{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T19:46:53Z","timestamp":1765828013023,"version":"3.40.5"},"reference-count":33,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T00:00:00Z","timestamp":1617926400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81671501"],"award-info":[{"award-number":["81671501"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ann Clin Transl Neurol"],"published-print":{"date-parts":[[2021,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To derive and validate a predictive algorithm integrating clinical and laboratory parameters to stratify a full\u2010term neonate's risk level of having bacterial meningitis (BM).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A multicentered dataset was categorized into derivation (689 full\u2010term neonates aged \u226428\u00a0days with a lumbar puncture [LP]) and external validation (383 neonates) datasets. A sequential algorithm with risk stratification for neonatal BM was constructed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the derivation dataset, 102 neonates had BM (14.8%). Using stepwise regression analysis, fever, infection source absence, neurological manifestation, C\u2010reactive 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\u2019s sensitivity and negative predictive value (NPV) in identifying neonates at low risk of BM were 96.2% (95% CI 91.7%\u201398.9%) and 98.9% (95% CI 97.6%\u201399.6%), respectively. In the validation dataset, sensitivity and NPV were 95.9% (95% CI 91.0%\u2013100%) and 98.8% (95% CI 97.7%\u2013100%).<\/jats:p><\/jats:sec><jats:sec><jats:title>Interpretation<\/jats:title><jats:p>The sequential algorithm can risk stratify neonates for BM with excellent predictive performance and prove helpful to clinicians in LP\u2010related decision\u2010making.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acn3.51356","type":"journal-article","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T17:39:40Z","timestamp":1617989980000},"page":"1132-1140","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A sequential guide to identify neonates with low bacterial meningitis risk: a multicenter study"],"prefix":"10.1002","volume":"8","author":[{"given":"Yan","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Neonatology Xinhua Hospital School of Medicine Shanghai Jiao Tong University Shanghai 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