Annals of clinical and translational neurology · 2021
A sequential guide to identify neonates with low bacterial meningitis risk: a multicenter study.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Chen Yan, Yin Zhanghua, Gong Xiaohui, Li Jing, Zhong Wenhua, Shan Liqin, Lei Xiaoping, Zhang Qian, Zhou Qin, Zhao Youyan, Chen Chao, Zhang Yongjun
Аннотация
OBJECTIVE: To derive and validate a predictive algorithm integrating clinical and laboratory parameters to stratify a full-term neonate's risk level of having bacterial meningitis (BM).
METHODS: A multicentered dataset was categorized into derivation (689 full-term neonates aged ≤28 days with a lumbar puncture [LP]) and external validation (383 neonates) datasets. A sequential algorithm with risk stratification for neonatal BM was constructed.
RESULTS: In the derivation dataset, 102 neonates had BM (14.8%). Using stepwise regression analysis, fever, infection source absence, neurological manifestation, C-reactive protein (CRP), and procalcitonin were selected as optimal predictive sets for neonatal BM and introduced to a sequential algorithm. Based on the algorithm, 96.1% of BM cases (98 of 102) were identified, and 50.7% of the neonates (349 of 689) were classified as low risk. The algorithm's sensitivity and negative predictive value (NPV) in identifying neonates at low risk of BM were 96.2% (95% CI 91.7%-98.9%) and 98.9% (95% CI 97.6%-99.6%), respectively. In the validation dataset, sensitivity and NPV were 95.9% (95% CI 91.0%-100%) and 98.8% (95% CI 97.7%-100%).
INTERPRETATION: The sequential algorithm can risk stratify neonates for BM with excellent predictive performance and prove helpful to clinicians in LP-related decision-making.
METHODS: A multicentered dataset was categorized into derivation (689 full-term neonates aged ≤28 days with a lumbar puncture [LP]) and external validation (383 neonates) datasets. A sequential algorithm with risk stratification for neonatal BM was constructed.
RESULTS: In the derivation dataset, 102 neonates had BM (14.8%). Using stepwise regression analysis, fever, infection source absence, neurological manifestation, C-reactive protein (CRP), and procalcitonin were selected as optimal predictive sets for neonatal BM and introduced to a sequential algorithm. Based on the algorithm, 96.1% of BM cases (98 of 102) were identified, and 50.7% of the neonates (349 of 689) were classified as low risk. The algorithm's sensitivity and negative predictive value (NPV) in identifying neonates at low risk of BM were 96.2% (95% CI 91.7%-98.9%) and 98.9% (95% CI 97.6%-99.6%), respectively. In the validation dataset, sensitivity and NPV were 95.9% (95% CI 91.0%-100%) and 98.8% (95% CI 97.7%-100%).
INTERPRETATION: The sequential algorithm can risk stratify neonates for BM with excellent predictive performance and prove helpful to clinicians in LP-related decision-making.