A nomogram to predict cryptococcal meningitis in patients with pulmonary cryptococcosis
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed a nomogram to predict cryptococcal meningitis risk in pulmonary cryptococcosis patients. Early prediction aids timely intervention and improves outcomes for this serious complication.
Area Of Science
- Infectious Diseases
- Pulmonology
- Neurology
Background
- Pulmonary cryptococcosis can lead to cryptococcal meningitis, a severe complication with non-specific symptoms, delayed diagnosis, and high mortality.
- Early identification of patients at risk for cryptococcal meningitis is crucial for timely clinical intervention and improved survival rates.
Purpose Of The Study
- To develop and validate a nomogram-based scoring system for predicting the risk of cryptococcal meningitis in patients diagnosed with pulmonary cryptococcosis.
Main Methods
- Retrospective analysis of clinical data from 525 pulmonary cryptococcosis patients.
- Identification of risk factors using univariate, LASSO, and multivariate logistic regression analyses.
- Construction and validation of a nomogram using ROC curve, DCA, and clinical impact curve.
Main Results
- Fourteen risk factors were identified, including clinical symptoms (fever, headache, nausea, psychiatric symptoms, tuberculosis, hematologic malignancy) and laboratory indicators (neutrophils, lymphocytes, glutamic oxaloacetic transaminase, T cells, helper T cells, killer T cells, NK cells, B cells).
- The developed nomogram demonstrated high predictive accuracy with an AUC of 0.978 (95% CI: 96.2%-98.9%).
Conclusions
- The nomogram scoring system provides an accurate tool for predicting cryptococcal meningitis risk in pulmonary cryptococcosis patients.
- This predictive model can serve as a valuable reference for clinical decision-making, facilitating earlier diagnosis and treatment of cryptococcal meningitis.
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