Workineh Yinager, Alene Getu Degu, Fekadu Gedefaw Abeje
Department of Midwifery, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia.
Department of Epidemiology and Biostatistics, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia.
PLoS One. 2025 Jul 10;20(7):e0328069. doi: 10.1371/journal.pone.0328069. eCollection 2025.
Maternal near-miss is a serious public health concern in impoverished countries such as Ethiopia. Despite its huge burden, the prognostic predictive model of maternal near-miss has received little attention in research in the Ethiopian context. As a result, this study aimed to build and validate (internally) a clinical prediction model of maternal near-miss in Bahir Dar City, Northwest Ethiopia, in 2024.
A prospective follow-up study was conducted among 2110 randomly selected pregnant women in Bahir Dar city between May 1, 2023, and March 6, 2024. Pregnant women with gestational age less than 20 weeks were included in the cohort and followed up to 42 days after delivery. Data were extracted from antenatal care records and collected by an interview-administered questionnaire. The model was developed using the standard Cox regression model, and model fitness was checked using the Schoenfeld assumption test. After applying a stepwise elimination, a p-value of less than 0.15 was used to fit the reduced model. Both discrimination and calibration were used to assess the model's performance. The model was internally validated through the bootstrapping method. The clinical usefulness of the model was checked using decision curve analysis. A nomogram was used for the model presentation.
Maternal near-miss incidence density rate was 1.94 per 1,000 woman-weeks. Maternal age, residence, decision-making power, intention to pregnancy, time of antenatal initiation, genital mutilation, history of cesarean section, middle upper arm circumference, systolic blood pressure, hemoglobin, and history of obstetric morbidity were identified as important predictors to predict maternal near-miss. The model demonstrated good discriminatory performance with a C-index of 0.82(95%CI: 0.80-0.85), and good calibration with close alignment with 45 degrees. A simplified risk score of 40 maximum points was developed. The model was presented using a nomogram.
The maternal near-miss incidence density rate was high in the present study. Socio-demographic and clinical factors were key variables for predicting maternal near-miss. The model has good discrimination and calibration. The researchers recommend external validation in different settings to assess the model's generalizability before applying it to clinical settings.
在埃塞俄比亚等贫困国家，孕产妇接近死亡是一个严重的公共卫生问题。尽管负担巨大，但在埃塞俄比亚的研究中，孕产妇接近死亡的预后预测模型很少受到关注。因此，本研究旨在构建并（在内部）验证2024年埃塞俄比亚西北部巴赫达尔市孕产妇接近死亡的临床预测模型。
2023年5月1日至2024年3月6日期间，对巴赫达尔市随机选取的2110名孕妇进行了前瞻性随访研究。孕周小于20周的孕妇被纳入队列，并随访至产后42天。数据从产前保健记录中提取，并通过访谈问卷收集。使用标准Cox回归模型开发模型，并使用Schoenfeld假设检验检查模型拟合度。在应用逐步剔除后，使用小于0.15的p值来拟合简化模型。使用区分度和校准来评估模型性能。通过自助法对模型进行内部验证。使用决策曲线分析检查模型的临床实用性。使用列线图展示模型。
孕产妇接近死亡的发病密度率为每1000妇女周1.94例。孕产妇年龄、居住地、决策权、怀孕意愿、产前开始时间、女性生殖器切割、剖宫产史、上臂中段周长、收缩压、血红蛋白和产科发病史被确定为预测孕产妇接近死亡的重要预测因素。该模型显示出良好的区分性能，C指数为0.82（95%CI：0.80 - 0.85），校准良好，与45度线紧密对齐。开发了一个最高40分的简化风险评分。使用列线图展示模型。
本研究中孕产妇接近死亡的发病密度率较高。社会人口学和临床因素是预测孕产妇接近死亡的关键变量。该模型具有良好的区分度和校准。研究人员建议在不同环境中进行外部验证，以在将模型应用于临床环境之前评估其可推广性。
