Department of International Public Health, Centre for Maternal and Newborn Health, Liverpool School of Tropical Medicine, Liverpool, UK.
BMC Pregnancy Childbirth. 2020 Sep 11;20(1):531. doi: 10.1186/s12884-020-03215-0.
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..
使用产科预警系统（EWS）已被推荐用于改善对出现或正在发展为危急疾病的女性的及时识别、管理和早期转介。此类预测模型的开发应涉及将预测临床观察结果统计组合到多变量模型中，然后对该模型进行验证。目前还没有针对资源有限环境开发和验证的产科 EWS。我们报告了一种用于资源有限环境中产科发病率和死亡率的简单预测模型的开发和验证。
我们对来自尼日利亚 42 家三级医院的 4360 名诊断为严重产妇结局（SMO）的女性进行了回顾性病例对照分析，使用多元逻辑回归分析来进行设计和验证。随机选择病例（n=500）用于设计和验证，对照组为 1000 名无 SMO 诊断的产科入院患者。我们使用 SMO 发生后 24 小时内收集的病例临床观察结果和对照组的正常分娩。我们创建了一个包含两个对照每个病例的合并数据集，随机分为开发（n=600）和验证（n=900）数据集。我们使用敏感性和特异性测量来评估模型的有效性，并使用接收器工作特征（ROC）曲线来评估其在预测 SMO 中的整体性能。然后，我们在验证数据集上拟合最终的开发模型，并评估其性能。我们使用英国产妇和儿童健康保密咨询 2007 年报告中提出的参考范围，将模型转换为简单的基于分数的产科 EWS 算法。
最终的开发模型包括异常收缩压（SBP>140mmHg 或<90mmHg）、高舒张压（DBP>90mmHg）、呼吸频率（RR>40/min）、体温（>38°C）、脉搏率（PR>120/min）、剖宫产和剖宫产次数。该模型对 SMO 的预测敏感性为 86%（95%CI 81-90%），特异性为 92%-（95%CI 89-94%），ROC 曲线下面积为 92%（95%CI 90-95%）。除 DBP 外，所有参数在验证模型中均有统计学意义。该模型在验证（n=900）数据集上保持良好的区分能力（AUC 92，95%CI 88-94%），且具有良好的筛查特征。低尿量（300ml/24h）和意识水平（昏迷时间延长-GCS<8/15）在单因素分析中是 SMO 的强预测因素。
我们使用来自资源有限环境的数据开发和验证了预测 SMO 的统计模型，这些模型的性能良好。基于这些模型，我们提出了一种简单的基于分数的产科 EWS 算法，该算法具有 RR、体温、收缩压、脉搏率、意识水平、尿量和分娩方式等参数，有可能在资源有限的环境中临床应用。
