Journal of Diagnostics Concepts & Practice
2025, 24 (
):
441-448.
Objective To explore the risk factors for poor prognosis in patients with hospital-acquired bacterial me-ningitis (HABM) and to establish a nomogram model to predict its occurrence. Methods A total of 110 patients with HABM admitted to Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from January 1, 2013 to December 31, 2020 were consecutively enrolled. Based on survival status at discharge, they were divided into a death group (n=22) and a survival group (n=88). Subsequently, 110 patients were randomly divided into a training cohort (n=77) and a validation cohort (n=33). The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to identify risk factors for poor prognosis in patients with HABM. A nomogram model was constructed based on these risk factors. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the model discrimination, and the calibration curve was used to evaluate the internal consistency of the model. Results Based on the LASSO regression, seven factors were identified: gram-positive staining of microorga-nisms in cerebro-spinal fluid (CSF) culture, elevated neutrophil count on routine blood tests, elevated procalcitonin, elevated CSF protein, decreased prothrombin time, positive blood culture, and history of lumbar drainage. A nomogram prediction model for poor prognosis in HABM patients was established. The areas under the ROC curves for the training cohort and the validation cohort were 0.931 and 0.862, respectively. The calibration plots demonstrated that the calibration curves showed good agreement with the ideal curves, indicating an excellent goodness of fit. Conclusions The risk factor-based nomogram model established in this study demonstrates good predictability, consistency, and clinical applicability for predicting mortality in hospitalized HABM patients, supporting clinicians in the preliminary assessment of the risk of poor prognosis.
