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Deciphering prognostic indicators in non-HIV cryptococcal meningitis: Constructing and validating a predictive
Feng Liang1, Runyang Li1, Make Yao2
1Department of Neurology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Abstract:
Cryptococcal meningitis (CM) is a well-recognized fungal infection, with substantial mortality in individuals infected with the human immunodeficiency virus (HIV). However, the incidence, risk factors, and outcomes in non-HIV adults remain poorly understood. This study aims to investigate the characteristics and prognostic indicators of CM in non-HIV adult patients, integrating a novel predictive model to guide clinical decision-making. A retrospective cohort of 64 non-HIV adult CM patients, including 51 patients from previous studies and 13 from the First Hospital of Shanxi Medical University, was analyzed. We assessed demographic features, underlying diseases, intracranial pressure, cerebrospinal fluid characteristics, and brain imaging. Using the least absolute shrinkage and selection operator (LASSO) method, and multivariate logistic regression, we identified significant variables and constructed a Nomogram prediction model. The model's calibration, discrimination, and clinical value were evaluated using the Bootstrap method, calibration curve, C index, goodness-of-fit test, receiver operating characteristic (ROC) analysis, and decision curve analysis. Age, brain imaging showing parenchymal involvement, meningeal and ventricular involvement, and previous use of immunosuppressive agents were identified as significant variables. The Nomogram prediction model displayed satisfactory performance with an akaike information criterion (AIC) value of 72.326, C index of 0.723 (0.592-0.854), and area under the curve (AUC) of 0.723, goodness-of-fit test P = 0.995. This study summarizes the clinical and imaging features of adult non-HIV CM and introduces a tailored Nomogram prediction model to aid in patient management. The identification of predictive factors and the development of the nomogram enhance our understanding and capacity to treat this patient population. The insights derived have potential clinical implications, contributing to personalized care and improved patient outcomes.
Insights
Cryptococcal meningitis (CM) in non-HIV adults is poorly understood. This study developed a Nomogram prediction model using clinical and imaging data to identify risk factors and improve patient management for this fungal infection.
Area of Science:
- Infectious Diseases
- Mycology
- Neurology
Background:
- Cryptococcal meningitis (CM) poses a significant mortality risk, particularly in individuals with human immunodeficiency virus (HIV).
- The epidemiology, risk factors, and outcomes of CM in non-HIV adults are not well-established.
- There is a need for improved diagnostic and prognostic tools for non-HIV CM patients.
Purpose of the Study:
- To investigate the clinical characteristics and prognostic indicators of CM in adult patients without HIV.
- To develop and validate a predictive model (Nomogram) for guiding clinical decision-making in non-HIV CM.
- To enhance the understanding and management of CM in immunocompetent adults.
Main Methods:
- Retrospective cohort analysis of 64 non-HIV adult CM patients.
- Assessment of demographic, clinical, cerebrospinal fluid, and neuroimaging data.
- Development of a Nomogram prediction model using LASSO and multivariate logistic regression, validated with Bootstrap methods, ROC analysis, and decision curve analysis.
Main Results:
- Significant predictors identified include age, parenchymal, meningeal, and ventricular involvement on brain imaging, and prior immunosuppressive agent use.
- The Nomogram demonstrated satisfactory predictive performance with a C-index of 0.723 and AUC of 0.723.
- Goodness-of-fit testing indicated excellent model calibration (P = 0.995).
Conclusions:
- This study provides a comprehensive summary of clinical and imaging features of non-HIV adult CM.
- A validated Nomogram prediction model is introduced to assist in the management of non-HIV CM patients.
- The findings offer potential clinical implications for personalized care and improved outcomes in this population.
