背景:新生儿早发性败血症(EOS)是一种严重的疾病,会在出生后的头三天内影响新生儿,死亡率很高,特别是在低收入和中等收入国家(LMICs)。在越南,EOS的诊断和管理受到临床体征模糊和血液培养检测设施有限的挑战.使用预测风险因素模型早期识别高危新生儿对于改善新生儿护理和降低死亡率至关重要。
目的:本研究旨在确定与EOS相关的孕产妇和新生儿危险因素,并开发一种预测性筛查工具,以促进越南高危新生儿的早期发现。
方法:在越南西南部一家主要三级医院的中央新生儿病房,对225名新生儿进行了为期两年的巢式病例对照研究。使用单变量和多变量逻辑回归分析确定危险因素。开发了预测列线图,并对其进行了区分评估,校准,和决策曲线分析(DCA)。
结果:该研究确定了EOS的八个重要风险因素,包括妊娠晚期的母体生殖器感染,怀孕期间尿路感染(UTI),妊娠期高血压,孕妇体重增加不足,胎膜破裂(ROM)≥18小时,羊水胎粪污染,第一分钟APGAR评分<7,早产<34周。预测模型显示出出色的区分度,曲线下面积(AUC)为0.913(95%CI:0.876-0.95,p<0.001)和良好的校准(Hosmer-Lemeshow检验,χ²(df)=5.496(5),p=0.358)。基于模型的列线图在0.25的最佳截止值下显示出高灵敏度(82.7%)和特异性(83.3%)。DCA说明了该模型的良好临床实用性,在大多数阈值概率范围(0.0-0.96)提供更高的净收益。
结论:这项研究为越南有EOS风险的新生儿的早期识别提供了一个可靠的预测模型,基于关键的孕产妇和新生儿危险因素。模型,具有证明的准确性和可靠性,通过及时干预改善新生儿结局具有巨大潜力。未来的研究应着眼于外部验证和纳入更广泛的临床数据,以增强模型的适用性和普适性。
BACKGROUND: Neonatal early-onset sepsis (EOS) is a severe condition that affects newborns within the first three days of life, with high mortality rates, particularly in low- and middle-income countries (LMICs). In Vietnam, the diagnosis and management of EOS are challenged by ambiguous clinical signs and limited access to blood culture testing facilities. Early identification of at-risk neonates using a predictive risk factor model is crucial for improving neonatal care and reducing mortality.
OBJECTIVE: This study aims to identify maternal and neonatal risk factors associated with EOS and develop a predictive screening tool to facilitate the early detection of at-risk neonates in Vietnam.
METHODS: A nested case-control study was conducted on 225 neonates at the central neonatal unit in a principal tertiary hospital in southwestern Vietnam over a two-year period. Risk factors were identified using univariable and multivariable logistic regression analyses. A predictive nomogram was developed and evaluated for discrimination, calibration, and decision curve analysis (DCA).
RESULTS: The study identified eight significant risk factors for EOS, including maternal genital infections during the third trimester, urinary tract infections (UTIs) during pregnancy, hypertension during pregnancy, insufficient maternal weight gain, rupture of membranes (ROM) ≥18 hours, meconium-stained amniotic fluid, first-minute APGAR score <7, and preterm birth <34 weeks. The predictive model demonstrated excellent discrimination with an area under the curve (AUC) of 0.913 (95% CI: 0.876-0.95, p<0.001) and good calibration (Hosmer-Lemeshow test with χ²(df)=5.496 (5), p=0.358). The model-based nomogram showed high sensitivity (82.7%) and specificity (83.3%) at an optimal cutoff of 0.25. The DCA illustrates the model\'s good clinical utility, providing a higher net benefit across most threshold probability ranges (0.0-0.96).
CONCLUSIONS: This study presents a robust predictive model for the early identification of neonates at risk of EOS in Vietnam, based on key maternal and neonatal risk factors. The model, with demonstrated accuracy and reliability, holds significant potential for improving neonatal outcomes through timely interventions. Future research should aim at external validation and inclusion of broader clinical data to enhance the model\'s applicability and generalizability.