Predictive models for Talaromyces marneffei infection in HIV-infected patients using routinely collected data
Objectives Late diagnosis of Talaromyces marneffei (T. marneffei) in patients with HIV/AIDS infection is strongly associated with greater mortality. To date, effective predictive model for T. marneffei infection in clinical practice have not been established. We aimed to identify a non-culture-based method for rapid detection of T. marneffei infection in HIV/AIDS patients. Methods The prediction models were initially constructed using patients in a retrospective cohort study. We obtained
... We obtained demographics, clinical and laboratory data for each individual. Univariate comparisons, logistic regression, Random Forest (RF) analysis and receiver-operating characteristic curves (ROC) were used to identify and evaluate the predictive factors of T. marneffei infection status. Results HIV-infected patients with a baseline characterized by weight loss, typical skin lesions, peripheral or abdominal lymphadenopathy (POAL), hepatomegaly, splenomegaly, decrease lymphocyte count, abnormal aspartate aminotransferase (AST) level , higher AST to alanine aminotransferase (ALT) ratio index (AARI) level (>1) and lower (<50 cells/mL) CD4+ T-cell counts had an increased risk of T. marneffei infection. Skin lesions, POAL, AARI, AST level and CD4+ T-cell count resulted in good classifiers of T. marneffei infection by RF analysis. RF model had a relative high power [area under the ROC curve (AUC): 0.859] to predict T. marneffei infection in the present study. A new indicator combine AST level and AARI could increase the classification power of the model (AUC: 0.877).