The clinic-based predictive modeling for prognosis of patients with cryptococcal meningitis

Abstract

Background: Cryptococcal meningitis (CM) is the most common fungal infection of the central nervous system that can cause significant morbidity and mortality. Although several prognostic factors have been identified, their clinical efficacy and use in combination to predict outcomes in immunocompetent patients with CM are not clear. Therefore, we aimed to determine the utility of those prognostic factors alone or in combination in predicting outcomes of immunocompetent patients with CM. Methods: The demographic and clinical data of patients with CM were collected and analyzed. The clinical outcome was graded by the Glasgow outcome scale (GOS) at discharge, and patients were divided into good (score of 5) and unfavorable (score of 1–4) outcome groups. Prognostic model was created and receiver-operating characteristic curve analyses were conducted. Results: A total of 156 patients were included in our study. Patients with higher age at onset (p = 0.021), ventriculoperitoneal shunt placement (p = 0.010), Glasgow Coma Scale (GCS) score of less than 15(p< 0.001), lower CSF glucose concentration (p = 0.037) and immunocompromised condition (p = 0.002) tended to have worse outcomes. Logistic regression analysis was used to create a combined score which had a higher AUC (0.815) than those factors used alone for predicting outcome. Conclusions: Our study shows that a prediction model based on clinical characteristics had satisfactory accuracy in prognostic prediction. Early recognition of CM patients at risk of poor prognosis using this model would be helpful in providing timely management and therapy to improve outcomes and to identify individuals who warrant early follow-up and intervention.