Related Experiment Video
Updated: Jan 18, 2026

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
Published on: July 24, 2013
Clinical features and mortality risk factors in non-HIV elderly patients with cryptococcal meningitis: A
Xiaofeng Xu1, Xiaohong Su1, Weipeng Li2
1Department of Neurology, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China.
Background:
Cryptococcal meningitis (CM) is a life-threatening fungal infection with increasing incidence among non-HIV (human immunodeficiency virus) elderly populations. However, data on CM in non-HIV elderly patients are limited. This study aimed to analyze the clinical features, outcomes, and prognostic factors in non-HIV elderly CM patients using the largest dataset to date.
Methods:
A retrospective cohort study was conducted using data from 667 non-HIV CM patients treated between 2013 and 2022. Patients were categorized into elderly (≥60 years) and non-elderly groups. Clinical features, laboratory findings, and neuroimaging results were analyzed. Least Absolute Shrinkage and Selection Operator regression identified prognostic factors, and multivariate logistic regression was used to construct a nomogram for predicting mortality. The model's discrimination, calibration, and decision curve analysis (DCA) were evaluated.
Results:
Elderly patients accounted for 23.5% of the study population, exhibited distinct clinical characteristics, and had a significantly higher one-year all-cause mortality rate (31.2% [95% confidence interval (CI) 23.61-38.71] vs. 13.8% [95% CI 10.77-16.81], P < 0.001). Four prognostic factors for elderly patients were identified, and a predictive nomogram was developed. The predictive model achieved an area under the curve (AUC) of 0.81 (95% CI 0.71-0.91), and the AUC was 0.79 (95% CI 0.70-0.87) in the internal validation. The model was well-calibrated, and DCA indicated a net benefit.
Conclusion:
Non-HIV elderly CM patients present distinct clinical characteristics and have a higher mortality risk. The predictive model may facilitate the early identification of high-risk patients and guide timely interventions.
Insights
Elderly patients with non-HIV cryptococcal meningitis (CM) face higher mortality risks. A new predictive model aids in early identification of high-risk individuals for timely intervention.
Area of Science:
- Infectious Diseases
- Mycology
- Epidemiology
Background:
- Cryptococcal meningitis (CM) is a serious fungal infection.
- Incidence is rising in non-HIV elderly populations.
- Limited data exists on this specific demographic.
Purpose of the Study:
- Analyze clinical features and outcomes of non-HIV elderly CM patients.
- Identify prognostic factors for mortality in this group.
- Develop a predictive model for risk stratification.
Main Methods:
- Retrospective cohort study of 667 non-HIV CM patients (2013-2022).
- Comparison between elderly (≥60 years) and non-elderly groups.
- Prognostic factor identification using LASSO regression and nomogram construction.
Main Results:
- Elderly patients (23.5%) had significantly higher one-year mortality (31.2% vs. 13.8%).
- Four prognostic factors were identified for elderly patients.
- A predictive nomogram showed good discrimination (AUC 0.81) and calibration.
Conclusions:
- Non-HIV elderly CM patients have unique characteristics and increased mortality risk.
- The developed predictive model can help identify high-risk patients early.
- Timely interventions can be guided by the predictive model.
Related Concept Videos
Factors Affecting the Risk of Infection
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
Pharmacokinetics in Geriatric Patients: Effect of Age on Drug Excretion
Pharmacokinetics in Geriatric Patients: Effect of Age on Drug Distribution
Pharmacodynamics in Geriatric Patients: Effects of Age
Pharmacokinetics in Geriatric Patients: Effect of Age on Drug Metabolism
Pharmacokinetics in Geriatric Patients: Effect of Age on Drug Absorption

