Predictive scores for critically ill obstetric patients in a resource‐limited setting: A retrospective validation of the Obstetric Early Warning Score

• Published in: South African journal of obstetrics and gynaecology (1999) p. e191
• Main Authors: Kheswa, N, Rodseth, R, Wise, R, Bishop, D G
• Format: Journal Article
• Language: English
• Published: 20-06-2024
• ISSN: 0038-2329; 2305-8862
• DOI: 10.7196/SAJOG.2024.v30i1.191

Background. In South Africa (SA), an unacceptably high institutional maternal mortality rate persists due to failure to recognise critically ill patients. Early warning systems could assist in identifying these patients sooner. Objectives. We evaluated the Obstetric Early Warning Score (OEWS) as a predictor of maternal outcomes in an intensive care unit (ICU) and compared its prognostic validity with the Acute Physiology and Chronic Health Evaluation (APACHE) II and the quick Sequential Organ Failure Assessment score (qSOFA). Methods. Data were extracted on pregnant and post-partum ICU-admitted patients at a tertiary and regional centre in SA between October 2015 and April 2020. Clinical characteristics and outcomes were used to compare the three scoring systems. Results. Among 251 eligible patients, the mortality rate was 8.5%. The OEWS score failed to differentiate between survivors and non-survivors (odds ratio 1.13, 95% confidence intervals (CI) 0.972 - 1.311, p=0.113). APACHE II outperformed the OEWS (area under the receiver operating characteristics curve (AUROC) 0.69, 95% CI 0.540 - 0.846 v. 0.55, 95% CI 0.430 - 0.674). The OEWS (AUROC 0.55, 95% CI 0.430 - 0.674) and qSOFA (AUROC 0.60, 95% CI 0.500 - 0.703) showed no differences. Further analysis revealed that positive scoring for diastolic blood pressure and high systolic blood pressure weakened OEWS performance. Removing these variables improved OEWS prediction (AUROC 0.68). Conclusion. In a SA obstetric population, OEWS did not predict mortality in ICU-admitted patients and offered no advantages over APACHE II or qSOFA scores. Further research should identify critical outcome predictors for low-resource populations.