Prognosis and treatment effects of HIV-associated talaromycosis in a real-world patient cohort.

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2021-04

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Abstract

Talaromycosis is a leading cause of AIDS-associated opportunistic infections and death in Southeast Asia. We have recently shown in the Itraconazole versus Amphotericin for Talaromycosis (IVAP) trial that induction therapy with amphotericin B reduced mortality over 24 weeks, but not during the first 2 weeks. Antifungal treatment effects in real-world settings have not been rigorously evaluated. Using data obtained from patient records at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam from 2004 to 2009, we first developed a prognostic model using Bayesian logistic regression to identify predictors of death. Second, we developed a causal model using propensity score matching to assess the treatment effects of amphotericin B and itraconazole. Our prognostic model identified intravenous drug use (odds ratio [OR] = 2.01), higher respiratory rate (OR = 1.12), higher absolute lymphocyte count (OR = 1.62), a concurrent respiratory infection (OR = 1.67) or central nervous system infection (OR = 2.66) as independent predictors of death. Fever (OR = 0.56) was a protective factor. Our prognostic model exhibits good in-sample performance and out-of-sample validation, with a discrimination power of 0.85 and 0.91, respectively. Our causal model showed no significant difference in treatment outcomes between amphotericin B and itraconazole over the first 2 weeks (95% credible interval: 0.62, 2.50). Our prognostic model provides a simple tool based on routinely collected clinical data to predict individual patient outcome. Our causal model shows similar results to the IVAP trial at 2 weeks, demonstrating an agreement between real-world data and clinical trial data.

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10.1093/mmy/myab005

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Klus, Jonathan, Vo Trieu Ly, Cliburn Chan and Thuy Le (2021). Prognosis and treatment effects of HIV-associated talaromycosis in a real-world patient cohort. Medical mycology, 59(4). pp. 392–399. 10.1093/mmy/myab005 Retrieved from https://hdl.handle.net/10161/23599.

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Scholars@Duke

Chan

Chi Wei Cliburn Chan

Professor of Biostatistics & Bioinformatics

Computational immunology (stochastic and spatial models and simulations, T cell signaling, immune regulation)
Statistical methodology for immunological laboratory techniques (flow cytometry, CFSE analysis, receptor-ligand binding and signaling kinetics)
Informatics of the immune system (reference and application ontologies, meta-programming, text mining and machine learning)

Le

Thuy Le

Associate Professor of Medicine

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