Development and validation of an obstetric early warning system model for use in low resource settings

The use of obstetric early-warning-systems (EWS) has been recommended to improve timely recognition, management and early referral of women who have or are developing a critical illness. Development of such prediction models should involve a statistical combination of predictor clinical observations into a multivariable model which should be validated. No obstetric EWS has been developed and validated for low resource settings. We report on the development and validation of a simple prediction model for obstetric morbidity and mortality in resource-limited settings.

We performed a multivariate logistic regression analysis using a retrospective case-control analysis of secondary data with clinical indices predictive of severe maternal outcome (SMO). Cases for design and validation were randomly selected (n = 500) from 4360 women diagnosed with SMO in 42 Nigerian tertiary-hospitals between June 2012 and mid-August 2013. Controls were 1000 obstetric admissions without SMO diagnosis. We used clinical observations collected within 24 h of SMO occurrence for cases, and normal births for controls. We created a combined dataset with two controls per case, split randomly into development (n = 600) and validation (n = 900) datasets. We assessed the model's validity using sensitivity and specificity measures and its overall performance in predicting SMO using receiver operator characteristic (ROC) curves. We then fitted the final developmental model on the validation dataset and assessed its performance. Using the reference range proposed in the United Kingdom Confidential-Enquiry-into-Maternal-and-Child-Health 2007-report, we converted the model into a simple score-based obstetric EWS algorithm.

The final developmental model comprised abnormal systolic blood pressure-(SBP > 140 mmHg or < 90 mmHg), high diastolic blood pressure-(DBP > 90 mmHg), respiratory rate-(RR > 40/min), temperature-(> 38 °C), pulse rate-(PR > 120/min), caesarean-birth, and the number of previous caesarean-births. The model was 86% (95% CI 81-90) sensitive and 92%- (95% CI 89-94) specific in predicting SMO with area under ROC of 92% (95% CI 90-95%). All parameters were significant in the validation model except DBP. The model maintained good discriminatory power in the validation (n = 900) dataset (AUC 92, 95% CI 88-94%) and had good screening characteristics. Low urine output (300mls/24 h) and conscious level (prolonged unconsciousness-GCS < 8/15) were strong predictors of SMO in the univariate analysis.

We developed and validated statistical models that performed well in predicting SMO using data from a low resource settings. Based on these, we proposed a simple score based obstetric EWS algorithm with RR, temperature, systolic BP, pulse rate, consciousness level, urinary output and mode of birth that has a potential for clinical use in low-resource settings..