Predictive models for Talaromyces marneffei infection in HIV-infected patients using routinely collected data

Abstract

ObjectivesLate 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.MethodsThe prediction models were initially constructed using patients in a retrospective cohort study. 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.ResultsHIV-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).ConclusionOur data suggest that accurate assessments for T. marneffei infection can be obtained using routinely collected data of patients with HIV. The prediction model could used to identify HIV patients who currently have early stage of T. marneffei infection, which would be benefit to both patients and clinicians.

An Overview of Diagnostic and Management Strategies for Talaromycosis, an Underrated Disease

“…Nevertheless, researchers have noted that in Southeast Asia, HIV-negative patients who possess the HLA-DR*15:02/16:02 and HLA-DQ*05:01/05:02 genes are predisposed to T. marneffei infection [ 29 ]. In HIV-infected individuals, a prediction model has been developed to produce accurate clinical outcome assessments based on the associations between T. marneffei infection and the clinical features of HIV infection [ 70 ]. Thus, candidate factors such as skin lesions, AST level, ALT ratio index, peripheral or abdominal lymphadenopathy, and CD4+ T-cell counts were all considered in different prediction models.…”

“…Thus, candidate factors such as skin lesions, AST level, ALT ratio index, peripheral or abdominal lymphadenopathy, and CD4+ T-cell counts were all considered in different prediction models. It was observed that the model combining AST levels and the ALT ratio index displayed high power, specifically for the early diagnosis of T. marneffei infection [ 70 ]. Although this work has only been published in a preprint research platform, the approach used to conduct the investigation is impressive, and may well be improved upon using a higher volume of, and more frequently collected data obtained from clinical practice.…”