Development and Validation of a Multivariable Predictive nomogram for Diagnosis of Neonatal Bacterial Meningitis

Purpose: To develop and validate a multivariable predictive nomogram to diagnose neonatal bacterial meningitis (NBM). Methods: A retrospective study was conducted and neonates with positive blood culture were included. Infants were divided into training and validation set. A predictive nomogram was established according to finding of univariate analysis and multivariate logistic regression. The diagnostic value was judged by receiver operating characteristic curves (ROC). Results: Among the 587 infants included finally, 177 of them developed NBM. Among training set (N=410), the incidence of abnormal temperature, seizure, bulging anterior fontanel were more common in NBM group ( P <0.05). Higher of protein, WBC and lower glucose in cerebrospinal fluid (CSF) were found in NBM group ( P <0.05). Bulging anterior fontanel, seizure, abnormal temperature, abnormal protein and WBC in CSF were considered as the independent predictors of NBM. A predictive nomogram was established, the C index was 0.89 and the sensitivity, specificity, positive predictive value, and negative predictive value were 63.4%, 94.3%, 83.3%, and 84.6% in internal validation.The model was validated in validation set (N=177). And the C index was 0.93, and the sensitivity, specificity, positive predictive value, and negative predictive value were 58.7%, 97.7%, 90.0%, and 87.1% in the validation set respectively in external validation. High score would increase the probability of NBM. Conclusion: The model including clinical manifestations had high potential diagnostic value in the prediction of NBM.