Latent transition analysis for longitudinal studies of post-acute infection syndromes

TL;DR

This paper introduces a new framework using latent transition analysis to study post-acute infection syndromes by identifying disease phenotypes and patient transitions over time.

Contribution

The novel contribution is a generalizable framework for analyzing longitudinal PAIS data using latent transition analysis with covariate integration.

Findings

The framework identifies distinct PCC phenotypes in the ORCHESTRA dataset.

Patient trajectories are influenced by age and sex according to the model results.

The method enhances interpretability of complex clinical data for personalized monitoring.

Abstract

Post-Acute Infectious Syndromes (PAIS) refer to the symptoms persisting months after initial infection. Clinical research studies on this topic often collect rich, multi-modal datasets. Yet, the complexity of the datasets and the lack of a precise clinical case definition pose difficulties in creating comprehensive analyses. Here, we present a generalisable framework for analysing data from longitudinal studies of PAIS using Latent Transition Analysis (LTA). It enables the identification of disease phenotypes and the patient-level analysis of transitions between them, without relying on predefined clinical categorisations. Furthermore, we introduce a method for incorporating covariate information, which enables exploration of how patient characteristics influence disease trajectories. We apply this methodology to the ORCHESTRA dataset, composed of individuals affected by SARS-CoV-2 infection from multiple European centres, for investigation into Post-COVID-19 condition (PCC). 5094 patient assessments were collected at SARS-CoV-2 infection, and at 6, 12, 18, and 24 months of follow-up. Our model identifies distinct PCC phenotypes with patient trajectories impacted by age and sex. Our results highlight how LTA can enhance the interpretability of complex, time-resolved clinical data, support personalized patient monitoring and management, and accelerate therapeutic development for other PAISs, too.

Post-acute infection syndromes often have heterogeneous symptoms that are difficult to interpret. Here, the authors develop a latent trajectory analysis framework designed to categorise complex relationships in longitudinal data into distinct disease phenotypes and analyse transitions between them.

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