Faculty of Medicine and Health Sciences, Department of Paediatrics and Child Health, Stellenbosch University, Cape Town, South Africa
Division IC Neonatology (NICU), Department of Pediatrics, Amsterdam University Medical Centres, Amsterdam, Noord-Holland, The Netherlands.
BMJ Paediatr Open. 2023 Aug;7(1). doi: 10.1136/bmjpo-2023-002056.
Early diagnosis of neonatal infection is essential to prevent serious complications and to avoid unnecessary use of antibiotics. The prevalence of healthcare-associated infections (HAIs) among very low birthweight (VLBW; <1500 g) infants is 20%; and the mortality in low-resource settings can be as high as 70%. This study aimed to develop an Infection Prediction Score to diagnose bacterial HAIs.
A retrospective cohort of VLBW infants investigated for HAI was randomised into two unmatched cohorts. The first cohort was used for development of the score, and the second cohort was used for the internal validation thereof. Potential predictors included risk factors, clinical features, interventions, and laboratory data. The model was developed based on logistic regression analysis.
The study population of 655 VLBW infants with 1116 episodes of clinically suspected HAIs was used to develop the model. The model had five significant variables: capillary refill time >3 s, lethargy, abdominal distention, presence of a central venous catheter in the previous 48 hours and a C reactive protein ≥10 mg/L. The area below the receiver operating characteristic curve was 0.868. A score of ≥2 had a sensitivity of 54.2% and a specificity of 96.4%.
A novel Infection Prediction Score for HAIs among VLBW infants may be an important tool for healthcare providers working in low-resource settings but external validation needs to be performed before widespread use can be recommended.
早期诊断新生儿感染对于预防严重并发症和避免不必要地使用抗生素至关重要。极低出生体重(VLBW;<1500 克)婴儿的医源性感染(HAI)发生率为 20%;在资源匮乏的环境中,死亡率可能高达 70%。本研究旨在开发一种感染预测评分以诊断细菌性 HAI。
对疑似患有 HAI 的 VLBW 婴儿进行回顾性队列研究,并将其随机分为两个不匹配的队列。第一队列用于评分的开发,第二队列用于内部验证。潜在的预测因素包括危险因素、临床特征、干预措施和实验室数据。该模型是基于逻辑回归分析开发的。
本研究共纳入 655 例 VLBW 婴儿,共发生 1116 例疑似 HAI 发作。该模型有五个显著变量:毛细血管再充盈时间>3 秒、嗜睡、腹胀、48 小时内有中心静脉导管以及 C 反应蛋白≥10 mg/L。受试者工作特征曲线下面积为 0.868。评分≥2 时,敏感性为 54.2%,特异性为 96.4%。
对于资源匮乏环境中的 VLBW 婴儿,HAI 的新型感染预测评分可能是医疗保健提供者的重要工具,但在广泛推荐使用之前需要进行外部验证。