Abstract
Background
HIV-associated talaromycosis causes substantial mortality despite available therapies. Early identification of high-risk patients remains challenging, particularly in resource-limited settings. We aimed to develop and validate a dynamic prognostic model for rapid risk stratification.
Methods
This retrospective cohort study analyzed 1,892 HIV-talaromycosis patients admitted to Guangzhou Eighth People’s Hospital (2011–2023). Poor outcome (in-hospital death or deterioration-related discharge) was the primary endpoint. A nomogram was developed using Cox regression on admission variables in a training set (2011–2020, N = 1,435), with internal validation set (2011–2020, N = 431) and independent testing set (2021–2023, N = 457). Performance was assessed via time-dependent AUC, C-index, calibration, and decision curve analysis.
Results
Poor outcomes occurred in 14.1% of cases (266/1,892), with 86.5% of these events happening within 28 days. Winter admissions exhibited the lowest case volume but the highest poor outcome rate. Multivariable analysis revealed eight independent readily available predictors: absence of lymphadenopathy (aHR: 0.581, 95%CI: 0.396-0.852,
P
= 0.005) and hepatosplenomegaly (aHR: 0.347, 95%CI: 0.232-0.519,
P
P
= 0.016), white blood cell count (aHR: 1.089, 95%CI: 1.049-1.132,
P
P
P
P
P
Conclusion
This study provides a dynamically validated prognostic tool for HIV-associated talaromycosis, enabling risk stratification using readily available clinical data. Its integration into electronic health systems could off an opportunity to optimize resource allocation and improve outcomes in endemic regions.