Canadian Network for International Surgery, #212-1650 Duranleau St, Vancouver, BC, V6H 3S4, Canada.
Centre for Sustainability & Resilient Infrastructure & Communities, London South Bank University, London, UK.
Glob Health Res Policy. 2022 Feb 11;7(1):6. doi: 10.1186/s41256-022-00240-8.
High rates of maternal mortality in low-and-middle-income countries (LMICs) are associated with the lack of skilled birth attendants (SBAs) at delivery. Risk analysis tools may be useful to identify pregnant women who are at risk of mortality in LMICs. We sought to develop and validate a low-cost maternal risk tool, the Community Maternal Danger Score (CMDS), which is designed to identify pregnant women who need an SBA at delivery.
To design the CMDS algorithm, an initial scoping review was conducted to identify predictors of the need for an SBA. Medical records of women who delivered at the Federal Medical Centre in Makurdi, Nigeria (2019-2020) were examined for predictors identified from the literature review. Outcomes associated with the need for an SBA were recorded: caesarean section, postpartum hemorrhage, eclampsia, and sepsis. A maternal mortality ratio (MMR) was determined. Multivariate logistic regression analysis and area under the curve (AUC) were used to assess the predictive ability of the CMDS algorithm.
Seven factors from the literature predicted the need for an SBA: age (under 20 years of age or 35 and older), parity (nulliparity or grand-multiparity), BMI (underweight or overweight), fundal height (less than 35 cm or 40 cm and over), adverse obstetrical history, signs of pre-eclampsia, and co-existing medical conditions. These factors were recorded in 589 women of whom 67% required an SBA (n = 396) and 1% died (n = 7). The MMR was 1189 per 100,000 (95% CI 478-2449). Signs of pre-eclampsia, obstetrical history, and co-existing conditions were associated with the need for an SBA. Age was found to interact with parity, suggesting that the CMDS requires adjustment to indicate higher risk among younger multigravida and older primigravida women. The CMDS algorithm had an AUC of 0.73 (95% CI 0.69-0.77) for predicting whether women required an SBA, and an AUC of 0.85 (95% CI 0.67-1.00) for in-hospital mortality.
The CMDS is a low-cost evidence-based tool that uses 7 risk factors assessed on 589 women from Makurdi. Non-specialist health workers can use the CMDS to standardize assessment and encourage pregnant women to seek an SBA in preparation for delivery, thus improving care in countries with high rates of maternal mortality.
在中低收入国家(LMICs),孕产妇死亡率居高不下与分娩时缺乏熟练的接生员(SBAs)有关。风险分析工具可能有助于识别在 LMICs 中处于死亡风险的孕妇。我们试图开发和验证一种低成本的产妇风险工具,即社区产妇危险评分(CMDS),旨在识别需要在分娩时接受 SBA 的孕妇。
为了设计 CMDS 算法,我们进行了初步的范围界定审查,以确定需要 SBA 的预测因素。检查了 2019-2020 年在尼日利亚马库尔迪联邦医疗中心分娩的妇女的医疗记录,以确定文献综述中确定的预测因素。记录与需要 SBA 相关的结局:剖宫产、产后出血、子痫和败血症。确定了孕产妇死亡率比(MMR)。使用多变量逻辑回归分析和曲线下面积(AUC)评估 CMDS 算法的预测能力。
文献中的七个因素预测了需要 SBA:年龄(<20 岁或 35 岁及以上)、产次(初产妇或多产妇)、BMI(体重不足或超重)、宫底高度(<35cm 或>40cm)、不良产科史、子痫前期迹象和并存疾病。在 589 名妇女中记录了这些因素,其中 67%(n=396)需要 SBA,1%(n=7)死亡。MMR 为每 100,000 人 1189 人(95%CI 478-2449)。子痫前期迹象、产科史和并存疾病与需要 SBA 有关。年龄与产次相互作用,表明 CMDS 需要调整,以表明年轻的多产妇和年长的初产妇风险更高。CMDS 算法预测妇女是否需要 SBA 的 AUC 为 0.73(95%CI 0.69-0.77),预测院内死亡率的 AUC 为 0.85(95%CI 0.67-1.00)。
CMDS 是一种基于证据的低成本工具,使用了在马库尔迪的 589 名妇女评估的 7 个风险因素。非专业卫生工作者可以使用 CMDS 进行标准化评估,并鼓励孕妇在分娩前寻求 SBA,从而改善高孕产妇死亡率国家的护理。