Development a nomogram model for the prediction of bacterial meningitis complicated with ischemic stroke

Abstract

Objective To screen the risk factors of bacterial meningitis complicated with ischemic stroke and initially construct a risk prediction nomogram model. Methods A retrospective research analysis was performed for baseline data, clinical characteristics, laboratory or imaging examinations about 176 patients with bacterial meningitis diagnosed and treated in Xijing Hospital, Air Force Military Medical University of Chinese PLA from June 2008 to June 2018. Univariate and multivariate Logistic regression screened the risk factors for bacterial meningitis complicated with ischemic stroke. A prediction nomogram model was established by R software, using receiver operating characteristic (ROC) curve and calibration curve to evaluate the discrimination and calibration of the model. Results Fifteen of the 176 patients with bacterial meningitis complicated with ischemic stroke, the incidence was about 8.52%. Logistic regression analysis showed that age≥55 years (OR=6.350, 95%CI:1.750-23.046; P=0.005), seizures (OR=5.114,95%CI:1.363-19.193; P=0.016), neurological deficit (OR=10.409, 95%CI:2.781-39.480; P=0.001) and cerebrospinal fluid white blood cell count<1634×10⁶ /L (OR=3.538, 95%CI:1.014-12.345; P=0.048) were risk factors for patients with bacterial meningitis complicated with ischemic stroke. The risk prediction nomogram model was constructed based on the above four indicators, and the probability of bacterial meningitis complicated with ischemic stroke was 66.8%. The area under the ROC curve was 0.859 (95%CI:0.749-0.968, P=0.001), which indicated that the model had excellent performance. The calibration chart showed that the trend of the model curve and the ideal curve was more consistent, which indicated that the model had better prediction performance. Conclusions Prediction of ischemic stroke in patients with bacterial meningitis has an excellent discrimination and calibration based on the currently constructed nomogram model for the risk. This prediction model contributes to the early detection of ischemic stroke in patients with bacterial meningitis, which has clinical significance to make a further study.

doi：10.3969/j.issn.1672⁃6731.2021.05.008