Multicentre derivation and validation of a prognostic scoring system for mortality assessment in HIV-infected patients with talaromycosis

Abstract

Background: Although the widespread use of modern antiretroviral therapy (ART) has reduced the incidence of talaromycosis in people living with HIV, mortality remains as high as 20% in this population, even after appropriate antifungal treatment.

Objectives: The objective of our study was to develop a risk assessment system for HIV-infected patients with comorbid talaromycosis, in order to provide these patients with appropriate, effective and potentially life-saving interventions at an early stage of their illness.

Patients/methods: This was a multicentre, retrospective cohort study conducted in China. We built a predictive model based on data from 11 hospitals, and a validated model using the data of 1 hospital located in an endemic area.

Results: Forward stepwise multivariate statistical calculations indicated that age, aspartate aminotransferase/alanine transaminase ratio and albumin levels, and BUN levels were valid, independent predictors of the risk of death in HIV-infected patients with talaromycosis. Our developed and validated risk scoring system is effective for the identification of HIV-infected patients with talaromycosis at high risk of death at hospital admission (p < .001; AUC = 0.860). In our study, our risk prediction model provided functional and robust discrimination in the validation cohort (p < .001; AUC = 0.793).

Conclusion: The prognostic scoring system for mortality assessment developed in the present study is an easy-to-use clinical tool designed to accurately assist clinicians in identifying high-risk patients with talaromycosis.

Keywords: AIDS; HIV; Talaromycosis; mortality; risk scoring system.

Figure 1

Flow chart of the study population

Figure 2

Mortality risk prediction rules for 384 HIV‐associated talaromycosis cases in the derivation cohort. A, Interactive dot diagram. The total score calculated by the prediction model labels the vertical axis of the above plot, and the clinical outcome of a patient during hospitalization marks the horizontal axis. The score of each patient were displayed as dots on two vertical axes. The horizontal line indicates the cut‐off point with the best separation. When using a score of 8.5 as the cut‐off value to predict the mortality risk of patients with talaromycosis, the sensitivity of our scoring system was 84.0% (95% CI, 0.639‐0.955), its specificity was 71.6% (95% CI, 0.666‐0.762). B, Receiver operator characteristic (ROC) curves. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. The area under the ROC curve is a measure of how well a parameter can distinguish between two groups (survived/died). AUC, area under the ROC curve; Sens, sensitivity; Spec, specificity

Figure 3

Mortality risk prediction rules for 233 HIV‐associated talaromycosis cases in validation cohort. A, Interactive dot diagram. The total score calculated by the prediction model labels the vertical axis of the above plot, and the clinical outcome of a patient during hospitalization marks the horizontal axis. The score of each patient were displayed as dots on two vertical axes. The horizontal line indicates the cut‐off point with the best separation. When using a score of 8.5 as the cut‐off value to predict the mortality risk of patients with talaromycosis, the sensitivity of the risk scoring system was calculated to be 78.6% (95% CI, 0.492‐0.953), its specificity was 72.6% (95% CI, 0.662‐0.784). B, Receiver operator characteristic (ROC) curves. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. The area under the ROC curve is a measure of how well a parameter can distinguish between two groups (survived/died). AUC, area under the ROC curve; Sens, sensitivity; Spec, specificity