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A sequential guide to identify neonates with low bacterial meningitis risk: a multicenter study.
Yan Chen1, Zhanghua Yin1, Xiaohui Gong2
1Department of Neonatology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Annals of Clinical and Translational Neurology
|April 9, 2021
Summary
This study developed a predictive algorithm to identify bacterial meningitis (BM) risk in newborns. The algorithm accurately stratifies neonates, aiding clinical decisions regarding lumbar puncture (LP).
Area of Science:
- Neonatal Medicine
- Infectious Diseases
- Clinical Diagnostics
Background:
- Bacterial meningitis (BM) is a serious infection in neonates.
- Accurate risk stratification is crucial for timely diagnosis and treatment.
- Current methods may lead to unnecessary lumbar punctures (LP).
Purpose of the Study:
- To develop and validate a predictive algorithm for neonatal bacterial meningitis risk.
- Integrate clinical and laboratory parameters for risk stratification.
- Improve decision-making for lumbar puncture (LP) procedures.
Main Methods:
- Multicentered dataset of 1072 full-term neonates (≤28 days) with lumbar puncture (LP).
- Dataset divided into derivation (689) and external validation (383) cohorts.
- Stepwise regression analysis to identify predictive factors: fever, infection source absence, neurological signs, C-reactive protein (CRP), and procalcitonin.
Main Results:
- The algorithm identified 96.1% of BM cases in the derivation set.
- 50.7% of neonates were classified as low risk.
- High sensitivity (96.2%) and negative predictive value (NPV, 98.9%) for low-risk classification.
- Validation set showed similar performance: sensitivity 95.9%, NPV 98.8%.
Conclusions:
- A sequential algorithm effectively stratifies neonatal bacterial meningitis risk.
- The algorithm demonstrates excellent predictive performance.
- A valuable tool for clinicians to guide lumbar puncture (LP) decisions.
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