背景:早产是新生儿发病和死亡的主要原因,特别是在低资源环境中。如果对高危妊娠实施早期干预措施,大多数早产都可以预防。根据容易获得的预测因子开发早产的预后风险评分可以支持卫生专业人员作为其决策的简单临床工具。因此,该研究旨在开发和验证在DebreMarkos综合专科医院进行了产前检查的孕妇早产的预后风险评分模型,埃塞俄比亚。
方法:对1,132名孕妇进行了回顾性随访研究。客户图表是使用简单的随机抽样技术选择的。使用在KoboToolbox应用程序中准备的结构化清单提取数据,并将其导出到STATA版本14和R版本4.2.2以进行数据管理和分析。进行了逐步向后多变量分析。基于二元Logistic模型,建立了简化的风险预测模型,并通过鉴别力和校准来评估模型的性能。通过自举评估模型的内部有效性。使用决策曲线分析来确定模型的临床影响。
结果:早产发生率为10.9%。开发的风险评分模型由六个预测因子组成,这些预测因子保留在简化的多变量逻辑回归中,包括年龄<20岁,产前护理开始晚,意外怀孕,最近的妊娠并发症,血红蛋白<11mg/dl,多党,总分17分。模型的鉴别力为0.931,校正检验p>0.05。将风险分类为低或高的最佳界限为4。在这个切点,灵敏度,特异性和准确性为91.0%,82.1%,83.1%,分别。它得到了内部验证,乐观度为0.003。发现该模型具有临床益处。
结论:开发的风险评分具有出色的辨别性能和临床益处。它可以在医疗服务提供者的临床环境中用于早期检测,及时决策,提高护理质量。
BACKGROUND: Prematurity is the leading cause of neonatal morbidity and mortality, specifically in low-resource settings. The majority of prematurity can be prevented if early interventions are implemented for high-risk pregnancies. Developing a prognosis risk score for preterm birth based on easily available predictors could support health professionals as a simple clinical tool in their decision-making. Therefore, the
study aims to develop and validate a prognosis risk score model for preterm birth among pregnant women who had antenatal care visit at Debre Markos Comprehensive and Specialized Hospital, Ethiopia.
METHODS: A retrospective follow-up
study was conducted among a total of 1,132 pregnant women. Client charts were selected using a simple random sampling technique. Data were extracted using structured checklist prepared in the Kobo Toolbox application and exported to STATA version 14 and R version 4.2.2 for data management and analysis. Stepwise backward multivariable analysis was done. A simplified risk prediction model was developed based on a binary logistic model, and the model\'s performance was assessed by discrimination power and calibration. The internal validity of the model was evaluated by bootstrapping. Decision Curve Analysis was used to determine the clinical impact of the model.
RESULTS: The incidence of preterm birth was 10.9%. The developed risk score model comprised of six predictors that remained in the reduced multivariable logistic regression, including age < 20, late initiation of antenatal care, unplanned pregnancy, recent pregnancy complications, hemoglobin < 11 mg/dl, and multiparty, for a total score of 17. The discriminatory power of the model was 0.931, and the calibration test was p > 0.05. The optimal cut-off for classifying risks as low or high was 4. At this cut point, the sensitivity, specificity and accuracy is 91.0%, 82.1%, and 83.1%, respectively. It was internally validated and has an optimism of 0.003. The model was found to have clinical benefit.
CONCLUSIONS: The developed risk-score has excellent discrimination performance and clinical benefit. It can be used in the clinical settings by healthcare providers for early detection, timely decision making, and improving care quality.