Deciphering prognostic indicators in non-HIV cryptococcal meningitis: Constructing and validating a predictive Nomogram model.

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.

Cryptococcal meningitis (CM) is a serious fungal infection that can affect the brain and spinal cord. It is well known to occur in people with HIV, but it can also affect those without HIV, although this is less common. This study focuses on understanding how CM affects non-HIV patients, which is not as well understood as its effects on HIV patients. We analyzed data from 64 non-HIV patients with CM to identify factors that might influence their recovery or lead to poor outcomes, such as severe disability or death. Using advanced statistical methods, we developed a predictive tool called a nomogram. This tool helps doctors estimate the likelihood of a poor outcome in non-HIV Cryptococcal meningitis (CM) patients based on specific factors like age, brain imaging results, and the use of certain medications. Our findings suggest that older patients and those with specific brain imaging abnormalities may be at higher risk. On the other hand, patients who have previously used immunosuppressive drugs might have a better prognosis, which is a surprising and new insight. This research is important because it provides new knowledge that could help doctors better manage CM in non-HIV patients, leading to more personalized and effective treatments. The predictive tool we developed could be used in hospitals to improve decision-making and patient care, ultimately improving outcomes for those suffering from this serious condition.