Prediction-based classification for longitudinal biomarkers

TL;DR

This paper introduces a prediction-based classification method for monitoring CD4 count changes in HIV patients on ART, aiming to optimize resource use in settings with limited testing capacity.

Contribution

It extends existing ROC analysis methods to handle continuous outcomes for predicting clinically meaningful CD4 count changes.

Findings

Achieved over 98% positive predictive value in test sample

Validated approach on independent cohort from Philadelphia

Potential to prioritize CD4 testing in resource-limited settings

Abstract

Assessment of circulating CD4 count change over time in HIV-infected subjects on antiretroviral therapy (ART) is a central component of disease monitoring. The increasing number of HIV-infected subjects starting therapy and the limited capacity to support CD4 count testing within resource-limited settings have fueled interest in identifying correlates of CD4 count change such as total lymphocyte count, among others. The application of modeling techniques will be essential to this endeavor due to the typically nonlinear CD4 trajectory over time and the multiple input variables necessary for capturing CD4 variability. We propose a prediction-based classification approach that involves first stage modeling and subsequent classification based on clinically meaningful thresholds. This approach draws on existing analytical methods described in the receiver operating characteristic curve literature while presenting an extension for handling a continuous outcome. Application of this method to an independent test sample results in greater than 98% positive predictive value for CD4 count change. The prediction algorithm is derived based on a cohort of $n=270$ HIV-1 infected individuals from the Royal Free Hospital, London who were followed for up to three years from initiation of ART. A test sample comprised of $n=72$ individuals from Philadelphia and followed for a similar length of time is used for validation. Results suggest that this approach may be a useful tool for prioritizing limited laboratory resources for CD4 testing after subjects start antiretroviral therapy.