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Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

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Published on: December 1, 2011

Machine Learning-based Prediction of Active Tuberculosis in People With HIV Using Clinical Data.

Lena Bartl¹, Marius Zeeb^1,2, Marisa Kälin¹

¹Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.

Clinical Infectious Diseases : an Official Publication of the Infectious Diseases Society of America

|March 25, 2025

Summary

This summary is machine-generated.

Machine learning models can predict active tuberculosis in people with HIV, outperforming current tests. This improves early identification for timely preventive treatment, crucial for managing coinfections.

Keywords:

HIV clinical risk score machine learning prediction tuberculosis

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Area of Science:

Medical Informatics
Infectious Disease Epidemiology
Machine Learning in Healthcare

Background:

Coinfection with Mycobacterium tuberculosis (MTB) and human immunodeficiency virus (HIV) presents a significant global health challenge.
Individuals with MTB infection have an increased risk of developing active tuberculosis (TB), a progression that can be prevented with timely therapy.
Existing diagnostic methods often fail to identify individuals at high risk for subsequent active TB development.

Purpose of the Study:

To develop and validate machine learning models for predicting incident active TB in people with HIV (PWH).
To assess the performance of these models against standard diagnostic tests for TB risk stratification.

Main Methods:

Random forest models were developed using routinely collected clinical data from the Swiss HIV Cohort Study (SHCS) for training.
The training dataset included 55 PWH who developed active TB and 1432 matched PWH without TB.
External validation was performed using data from the Austrian HIV Cohort Study (AHIVCOS), comprising 43 PWH with incident active TB and 1005 PWH without TB.

Main Results:

The model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.83 in the SHCS.
After adjustments and re-fitting, AUC values of 0.72 (SHCS) and 0.67 (AHIVCOS) were obtained.
The machine learning model demonstrated superior performance compared to standard care (tuberculin skin test and interferon-gamma release assay), with a lower number needed to diagnose (1.96 vs. 4).

Conclusions:

Machine learning models show significant potential for enhancing the care of PWH by improving the identification of individuals who could benefit from preventive TB treatment.
These models require no additional data collection and incur minimal costs, offering a cost-effective solution.
The findings support the integration of predictive modeling into clinical practice for better management of TB/HIV coinfection.