Baseline characteristics of study participants.

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 < 0.001), respiratory rate (aHR: 1.041, 95%CI: 1.007-1.076, P = 0.016), white blood cell count (aHR: 1.089, 95%CI: 1.049-1.132, P < 0.001), platelet count (aHR: 0.995, 95%CI: 0.992-0.997, P < 0.001), albumin level (aHR: 0.911, 95%CI: 0.872-0.952, P < 0.001), lactate dehydrogenase (aHR: 1.000, 95%CI: 1.000-1.000, P < 0.001), and blood urea nitrogen (aHR: 1.087, 95%CI: 1.068-1.106, P < 0.001). The above indicators were stratified according to predefined classifications and used to established a nomogram. The nomogram demonstrated strong discriminatory performance for 7-, 14-, and 28-day outcomes (AUC 0.905/0.863/0.838 in development; 0.851/0.832/0.807 in independent testing; C-index 0.813-0.841). Calibration curve analysis demonstrated that the nomogram exhibited excellent predictive accuracy and decision curve analysis indicated substantial clinical benefit. The model could effectively differentiate between high-risk and low-risk populations.
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.