Department of Obstetrics and Gynaecology, Lagos University Teaching Hospital, Surulere, Lagos, Nigeria.
Department of Obstetrics and Gynaecology, College of Medicine, University of Lagos, Surulere, Lagos, Nigeria.
Int J Gynaecol Obstet. 2024 Jul;166(1):343-352. doi: 10.1002/ijgo.15364. Epub 2024 Jan 17.
There is currently a limited ability to accurately identify women at risk of postpartum hemorrhage (PPH). We conducted the "Predict-PPH" study to develop and evaluate an antepartum prediction model and its derived risk-scoring system.
This was a prospective cohort study of healthy pregnant women who registered and gave birth in five hospitals in Lagos, Nigeria, from January to June 2023. Maternal antepartum characteristics were compared between women with and without PPH. A predictive multivariable model was estimated using binary logistic regression with a backward stepwise approach eliminating variables when P was greater than 0.10. Statistically significant associations in the final model were reported when P was less than 0.05.
The prevalence of PPH in the enrolled cohort was 37.1%. Independent predictors of PPH such as maternal obesity (adjusted odds ratio [aOR] 3.25, 95% confidence interval [CI] 2.47-4.26), maternal anemia (aOR 1.32, 95% CI 1.02-1.72), previous history of cesarean delivery (aOR 4.24, 95% CI 3.13-5.73), and previous PPH (aOR 2.65, 95% CI 1.07-6.56) were incorporated to develop a risk-scoring system. The area under the receiver operating characteristic curve (AUROC) for the prediction model and risk scoring system was 0.72 (95% CI 0.69-0.75).
We recorded a relatively high prevalence of PPH. Our model performance was satisfactory in identifying women at risk of PPH. Therefore, the derived risk-scoring system could be a useful tool to screen and identify pregnant women at risk of PPH during their routine antenatal assessment for birth preparedness and complication readiness.
目前，准确识别产后出血（PPH）高危妇女的能力有限。我们开展了“Predict-PPH”研究，旨在开发和评估一种产前预测模型及其衍生的风险评分系统。
这是一项前瞻性队列研究，纳入了 2023 年 1 月至 6 月在尼日利亚拉各斯的五家医院登记并分娩的健康孕妇。比较了有和无 PPH 的孕妇的产前特征。使用二项逻辑回归估计预测性多变量模型，采用向后逐步法剔除 P 值大于 0.10 的变量。最终模型中的统计学显著关联的 P 值小于 0.05。
纳入队列中 PPH 的患病率为 37.1%。PPH 的独立预测因素，如母亲肥胖（调整后的优势比[aOR]3.25，95%置信区间[CI]2.47-4.26）、母亲贫血（aOR 1.32，95%CI 1.02-1.72）、既往剖宫产史（aOR 4.24，95%CI 3.13-5.73）和既往 PPH（aOR 2.65，95%CI 1.07-6.56）被纳入风险评分系统。预测模型和风险评分系统的受试者工作特征曲线下面积（AUROC）为 0.72（95%CI 0.69-0.75）。
我们记录了相对较高的 PPH 患病率。我们的模型在识别 PPH 高危妇女方面表现令人满意。因此，衍生的风险评分系统可能是一种有用的工具，可用于在常规产前评估中筛查和识别有 PPH 风险的孕妇，以做好分娩准备和并发症应对准备。
