9.6)]. After assigning weighted scores, the 74-item antenatal risk assessment tool had a maximum possible score of 86. Risk score was calculated for all subjects. Cut-off score to predict adverse outcome was 4, using ROC curve, with a sensitivity of 98%, a specificity of 21% and positive likelihood ratio of 1.23 (1.10-1.37). Conclusion: This comprehensive antenatal risk assessment tool is easy to administer, specific to rural areas and can help community-level workers to screen, monitor, and refer high risk pregnancies for further management to prevent adverse maternal and perinatal outcomes. This may be considered a prototype towards developing more robust antenatal risk screening and outcome prediction in rural settings.

Keywords: Perinatal Outcomes; Pregnancy; Risk Prediction; Score Card.

Figure 1

Flowchart depicting creation of a Comprehensive Antenatal Risk Assessment Tool

Figure 2

ROC curve showing maximum area under the curve (0.78) at the cut off of 4 with sensitivity of 98% and specificity of 21% for predicting adverse outcomes.